VALVE AND METHOD TO CONTROL THE FLOW OF A FLUID, AND, PRODUCTION TUBE

专利摘要:
computer implemented method, computer system, and non-temporary computer-readable storage media, a system and method for providing better correction information to navigation receivers (120) which include receiving from a plurality of stations are described (140-1, 340-2, i40-m) at known locations, a plurality of satellite navigation measurements of signals from a plurality of global navigation satellites (110-1, 110-2, 110-m ). a state of the plurality of global navigation satellites (110-1, 110-2, 110-n) is computed based on the received satellite navigation measurements. references, each corresponding to a pair of reference stations (140-1, 140-2, 140-m), are identified. for each identified referential, floating and integral values are computed for a doubly differentiated integral ambiguity. doubly differentiated integral ambiguities that satisfy a set of predefined conditions are identified and the computed state of the plurality of global navigation satellites (1104, 110-2, 110-n) is adjusted according to a full value constraint applied to each integral ambiguity doubly differentiated that satisfies the set of predefined conditions. correction information is computed from the adjusted computed state of the plurality of global navigation satellites (110-1, 110-2, t 10-n).
公开号:BR112013017730A2
申请号:R112013017730-6
申请日:2011-12-22
公开日:2020-02-18
发明作者:L. Dai Liwen；R. Hatch Ronald；Zhang Yujie；Wang Min
申请人:Navcom Technology, Inc.；
IPC主号:

专利说明:

“COMPUTER IMPLEMENTED METHOD, COMPUTER SYSTEM, AND NON-TEMPORARY COMPUTER LEGIBLE STORAGE MEDIA”
RELATED ORDER
This order claims priority to Provisional Order US 61 / 432,172, filed on January 12, 2011, which is incorporated herein by reference in its entirety.
FIELD OF THE INVENTION
The disclosed modalities refer, in general, to systems and methods to generate correction information, for respective satellites in a satellite-based navigation system, and, more specifically, to the generation of better correction information by solving integral ambiguities in Range measurements made by reference stations using integral double difference ambiguity constraints.
FUNDAMENTALS OF IN VENTION
An RnZ / ncm filter can be used to resolve static and dynamic parameters in a dynamic system with noisy measurements. A system like this is a Global Navigation Satellite System (GNSS), in which satellite navigation measurements by navigation receivers (for example, receivers on or near the Earth's surface) are affected by various sources of noise (for example, example, multi-path effects, ionospheric effects, iropospheric effects, etc.).
An application of RnZmm filters is a Kalman filter (for example, in a “wide area differential GPS system (WADGPS)) that tracks the orbits of thirty global navigation satellites using a set of reference stations (for example, 50 to 80 reference stations) located around the world, the resulting orbital solutions are compared with the locations and trajectories of the “almanac” data satellites (here, hereinafter referred to as almanac data) and combined with the ephemeris information disseminated by satellites or other systems, which provide navigation receivers with adjustments to the almanac data. The difference between the orbital solutions produced by the filter and the adjusted almanac data is used to generate correction information, sometimes called auxiliary data or differential data, which is broadcast to subscribers' navigation receivers (for example, navigation receivers whose holders have paid a subscription fee). Stórrime, a system and service provided by ADvCo » T'Cc / moZogv, / nc ,, is an example of a system that tracks the orbits of global navigation satellites and transmits correction information to the receivers of. subscriber navigation. Differential data, when used by compatible navigation receivers, enables these receivers to more precisely determine their positions, in some implementations, with an accuracy better than one meter.
It would be highly desirable to provide a system and method that determine better correction information to enable navigation receivers to achieve higher levels of accuracy.
SUMMARY OF THE INVENTION
In order to provide better correction information, some 20 modalities provide a system, a non-temporary, computer-readable storage medium that includes instructions, and a computer-implemented method for receiving from a plurality of reference stations in known locations, a plurality of satellite navigation measurements of signals from a plurality of global navigation satellites. The method includes computing a state of the plurality of global navigation satellites based on the received satellite navigation measurements, identifying a plurality of references, each reference corresponding to a pair of reference stations, and for each identified reference, computing floating values and Integrals for a doubly differentiated integral ambiguity corresponding to the identified framework. ( ) method additionally includes identifying according to the floating and integral values computed for the differentiated integral two-dimensional ambiguities corresponding to the plurality of identified references, a set of one or more doubly differentiated integral ambiguities that satisfy a set of predefined conditions. In addition, the method includes adjusting the computed state of the plurality of global navigation satellites according to an Integral value constraint applied to each integral ambiguity doubly differentiated in the identified set of one or more differentially integrated integral ambiguities that satisfy the set of conditions to produce an adjusted computed state of the plurality of global navigation satellites.
In some embodiments, the method includes computing correction information according to the adjusted computed state of the plurality of global navigation satellites and transmitting the correction information to a plurality of navigation receivers. Typically, correction information includes correction values for each of the global navigation satellites in the plurality of global navigation satellites and, more generally, includes correction values for two or more of the global navigation satellites in the plurality of navigation satellites global.
In some modalities, the set of predefined conditions for a respective doubly differentiated integral ambiguity includes a requirement that a fractional difference between the integral and fluctuating values of the respective doubly differentiated integral ambiguity does not exceed a first predefined limit value »Furthermore, in some modalities, the method includes computing a variance and one. standard deviation of the respective differentiated integral ambiguity, and the set of predefined conditions for the respective doubly differentiated integral ambiguity includes a requirement that the standard deviation of the respective doubly differentiated integral ambiguity does not exceed a second predefined limit value. Additionally, in some modalities, the set of predefined conditions for the respective doubly differentiated integral ambiguity Includes a requirement that a predefined W ratio has a value that exceeds a third predefined limit value.
In some modalities, the identified references include only mathematically independent ones.
In some modalities, the identification of the set of one or more doubly differentiated integral ambiguities that satisfy the set of predefined conditions includes the filtering of doubly differentiated integral ambiguities corresponding to the identified referentials to prevent any doubly differentiated integral ambiguities that fail to satisfy filtering criteria predefined numbers are included in the identified set.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 is a block diagram that illustrates the global satellite navigation system according to some modalities.
Figure 2 is a block diagram showing a satellite navigation receiver according to some modalities.
Figure 3 is a block diagram that illustrates a computer system according to some modalities.
 Figure 4 is a flow chart. of a method to compute an estimated state of a plurality of global navigation satellites and to adjust the estimated state computed by applying restrictions on one or more doubly differentiated integral ambiguities that satisfy predefined criteria according to some modalities.
Figures 5A-5B represent a flow chart of a method for adjusting the estimated computed state of a plurality of global navigation satellites according to some modalities.
Figures 6Λ-6Β represent a flowchart of a method for adjusting the estimated computed state of a plurality of global navigation satellites according to some modalities.
Figure 7 is a flowchart of a method for adjusting the status of a satellite navigation receiver (for example, a mobile satellite navigation receiver other than the reference stations) based on correction signals sent by a computer system ( for example, the system in Figure 3) according to some modalities.
Equal reference numbers are retained in corresponding parts throughout the drawings.
DESCRIPTION OF THE MODALITIES
Reference will now be made, with details on the modalities, examples of which are illustrated in the attached drawings. In the following detailed description, numerous specific details are presented. However, it will be apparent to those skilled in the art that the present invention, as defined by the claims, can be practiced without many of these specific details. In other cases, well-known methods, procedures, components, circuits and networks have not been described in detail so as not to unnecessarily obscure aspects of the modalities.
It will also be understood that, although the terms first, second, etc. can be used here to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from the other. For example, a first contact can be called a second contact and, similarly, a second contact can be called a first contact. The first contact and the second contact are both contacts, but they are not the same contact.
The terminology used here in the description is for the purpose of describing particular modalities only and is not intended to be limiting of the invention. As used in the description of the invention and in the appended claims, it is intended that the singular forms ⁴ * an ' “an”, “o” and a ”also include plural forms, unless the context clearly indicates otherwise . It will also be understood that the term and / or ”, as used herein, refers to any and all possible combinations of one or more of the associated listed items, and covers them. It will be further understood that the terms comprise ”and / or comprising”, when used in this specification, specify the presence of declared resources, integers, steps, operations, elements and / or components, but do not prevent the presence or addition of one or more other resources, integers, steps, operations, elements, components and / or groups thereof.
As used herein, the term if "can be interpreted to mean when" or by "or in response to a determination" or in accordance with a determination "or in response to detection", in which a condition stated precedent is true, depending on the context . Similarly, the phrases if it is determined [that a condition declared precedent is true] "or if [a condition declared precedent is true]" or when [a condition declared precedent is true] "can be interpreted to mean" by determination "or in response to determination ”or according to a determination” or upon detection ”or“ in response to detection ”, where the preceding condition is true, depending on the context.
Figure 1 is a block diagram illustrating the global satellite navigation system 100 according to some modalities. The global satellite navigation system 100 includes global navigation satellites 110-1 through I10-N. Each of the global navigation satellites 110-1 through 110-N transmits at least two carrier signals. In the case of the Global Positioning System (GPS), the carrier signals include the LI and L2 signals with frequencies of 1.5754 GHz and 1.2276 GHz, and wavelengths of 0.1903 m and 0.2442 m, respectively. The next generation GPS will offer a third carrier signal, L5, which will have a frequency of 1.1765 GHz and a wavelength of 0.2548 'm. Note that, although the modalities described here are described in relation to GPS, other Global Navigation Satellite (GNSS) systems, such as GZGN / íÓN and GaZ / Zeo, can be used.
In some embodiments, carrier signals are received by a satellite navigation receiver 120. The satellite navigation receiver can be used by a user 121 for navigation or to determine a user's current position 121. In order to perform navigation operations and / or position determination, satellite navigation receiver 120 receives signals from a subset of global navigation satellites 110-1 through 110-N (i.e., the subset includes global navigation satellites in view of the receiver satellite navigation system 120). Then, the satellite navigation receiver 120 makes satellite navigation measurements based on the signals and calculates a state of the satellite navigation receiver 120 based on the satellite navigation measurements. In some embodiments, the status of the satellite navigation receiver includes a position of the satellite navigation receiver (for example, X, Y and Z, or components of the position's latitude, longitude and zenith), a speed of the navigation receiver by satellite and a time. The '120 satellite navigation receiver is described in more detail below in relation to Figure 2.
In some embodiments, carrier signals are received by reference stations 140-1 through 140-M at known locations (for example, inspected locations). Reference stations include a GNSS receiver that receives signals from global navigation satellites 110-1 through 110-N. At any time, the GNSS receiver receives signals only a. from global navigation satellites 110 which are in view of the receiver's antenna. Typically, reference stations 140-1 through 140-M. are used to perform differential GPS operations and / or to track the orbits of the 110-1 to 110-N global navigation satellites. In order to perform these operations, each of the reference stations 140-1 to 140-M receives signals from a subset of the global navigation satellites 110-1 to 110-N (that is, the subset of global navigation satellites 110-1 to 110-N which are in view of each of the reference stations 140-1 to 140-M) and makes satellite navigation measurements based on the signals. In some embodiments, reference stations 140-1 to 140-.M 'transmit the satellite navigation measurement to a computer system 130 via network 150. Reference stations 140-1 to 140-M are described with more details to follow in relation to Figure 2.
In some embodiments, computer system 130 processes satellite navigation measurements received from reference stations 140-1 through 140-M to determine the status of global navigation satellites 110-1 through 110-N. In some embodiments, the status of the global navigation satellites includes a position for each of the global navigation satellites 110-1 through 110-N (for example, X, Y and Z, or components of position latitude, longitude and zenith) , a speed for each of the global navigation satellites 110-1 through 110-N and a time. Then, computer system 130 generates correction signals 132 (sometimes called auxiliary signals) that correct orbital deviations from global navigation satellites até-1 through 110-N. Note that errors in the predicted orbits and clocks of global navigation satellites 11.0-1 through 110-N are referred to as orbital deviations in this specification. Computer system 130 sends correction signals 132 to communication satellites 160-1 to 160-P, which in turn transmit correction signals 132 to satellite navigation receiver 120 and / or reference stations 140-1 to 140-M. Alternatively, computer system 130 sends correction signals 132 to satellite navigation receiver 120 and / or to reference stations 140-1 through 140-M over a network (e.g., network 150). Computer system 130 is described in more detail below in relation to Figure 3,
The network 150 can include, in general, any type of wired or wireless communication channel capable of coupling computing nodes together. This includes, but is not limited to, a local area network, a wide area network or a combination of networks. In some embodiments, network 150 includes the Internet.
Note that there are two types of GPS measurements (ie satellite navigation measurements 10) that are usually taken (for example, by the satellite navigation receiver 120 and / or by the reference stations 140 * 1 to 140-M ): pseudo-range measurements and phase carrier measurements. The operations used to determine the status of the satellite navigation receiver 12 (3 and the operations used to determine the status of the satellites of global navigation 1.10-1 through 11.0-N based on these satellite navigation measurements are well known in the technology and, therefore, a detailed explanation of these operations is not provided in this specification,
Figure 2 is a block diagram illustrating the satellite navigation receiver 120 according to some modalities. This block diagram 20 also illustrates reference stations 140. Typically, the satellite navigation receiver 120 includes one or more processing units (CPUs) 202, one or more network interfaces or other communication interfaces 204, memory 210 and one or more communication buses 209 to interconnect these components. The 25 communication buses 209 can include a circuit system (sometimes called a chip set) that interconnects and controls communications between system components. Optionally, the satellite navigation receiver 120 may include a user interface 205 comprising a display device 206 and one or more input devices 208 (for example, one or more of a keyboard, a touch screen, a numeric keypad, etc.). The satellite navigation receiver 120 also includes one or more GNSS antennas configured to receive signals transmitted by global navigation satellites 110-1 to 1 ΙΟΝ. Memory 210 includes high speed random access memory, such as DRAM, SRAM, RAM DDR or other memory devices, random access in solid state; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, .Z / nsA memory devices, or other non-volatile solid state storage devices. Memory 210 may optionally include one or more storage devices remotely located in relation to CPU (s) 202. Memory 21.0 or, alternatively, the non-volatile memory device (s) in memory 210, comprise non-temporary computer-readable storage media. In some embodiments, memory 210 or computer-readable storage media of memory 210 stores the following programs, modules and data structures, or a subset of these:
• an operating system 212 that includes procedures for handling various basic system services and for performing tasks that depend on Imáíw;
• a communication module 214 that is used to connect the satellite navigation receiver 120 to other computer systems via one or more communication interfaces 204 (wired or wireless) and one. or more communication networks, such as the Internet, other wide area networks, local area networks, metropolitan area networks and the like;
and a user interface module 216 that receives user commands through input devices 208 and generates user interface objects on display device 206;
• a GNSS 218 module that receives and processes signals from the global navigation satellites 110-1 through 110 ~ N via one or more GNSS 260 antennas, the GNSS 218 module including a tmax max 220 filter module configured to estimate a status 226 of satellite navigation receiver 120 based on satellite navigation measurements. 224 obtained from signals received from global navigation satellites 110 in view of the receiver antenna 260;
»Ephemeris data 228, which includes a set of parameters used by receiver 120 to predict orbits and clocks of global navigation satellites; and ♦ correction signals 132 (for example, signals received from a service that provides GNSS correction signals, as represented by system 130 in Figure 1) for errors in the predicted orbits and clocks of global navigation satellites 110-1 through 1.10-N.
In some modalities, the correction signals 132 include corrections not only for errors in orbits (for example, orbital position and speed) and in the predicted clocks of satellites, but also corrections to compensate for tropospheric and ionic effects, predispositions of fractional phase of wide track and narrow track of the satellite and / or inter-frequency predispositions in code and carrier phase measurements.
At each respective reference station 140, the communication module 214 includes instructions 142 for sending the measurements made by the respective reference station. 140 to the computer system 130 through one or more communication interfaces 204 (wired or wireless) and one or more communication networks 150, such as the Internet, other wide area networks, local area networks, networks metropolitan area and the like. Typically, reference stations 140 have a more substantial housing than mobile receivers, typically a construction or other durable structure that is positioned durable in a known location.
I
Each of the elements identified above can be stored in one or more of the previously mentioned memory devices and corresponds to a set of instructions for performing a function described above. The instruction set can be executed by one or more processors (for example, CPUs 202). Do the modules or programs identified earlier (ie, instruction sets) not need to be implemented as svfihmo programs, procedures or modules separate and thus several subsets of these modules can be combined or otherwise rearranged in various modalities. In some embodiments, memory 210 can store a subset of the modules and data structures identified earlier. In addition, memory 210 can store additional modules and data structures not described above.
Although Figure 2 shows a “satellite navigation receiver”, it is intended that Figure 2 is given more as a functional description of the various features that may be present in a satellite navigation receiver than as a schematic structural representation of the modalities described here, In practice, and as recognized by those skilled in the art, items shown separately can be combined and some items can be separated.
In some embodiments, each of the reference stations 140-1 through 140-M includes a satellite navigation receiver that includes components and modules described in relation to Figure 2.
Figure 3 is a block diagram illustrating computer system 130 according to some modalities. Typically, computer system 130 includes one or more processing units (CPU's) 302, one or more network interfaces or other communications interfaces 304 (for example, to communicate with reference stations 140 and communications satellites 160, Figure 1 ), memory 310 and one or more communication buses 309 to interconnect these components. Communication buses 309 can include a circuit system (sometimes called a set of c / nps) that interconnects and controls communications between system components. Optionally, the computer system 130 5 may include a user interface 305 comprising a display device 306 and one or more input devices 308 (for example, one or more of a keyboard, a mouse, a screen touchscreen, a numeric keypad, etc.). Memory 310 includes high speed random access memory, such as .DRAM, SRAM, RAM DDR or other solid state random access memory devices, and I was able to include non-volatile memory, such as one or more storage devices in magnetic disk, optical disk storage devices, memory devices / Zus / · or other non-volatile solid state storage devices. Memory 310 may optionally include one or more storage devices 15 remotely located in relation to CPU (s) 302. Memory 310 or, alternatively, the non-volatile memory device (s) in memory 310 , comprise non-temporary, computer-readable storage media. In some embodiments, memory 310 or. the computer-readable storage media of memory 310 store 20 the following programs, modules and data structures, or a subset of these:
<a 312 operating system that includes procedures for handling various basic system services and for performing tasks that depend on o-Aw:
♦ a communication module 314 that is used to connect computer system 130 to other computer systems through one or more communication interfaces 304 (wired or wireless) and one or more communication networks (for example, the network 150 in Figure 1), such as the Internet, other wide area networks, local area networks, metropolitan area networks or a combination of such networks;
♦ a 318 satellite tracking module that receives and processes signals from global navigation satellites 110-1 to 1 ΙΟΝ through reference stations 140-1 to 140-M, the satellite tracking module 318 including a tracking module filter 320 configured to- estimate a state 326 of global navigation satellites 110-1 to 110-N based on satellite navigation measurements 324 obtained from a set of reference stations 140 (Figure 1) and a measurement module of integral ambiguity resolution 322 which determines when 10 a predefined condition has been met e. then, modifies the filter update process to apply a predefined constraint to. one or more double difference integral ambiguities during updating the state of the Áh / nmr filter as described in more detail below in relation to Figures 4-5;
® ephemeris data 328, which includes a set of parameters used by the computer system 130 to predict orbits and clocks of global navigation satellites;
♦ reference station position information 340, which specifies the inspected positions known to the reference stations
140 (Figure 1); and ♦ a correction module 330 that uses state 326 of global navigation satellites 110-1 through 110-N to generate .132 correction signals that correct orbital deviations (ie errors in orbits- and predicted clocks) ) of global navigation satellites 110-1 to 110-N in relation to the predicted orbits and clocks broadcast from the satellites.
As explained in the description of satellite navigation receiver 120 (Figure 2), satellite navigation measurements 324 obtained from a set of reference stations 140 are based on the signals received by the reference stations from the navigation satellites global 110-1 to 110-N. Also, as explained, the correction signals 132 generated by the correction module may include corrections that compensate for additional sources of navigation signal errors, in addition to errors in the orbits and predicted clocks of the global navigation satellites 1.1 (1 such as tropospheric and ionospheric effects, fractional predispositions of wide track, and narrow satellite track, and / or inter-frequency predispositions in code and carrier phase measurements.
Each of the elements identified above can be stored in one or more of the previously mentioned memory devices, and corresponds to a set of instructions for performing a function described above. The instruction set can be executed by one or more processors (for example, 302 CPUs). The modules or programs previously identified (that is, instruction sets) do not need to be implemented as separate programs, procedures or xq / twwe modules and, therefore, several subsets of these modules can be combined or otherwise rearranged in various modalities. In some embodiments, memory 310 can store a subset of the modules and data structures identified previously. In addition, memory 310 can store additional modules and data structures not described above.
Although Figure 3 shows a “computer system”, it is intended that Figure 3 is given more as a functional description of the various resources that may be present in a set of computer systems than as a structural schematic representation of the modalities described here In practice, and as recognized by those skilled in the art, items shown separately can be combined, and some items can be separated. For example, some items shown separately in Figure 3 can be implemented on individual computer systems and individual items can be implemented on one or more computer systems. The actual number of computer systems and how resources are allocated between them will vary from one implementation to another, and may depend, in part, on the amount of data traffic that the system must handle during periods of peak use, as well as during periods medium use.
Determine a State for a Plurality of Satellites from. Global Navigation Using Standard Jet Estimation
Before proceeding with the discussion about the application, in the state update computations performed by a filter restrictions on values of doubly differentiated ambiguity, to generate a better fà / wt state from which correction information is generated, and instructive to discuss a implementation of standard ÁAZmun filter. Note that an ÃAdmon filter, as used in this specification, includes standard filters, as well as extended and modified Kalman filters. The modalities described here can be applied to any of these types of Kalman filters. Kalman filters have two computation phases for each measurement period: a forecast phase and an update phase. In addition, in general, a Ka / man filter predicts and updates both an estimated state of the device or system that is tracked by the filter and an estimated estimate covariance (often referred to simply as estimate covariance or covariance) , which represents the estimated accuracy of the computed state.
Typically, the An / nmn filter is a procedure (or module) or set of procedures (or modules) performed by one or more processors, The Kato filter is performed repeatedly (for example, once a second), each time using measurements of new code (also called pseudo-range measurements) and carrier phase measurements, to update the Kalman filter status. Although the equations used by filters
Ka / man are complex, filters are widely used in the navigation field and therefore only those aspects of Xn / nm filters that are relevant to the present invention need to be discussed in some detail. It should be emphasized that, although Kahnan filters are widely used in GPS receivers and other navigation systems, many aspects of these filters are used will vary from one implementation to another. For example, Xufemn filters used in some GPS receivers may include states that are not included in other Ku / mun filters, or they may use equations somewhat different from those used in other .XòZnmm filters.
Consider sets of satellite navigation measurements made in the sequence of time denoted as L _s , L ₂ , ..., L _ti , in which the subscript denotes the time in which the satellite navigation measurement is taken and in which each set Satellite navigation measurement tool is defined as a measurement vector. The unknown state vectors at each measurement time are denoted as Xj, X ₂ , ..., X _n , respectively.
The relationship between the expected value of satellite navigation measurements as a function of unknown state parameters can be described as E (L _k ) ~ .F (X0. The difference between satellite navigation measurements and their expected values, sometimes referred to as pre-fixed residues, is designated as 2. The linear observational model whose norm (ie, length) must be minimized is given by:
V _k - HòX ^; _k - (L _k ..... f (X ' _k )) - ΗδΧ' _k - 2 (A) where Vk is the residual vector (ie, post-measurement update), XK is the vector parameters before the measurement update, H is the sensitivity of the satellite navigation measurements in relation to the unknown state parameters (partial derivatives of f (Xk) in relation to the parameters of the state vector), õXrk is the correction in relation to the state vector that minimizes the residual vector norm.
In standard Ka / man filter processing, satellite navigation measurements are considered to have a noise, ε, which is not correlated between measurement times. Furthermore, individual satellite navigation measurements at a specific measurement time are also considered to be uncorrelated. Note that when satellite navigation measurements are correlated, it is often possible to remove the correlation in a pre-processing step. The covariance matrix for a given measurement period is defined by the diagonal matrix R:
) = £ = 4 (B) where r = is the i-th diagonal element of the covariance matrix R, which represents the covariance of the i-th satellite navigation measurement 10.
In some ways of implementing the Kalmcm filter, the inverse of the matrix R is used instead of R. The inverse of R is also referred to as the weight matrix W in which the elements Wj of W are simply the inverse of the individual elements of R (that is, vq ~ 1 / η),
The relationship between sequential epochs of the vector X _b X ₂ ,
Shah is established statistically as:
(C) where the state vector is unknown at the time k, it is a matrix (also called a transition matrix) that relates X ^ to Xí _; , and U _k is a vector whose elements are a sequence of white noise 20 (i.e., a sequence of zero mean random values that are not correlated).
The covariance matrix associated with U _k is considered to be known and is denoted as:
Ç * o »where U ^} j is the transposition of Uí, which is equal to the non-transposition of U; by virtue of U; be a diagonal matrix.
The Kabnap filter estimate of the state after k ~ l epochs is X '. I with a corresponding covariance of P .} ,. The predicted state and the corresponding covariance matrix for the next season, k, is:
(E) (F) where: X g and P j are estimated states and variance of the filter Xu / nmn, respectively, for the time kl, and ÇÇ are transition matrices of the state of the filter Xn / mrm and dynamics, respectively , between periods kl and k, and X ' _k and P are predicted states and variâneia of the filter Kn / amn, respectively, for season k. The estimated state and variance of the áa / max filter are sometimes called the state and variance (or the calculated state and variance) of the object or system whose state is being tracked by the Kahnafí filter.
In order to update the results using the measurement vector at time k (the observation equation), the following formulas are used. The gain matrix of Âh / mi, K, is
X «+ kV (G)
The update of the measurement of the state vector (parameter), X ~ _k , also called the estimated state of the Kabnan filter or the computed state of the object or system, is («)
The update of the measurement of the covariance matrix, P , also called the estimated covariance or computed covariance of the object or system XWmcm filter, is (I) where I is the identity matrix.
The process described above in relation to equations (A) to (I) can be used to determine a state of a plurality of global navigation satellites (for example, to track satellite orbits), as described below in relation to the Figure 4,
Double Difference Ambiguity Values and Restrictions on Double Difference Ambiguity Values
The precise determination of the orbit and the clock in precision of 3 ~ 5 centimeters for Global Navigation Satellite Systems (G.NSS) requires that carrier phase ambiguities are correctly resolved. Because of satellite and receiver dependent predispositions, resolving ambiguity about non-differential carrier phase measurements is challenging.
This document describes a new approach to resolve carrier phase ambiguities using doubly differentiated integral ambiguities and applying constraint on doubly differentiated integral ambiguities when updating the state of an á / mart filter. The state of the Àb / ruto filter represents position, trajectory and clock values of global navigation satellites 110-1 through 110 ~ N (Figure 1).
Carrier phase ambiguities are ambiguities in the distance measured between a satellite and a receiver. These are often called integral ambiguities, because the correct value of the ambiguity is an Integer that is multiplied by the applicable wavelength. The sum of the measured carrier phase and the value of the resolved integral ambiguity, multiplied by the applicable wavelength, is equal to the distance between the satellite and the receiver. Integral ambiguity is measured in “cycles”, since they are values, such as the fractional difference between floating and integral values of a respective integral ambiguity and the standard deviation (also called sigma) of integral ambiguity.
The basic observable element for the precise determination of the orbit and clock of the GNSS satellite of a respective satellite is a linear combination (LC) without ionosphere with zero difference of LI / L2 carrier phases shown in the following equations (3) and (4 ). The first order ionospheric delay in the original observable element is eliminated by the linear combination of dual frequency carrier phases. Broadway ambiguities can be resolved using the combination shown in equations (1) and (2).
Wide track ambiguity can be computed for each satellite (without differentiation across satellites or receivers) using the equation Afe / àrmme- lJkõbenn shown in equation (1). The wide track ambiguity is computed by subtracting the wide track phase from the narrow track pseudo-range observations, as in equation (1):
Solving this equation for track ambiguity. broad and predisposition results in equation (2):
+ ¾ 1 ^φ · - ^φ ι) = *> ί - (Φ, - «> 1 (2) where:
P ;, Ps are pseudo-range measurements in meters at GNSS f ₂ frequencies, respectively;
λι, λ are the wavelengths in meters for GNSS frequencies f), respectively;
Φη Φ ₂ are the measurements of the carrier phase in cycles at frequencies fl> Í2 of the GNSS, respectively;
c and the speed of light (m / s);
N ^! b Ns are the integral ambiguities for satellite i at frequencies f), f> of the GNSS, respectively;
B ^l _w is the predisposition of wide track in cycles that depend on both the satellite and the receiver.
N ,. - N - N’s is the broadband ambiguity in cycles for satellite i

Linear Combination Without Non-Differentiated Lonospherical Refraction
The linear combination without non-differentiated ionospheric retraction of the measurements of the carrier phase LI / L2 can be expressed, as in equation p):
~ /> · * · R, + ^f ψ T s where:
p is the geometric distance between the center of the satellite phase and the .10 center of the corrected phase of refraction of the receiver that includes orbital, satellite error, displacement of the receiver, etc .;
r _f is the receiver's clock error;
t is the satellite clock error;
T is tropospheric delay (m); and
AMBu is the predisposition of the carrier phase, measured in meters, of a linear combination without ionic refraction of the integral ambiguities for two signals with different wavelengths (for example, LI and L2) coming from the same satellite.
This ambiguity term can take different forms, as shown in equation (4):

on what:
is the wavelength of the narrow path, typically with a value of approximately 10.7 in;
c “fo Τ '/ D
is the wavelength of the wide track, typically with a value of approximately 86.4 cm;
Xa ™ 4- JV ₂ g ₃ combined narrow track ambiguity; and
6 ».. ~ $ _{g am} kjgyj ^ _{a (} j _e 4 _{and V} j _a wide.
Ambiguities can be resolved in two stages. The first step is the resolution of wide track ambiguity using equations (1) and (2). Details are discussed below ("Individually Differentiated Wide 10-way Ambiguity Resolution"). The second step is the resolution of narrow track ambiguity. It is computed by inserting the full broadway ambiguity resolved in equation (4). This narrow track ambiguity, which is resolved in the second step, may be the value of the integral ambiguity associated with both the LI frequency and the L2 frequency or the combination of the narrow track of both the LI frequency and the L2 frequency. The effective wavelength of the narrow track ambiguity is about 10.7 cm, regardless of which of the narrow track ambiguities are resolved. This wavelength of the narrow path is easily computed for both Ll ambiguity. and for the L2 ambiguity using equation (4). If the narrow track combination of both the LI frequency and the L2 frequency is used, the explicit wavelength ambiguity is only half the size. However, since the combined narrow track ambiguity has the same odd - even integral characteristic as the wide track ambiguity, the same effective wavelength (10.7 cm) results for the combined narrow track 25 due to the odd - even constraint.
Individual difference wide saw ambiguity resolution
The long-term fractional predisposition term B ^! _w that appears in equations (1) and (2) is called wide track predisposition (in cycles) and depends on both the satellite and the receiver. By forming the only difference in equation (2) between two satellites in the same constellation (for example, GPS, GWAGLSS or Gu / ueo) at a receiver location, the receiver-dependent component of the wide track predisposition can be canceled. The remaining wide track predisposition part contains the single track wide track predisposition between satellites.
There are two options for computing fractional predispositions for broadways. They can either be pre-computed for each day in a batch mode 10 or they can be estimated in real time for each season during the wide track ambiguity resolution process. The latter option is typically preferred, as it can be performed concurrently while individually differentiated broadband ambiguities are being resolved.
Computing the Satellite Broadband Predisposition
A & / »íí» í filter approach is used to estimate all satellite predispositions for the resolution of wide track ambiguity. A predisposition state for each satellite is required. The only difference in equations (!) And (2) between satellites is used to estimate each satellite predisposition during the wide track ambiguity resolution process. The computation procedure involves the following steps:
1. Initialize the KaÍwax filter using large variance (for example, 0.5 cycle squared) and zero for the filter status of each satellite's broadband predisposition in a constellation (for example, GPS, GúUAvíóN or Ga / iúm) navigation satellites. If satellites from multiple constellations are being tracked, initialize the filter state for each track predisposition. satellite in each constellation.
2. In each season, compute the term CMC (carrier minus code) (that is, the measurement of narrow track code minus the measurement of the carrier phase of the broad track) given in equations (1) and (2) using each of the code and carrier phase measurements at each location.
3. Uniform the CMC term using a low-pass filter and reset the filter if a shift in the carrier cycle or a large change in value is detected.
4. Select a high-elevation reference satellite for each location and each constellation.
5. Form an individual difference for each uniform CMC by subtracting each uniform CMC value for the selected reference satellite.
6. Round the CMC value individually for each satellite.
7. Update each predisposition state and variance matrix using the differentiated fractional CMC measurement (that is, the rounding quantity) for each location.
S. Apply the zero average constraint of all satellite predispositions for each constellation as additional virtual measurements in the Ào / mm filter for predispositions of wide track ambiguity.
9. Perform the time update of the Âfo / mcm filter for the satellite broadband predispositions after the update of fractional CMC measurements for all locations is completed.
10. In each season, repeat steps 2 through 9.
The real-time predisposition computation module is designed and implemented to automatically compute satellite broadband predispositions at a specific predisposition update interval. The satellite's wide track predisposition will be indicated as ready to be used after data for a specified period of time (for example, 4 to 6 hours) has been processed. In some circumstances, such as when all navigation satellites are functioning properly, all wide track predispositions are treated as
Zb ready to be used after the Kabncm filter has been active (for example, since the last time it was restarted or started) for at least six hours. When the satellite predispositions from the real-time Au / mun filter estimate are ready, the full wide track ambiguity resolution process will begin.
Resolution of Ambiguity of Wide Difference of Individual
The process for resolving individual difference wide track ambiguity consists of several steps in real time that include: I) selecting the reference satellite and switching the logic, 2) computing the wide track ambiguities of individual difference and associated statistics, 3 ) ambiguity validation and recovery from incorrect fixation; and 4) the prediction of the satellite's wide track predisposition.
The computation of broadway ambiguities of individual difference for each satellite at each location (ie, each reference station) and the associated statistics includes:
♦ compute the value of CMC (narrow track less less wide track code) and its variance as a function of elevation and uniformity time;
♦ update the measurement of the floating ambiguity amphi / asva filter (ITZ / Zoot) and its sigma value (JfZ / 7ont sdgnm);
♦ subtract the predisposition of fractional wide track (JWas) from the floating ambiguity (RZ / foot) and round to the nearest whole number to the potential fixed integral value;
«Compute the residue by subtracting the WL predisposition and the whole number from the floating ambiguity; and ♦ update the Àofew filter ambiguity residue (RZm ') and its sigma (RZ res / fgmn).
In. In some modalities, the ambiguity validation and the wrong fixed integer recovery procedure is as follows. Ambiguity is resolved or fails (IFF / b) when the following criteria are met:
• The variance of the floating ambiguity (IFLQfoa / signtu) must be within a specified limit, (for example, 0.1 cycle);
• given a level of confidence, (for example, 99.7%), the residue (01 rcx) must be in the associated multiples (for example. 3) of a floating ambiguity sigma value (RZj & vdjngnm), as defined by a normal distribution (for example, the residue, must be less than 3 times IFL ,, βοαί sigma); and • the standard deviation (sometimes called sigma) of the residue must be a limit value, (for example, 0.25 cycle).
“Ambiguity fixations (in a displacement time window) are identified as incorrectly fixed to an integer if the standard deviation of the residuals (in this displacement window) is above a limit value, (for example, 0.25 cycle).
• When it is determined that the WL fixation criteria have not been met, the IFLQfr indicator is set to (wrong fixation) and the wide track ambiguity as a floating value is restored.
A pseudocode representation of the wide track ambiguity resolution process is shown in Table 1, below.
Repeat for Each Epoch {
Repeat for each reference station {
Select Reference Satellite (e.g., the highest elevation satellite);
Record measurement data;
For each satellite in. view {
Perform Wide Lane smgle-differenced integer ambiguity resolution; Perform ambiguity validation and wrong fixed integer recovery;
}
Update Wide Lane Biases in Kalman Filter}
Table 1 Flow of the Broadway Ambiguity Process
Resolution of Double Difference Narrow Path (DD) Since there are predispositions dependent on both the satellite and the receiver, the narrow path ambiguity is not an Integer. However, the doubly differentiated narrow track (DD) ambiguity between the receiver and the satellite has an integral characteristic (that is, it has an integral value) after the fixed wide-path ambiguities with individual difference are inserted into the equation (4 ).
Concept of Integral Restriction of Ambiguity DD
The equations shown below are used to describe 10 how a single integral double-differentiated ambiguity (DD) constraint is implemented in the Ku / man filter (which is used to determine the orbital state of a plurality of satellites). Additional independent ambiguity restrictions can be implemented during the same processing time as the Kalman filter or at different times. No loss of generality occurs by removing the subscripts in equation (4) for the narrow path or the combination of narrow path considered specific and, instead, by using the subscript to indicate the specific receiver (ie, reference station) involved. Thus, the narrow path ambiguities in equation (4) for two specific receivers can be indicated with subscripts A and 20 B. Thus, the floating narrow path ambiguities can be indicated by ^Λ 'Ζ ^Λ ' ^ RE for the two receivers in equation (4) and its variances and covariance for satellites t, j and reference stations A, B, respectively. These values can be easily obtained from the Ku / nmn filter vector and the variance matrix ~ oovariâncla after the measurement update in the filter Κα / mam The computation of the floating ambiguity DD ^a and the variance is computed using the equations (5) and (6).
(5)
V Τ.-ί, Λ
~ '^ μ * ^ <4'&Μ; ⁺ <AA ~ & 3 _λ 4 <
§ΛΛ ⁺ Λ; Λ ⁺ ^ Λ “&; Α * Cm)“ Cí, ^ ^ ¾.¾
2 ^. ^ ~ 2§á _f , 4 ^ 2¾ ^. -2¾ ^ “2 ^ _{β; S;}
The definition of an indicator to indicate successful ambiguity resolution must be made based on a number of factors. The probability of success is, among other factors, a function of the selected limits. It is also a function of the filtering time (which should be as short as possible without making the incorrect ambiguity resolution rate excessive). The probability of final success depends strongly on the chosen limits I) for the permissible residual fraction by which the floating ambiguity differs from an integral value, 2) the perpetual sigma value of the floating ambiguity and the minimum filtering time. The probability of successful ambiguity resolution can be derived, in theory, as a pure function of the floating ambiguity and its variance, which are a function of the filtering time. It should be noted that redundant ambiguity pairs may arise due to satellite or ambiguity pairs. sites can form link chains that can be made redundant. Fixing redundant ambiguity pairs should be avoided. It is easy to determine whether a derived floating DD ambiguity is already an integer or not since its computed variance will be zero, considering that there is no numerical computational error. In practice, a very small epsilon value can be used as a check that the variance is very close to zero.
If the decision is made that it can be fixed at 4VA '^. _where it is equal to the integer with a value closer to x, then the integral ambiguity constraint DD is introduced in the filter state and in the variance matrix.
This is accomplished by introducing virtual measurements with the variance - covariance matrix. associated with the jMma filter ». Multiple floating and / or fixed restrictions can be imposed on the same virtual measurement. The state vector and the variance matrix ~ associated covariance for 10 individual ambiguities are as shown in equation (7).
$ 4.4 4Λ $ 43, <
$ 4.4 $ 4.4 $ 4.4 $ 4.4 ^ 4..4 $ 4.4 * $ 4.4 $ 43. $ 4.4
The new specific restriction imposed on the Ka / mun filter for _A A, <<
force the doubly differentiated floating ambiguity to be an integer (or next integer) is: CÓX ~ W where C ”(<.. 0 1 -1 -1 1 0
In equation (8), δΧ is the state correction vector that arises from the imposition of the integral restriction DD. Stated in another way, δ.Χ. is the state update corresponding to a doubly differentiated integral ambiguity constraint that is used by the filter The value of W in equation (8) is referred to as the wrong conclusion and the difference between the rounded value of the double difference ambiguity is defined as 20 floating minus the non-rounded value, specifically, W is computed as given in equation (9).
r (9)
Altem.ativam.ente, the mistaken conclusion, W may be defined as _{the u} ^ O _Eren Jif. _{that in} fixed integral ambiguity and floating ambiguity.
Considering equations (7) and (8), the following observation equation for virtual measurements can be recorded as equation (10), (10) in which P is called a weight matrix, an inverse of the variance - covariance matrix; V is the post-suitable residue; T is the transposition operator and K is a linked singular coefficient (defined below) if only a single integral DD ambiguity is introduced during an iteration of the Âzfown filter update, and it is a vector if multiple DD ambiguities are introduced in the same iteration of updating the Âhta filter. The general least squares solution of equation (10), considering the double differentiation ambiguity constraint is designed to minimize Υ ^λ ΡΥ, as represented by equation (11).
w aásr '
Considering equation (8) and replacing equation (10) in Equation (11.) produces the following equations (12) and (13):
P5XAC ^T K-0 (1.2)
Cdx-W (13)
Since P is the matrix, in full rank, equation (12) can be rearranged like equation (14),
SX ~ -P ** C ^T K (14)
Replacing equation (14) in equation (13) provides equation (15):
K - (CF ^ C ^^ W (15)
Replacing equation (15) in equation (12) produces the following:
where, which can be easily verified (for example, see equation (6), supra), and M. is the doubly differentiated covariâneia matrix, defined as in equation (17).
f Ϊ | ~ ίΛ * · ^ Α 07} j «<* ~ & -M _z “ & ί-! Λ
Ç Κ.λ ~ Í> <a ~~ & <Λ · J
Based on equations (12) and (13), it can be shown that the 5 variance matrix for K and δΧ is (p C V (c 0 J
Using the submatrix inversion formulas, the variance of the estimated state vector after introducing an integral ambiguity constraint DD can be derived as Equation (18)
(18)
Based on the above, the existing Àa / w »filter can be modified by using equations (16) and (18) to introduce the DD ambiguity constraint in the filter update computation. After the state of the Kalman filter has been updated according to equations (16) and (18), the mistaken conclusion _{between the} floating ambiguity and the fixed integral ambiguity is both equal to 0 and close to zero (here 15 defined to mean that the wrong conclusion was reduced in magnitude to less than a predefined limit value, such as 0.05 cycle). It should be noted that the estimation of all state parameters is improved after one or more DD ambiguity constraints are applied.
After the DD full ambiguity constraint is applied, the state corrections for narrow path ambiguities, Aj and p _(} j, can _be derived as shown in equations (19) through (22).
«Um + p., 3. J --- £ .......... W
Kwí ·;
~ wm ~ · *, z »~ ^& 0-0
... i UVxVH -avaR
U «, ^:“ M> ”^ * <Λ * 'A ................. t ^ N
This solution update satisfies constraint equations (8) or (9).
'Additional formulas for fixing multiple narrow track ambiguities at the same time and time are given in equations (23) and (24).
(23)
- _I5 g - (24) where C is an mxn matrix;
m is the number of narrow track ambiguities to be fixed; n is the number of states in the Àu / mo / í filter state vector.
The restriction that narrow track ambiguities are exact integers can be alleviated to take in. minor system errors. Equations (25) and (26) are modifications of equations (23) and (24) to allow small deviations from integers for narrow track ambiguities.
At »4- F (2S) *« F Àf (W where R is a diagonal matrix of the precision u prior / (variâneia) of the narrow path integral ambiguities D.D and C is a matrix m
Selection of Doubly Differentiated Integral Ambiguity Sets
Independent
Two approaches that can be used to select sets of independent double-differentiated ambiguity are described below. A first approach, described in more detail below, is to use independent frameworks. Each re-ferenclal corresponds to a pair of reference stations; the term "referenciaF 'refers to a" line * between the locations of two reference stations. In a set of independent benchmarks, none of the benchmarks can be reproduced by forming a linear combination of other benchmarks in the set. For every 10 references, the satellite with the highest average elevation is chosen as a reference satellite. From the independent referentials and the determined reference satellites, a set containing all the independent doubly differentiated integral ambiguities can be determined. A second approach, also described in more detail below, is to form all possible combinations of integral ambiguities doubly differentiated from all possible references. Note that any doubly differentiated integral ambiguity connected with a common data interval shorter than five to fifteen minutes is ignored, as it is generally difficult to resolve ambiguities during such short filter intervals.
Independent Referential Approach
For a given global network of GNSS receivers (for example, reference stations 140, Figure 1), many different combinations of independent references (i.e., references between pairs of reference stations) can be formed. Graphic theory can be used to form the best combinations of independent references. Given a connected non-directed graph, a tree transposing this graph is a sub-graph, which is a tree and connects all the vertices (reference receivers) together. Any referential that would cause an area to be confined is not an independent referential. A single graph can have many different transposition trees that connect the receivers together. You can also assign a weight or cost to each edge (referential), which is a number that represents how unfavorable it is, and use 5 this to assign a weight to a transposition tree by computing the sum of the edge weights in this transposition tree. Therefore, a minimum transposition tree, or minimum transposition tree, is a transposition tree with a weight less than or equal to the weight of each other transposition tree. More generally, any undirected graph (not necessarily 10 connected) has a minimal transposition forest, which is a union of minimal transposition trees for its connected components.
If each edge has a different weight, then there will be only one unique minimum spanning tree. Prim's algorithm is an algorithm in graphic theory that finds a minimum transposition tree for a weighted connected graph. This means that it finds a subset of the edges (referential) that forms a tree that includes each vertex, in which the total weight of all edges in the tree is minimized. The algorithm was discovered in 1930 by the mathematician Vojtech Jarník and, 20 later, independently by the computer scientist Robert C. Prim in 1957 and rediscovered by Edsger Dijkstra in 1959. Therefore, it is sometimes called the DJP algorithm, the Jarník algorithm or the Prim-Jarník algorithm.
The algorithm continuously increases the size of a tree, 25 starting with a single vertex until it spans all vertices. It should be noted that the computation time is Ο (ν ^ζ ). V is the number of vertices (reference locations) and the large O notation is used in computational complexity theory to describe how the size of the input data affects a use of the computational resources algorithm (usually.
runtime or memory).
Any Possible Referential Approach
Theoretically, any set with the maximum number of independent DD ambiguities can be expressed as a linear combination of the others, and they are statistically equivalent to each other if both the estimates and their complete eovariance matrix are considered. For a system with around sixty reference stations and thirty global navigation satellites, with about 500 - 700 independent DD ambiguities, only the estimates and their variances are used to make the decision in setting ambiguity. This is because it takes time to take the correlation part into account. Different selections of DD ambiguities lead to. ambiguities with different displacements in relation to the nearest whole number and different variances and, consequently, different efficiencies in setting ambiguity. In principle, independent DD ambiguities are chosen starting with the most reliable of all possible ambiguities. Dependent ambiguities in fixed DD ambiguities can easily be removed from the list to be fixed by checking zero fractional DD ambiguities and zero (or very close to zero) variance. Once the independent benchmarks or all possible benchmarks have been determined, the satellite with the highest average elevation can be chosen as the reference satellite. Information from independent DD ambiguities or all possible DD ambiguities, such as base location, displacement location, reference satellite and satellite, floating ambiguities and their variances, can be stored.
DD Integral Ambiguity Resolution Strategy
The ratio W for a particular referential and its estimated doubly differentiated integral ambiguity value, based on equations (7) and (8), can be computed as shown in equation (27) ratio w ~ ------ ^s —...:
where: Gq is the square root of the unit variance, ο denominator is (or, alternatively, corresponds to) the standard deviation of the estimated double-differentiated integral ambiguity (which is Same as the square root of the variance of the estimated double-differentiated integral ambiguity) , the brackets in the numerator represent the function and the absolute value in the numerator corresponds to the difference between the integral and floating versions of the estimated double-differentiated integral ambiguity value. Additional information on the W ratio can be found in (1) J. Wang, MP Stewart & M. Tsakiri, “A rifixirimòwíon 'promrinte / õr ambiguity resolution on rite Journal of Geodesy, 72 (1.1), 644-653 (1998 ); and (2) L. Dai, J. Wang, C. Rizos, & S. Han, 'riVeri / cring amtmp / teràt bmses / òr real-time ambiguity resolution in GPS / G.LONASS reference station networks' *, Journal of Geodesy, 76, 617-628 (2003).
In practice, q (in equation (27)) can either be considered equal to I or can be computed using post-adequate residues or n-í where V represents post-adequate residues; n-t is the number of degrees of freedom and P is the weight matrix of the measurement (not to be confused with the magnet covariance matrix).
In theory, the W ratio should satisfy the normalized 'Ónrienf distribution'. The W ratios of independent ambiguities, or all possible doubly differentiated integral ambiguities, can be classified in descending order. The independent dual.and differentiated integral ambiguities can be fixed by applying equations (16) and (18) if the conditions in equations (28) through (30) are satisfied.
¢ 28) (29) (30) where a, b and c are empirical limit definitions. For example, in some embodiments, a has a value between 0.1 and 0.25 cycle, b has a value between 0.03 and 0 J cycle and c has a value of 3 (equivalent to 99.9%) or higher. These threshold parameters can be tuned to optimize the disambiguation process based on large data sets.
After one or more doubly differentiated integral ambiguities are fixed as integers, that is, after one or more constraints have been applied, all non-fixed doubly differentiated integral ambiguities and variances from independent sets of nonfixed ambiguities are recomputed and classified again. The ambiguity resolution process for when there is no longer any doubly differentiated integral ambiguity that has not yet been fixed (that is, by applying a corresponding restriction in the 15 update computations of the Afo / mnn filter) and that satisfies the equations (28 ) to (30).
DD Ambiguity Resolution Procedure
Two alternative methods or processes for narrow path ambiguity resolution are described here. These methods or processes 20 are here called the integral ambiguity resolution process (or procedure) with independent referential and the ambiguity resolution process (or procedure) integral of all referential. These two processes (or procedures) are described below in relation to Figures 5A-5B and Figures 6A-6B. The process represented by flowchart 25 of Figures 6A-6B corresponds to, or replaces, operations 522 and 524 of Figure 5A.
Integral Ambiguity Resolution Procedure! with referential
Independent
1. Determine a set of independent references (520, Figure 5). In some modalities, this is accomplished using the Pnm algorithm. The number of independent references is equal to the number of (local) reference stations minus 1. In some modalities, the independent references are mathematically independent, meaning that no reference in the set can be determined as a linear combination of other references in the set.
2. For each referential, determine the reference satellite .10 with the highest mean elevation and compute fluctuating and integral values for the corresponding differentiated integral dual ambiguity (520, Figure 5).
3. For each referential, store the following information: the two reference stations at the ends of the referential, satellite identifications, identification of the reference satellite, doubly differentiated integral ambiguity, the variance for doubly differentiated integral ambiguity and the W ratio. , form a “DD list to be fixed” or arrangement (see 602, Figure 6A). Any doubly differentiated integral ambiguity connected with a data filtering interval shorter than a predefined interval, typically five to fifteen minutes, is ignored (i.e., not included in the DD list to be fixed). Any doubly differentiated integral ambiguity that corresponds to a non-fixed broadway ambiguity is ignored (that is, not included in the DD list to be fixed). All other doubly differentiated integral ambiguities (corresponding to the set of independent references, excluding those to be ignored) are added to the DD list to be fixed.
4. Select the doubly differentiated integral ambiguity from the list DD to be fixed, where (A) has the maximum ratio W and also (B) satisfied the condition of equations (2S) to (30) (see 604, Figure 6A). If more than one doubly differentiated integral ambiguity has the maximum value of the ratio W, select the group of integral doubly differentiated ambiguities with the maximum ratio W which also satisfies the condition of equations (28) to (30); the selected group is called the “best group” of doubly differentiated integral ambiguities. If there is at least one doubly differentiated integral ambiguity selected from the DD list to be fixed that satisfies the condition of equations (28) through (30), proceed to step 5; if none of the remaining doubly differentiated integral ambiguities in the DD list to be fixed satisfy the condition of the equations, go to step 8 (the disambiguation process ends, at least for the current time).
Step 4 can be implemented by ordering the doubly differentiated integral ambiguities in the DD list to be fixed according to their values of the W ratio, from the highest to the lowest W ratio, thereby producing an ordered list (see 602, Figure 6A). Starting with the ambiguity or integral ambiguities doubly differentiated with the value of the highest W ratio (604, Figure 6A), test each ambiguity doubly differentiated in the ordered list until both (1) the end of the list is reached without selecting any integral ambiguity doubly differentiated (616 ~ yes, Figure 6Λ), in which case the process goes to step 9, how much (2) one is found. doubly differentiated integral ambiguity that satisfies the condition of equations (28) to (30) (608 ~ yes, Figure 6A). In the latter case, select the identified doubly differentiated integral ambiguity and also select any other doubly differentiated integral ambiguities with the same ratio W that also satisfy the condition of equations (28) to (30) (604, 606, Figure 6A) . Go to step 5 to process one or more selected doubly differentiated integral ambiguities.
5. Modify the Knfomn filter update equations and also adjust the state of the Àfefem.n filter by applying one or more selected doubly differentiated integral ambiguity constraints (610, Figure 6A). If there is only one doubly differentiated integral ambiguity selected, modify the update equations for filter 5 ÁmWm with the restriction equations DD (16) and (18) for the selected individual doubly differentiated integral ambiguity. If two or more doubly differentiated integral ambiguities were selected, modify the update equations of the Xa / man filter with the restriction equations DD (23) and (24) for the best group of integral doubly differentiated ambiguities 10. The one or more doubly differentiated integral ambiguities selected are now “fixed * doubly differentiated integral ambiguities (see 610, Figure 6A).
6. Remove the fixed double or differentiated integral ambiguities from the DD list to be fixed (see 612, Figure 6A).
7. Recognize the DD ambiguities, the variance. and the W ratio for the remaining doubly differentiated integral ambiguities in the DD list to be fixed (see 614, Figure 6A). If the DD list to be fixed is not empty (616 ~ No), continue processing in step 4.
8. The ambiguity resolution for (62.2, Figure 6A).
Full Ambiguity Resolution Procedure for Every Possible Reference
L Determine a set of 'all possible references' (520, Figure 5). Optionally, a constraint on the maximum frame length is used (for example, frames longer than 6,000 km are excluded from the set of possible frames) to reduce unnecessary frames.
2. For each reference, determine the reference satellite with the highest average elevation and compute fluctuating and integral values for the corresponding doubly differentiated integral ambiguity (520, Figure
5).
3. For each referential, store the following information: the two reference stations at the ends of the referential, satellite identifications, identification of the reference satellite., Doubly differentiated integral ambiguity, the variance for doubly differentiated integral ambiguity and the reason W. Also, form a “DD list to be fixed” or arrangement (see 602, Figure 6A). Any doubly differentiated integral ambiguity connected with a Data Filtering Interval shorter than a predefined interval, typically five to fifteen minutes, is ignored (that is, not included in the DD list to be fixed). Any doubly differentiated integral ambiguity that corresponds to a non-fixed broadway ambiguity is ignored (that is, not included in the DD list to be fixed). All other doubly differentiated integral ambiguities (corresponding to the set of references, excluding those to be ignored) are added to the list. DD to be fixed.
4. Select the doubly differentiated integral ambiguity from the list. DD a. be fixed, where (A) has the maximum ratio W and also (B) satisfies the condition of equations (28) through (30) (see 604, Figure 6A). If more than one integral doubly differentiated ambiguity has the maximum value of the ratio W, select the group of integral ambiguities doubly differentiated with. the maximum ratio W that also satisfies the condition of equations (28) to (30); the selected group is called the "best group" of integral doubly differentiated ambiguities. If there is at least one doubly differentiated integral ambiguity selected from the list. DD to be fixed that satisfies the condition of equations (28) to (30), proceed to step 5; if none of the remaining doubly differentiated integral ambiguities in the DD list to be fixed meet the condition of the equations, go to step 9 (the disambiguation process ends, at least for the current time).
Step 4 'can be implemented by ordering the doubly differentiated integral ambiguities in the DD list to be fixed according to their values of the W ratio, from the highest to the lowest W ratio, thereby producing an ordered list (see 602, Figure 6A). Starting with the ambiguity or integral ambiguities doubly differentiated with the value of the highest W ratio (604, Figure 6A), test each ambiguity doubly differentiated in the ordered list until both (1) the end of the list is reached without selecting any integral ambiguity doubly differentiated (616 ~ yes. Figure 6A), in which case, the process goes to step 9, both (2) being found a doubly differentiated integral ambiguity that satisfies the condition of equations (28) to (30) (608 ~ yes Figure 6A). In the latter case, select the identified doubly differentiated integral ambiguity and also select any other doubly differentiated integral ambiguities with the same ratio W that also satisfies the condition of equations (28) through (30) (604, 606, Figure 6A). Go to step 5 to process one or more selected doubly differentiated integral ambiguities.
5. Modify the Ka / map filter update equations and also adjust the filter state by applying one or more doubly differentiated integral ambiguity constraints (610, Figure 6A). If there is only one doubly differentiated integral ambiguity selected, modify the update equations for the Ah / mtm filter with the restriction equations DD (16) and (18) for the selected individual doubly differentiated integral ambiguity. If two or more doubly differentiated integral ambiguities were selected, modify the filter update equations with the restriction equations DD (23) and (24) for the best group of integral doubly differentiated ambiguities. The one or more doubly differentiated integral ambiguities selected are now “fixed” doubly differentiated integral ambiguities (see
610, Figure 6A).
6. Remove the fixed double or differentiated integral ambiguities from the DD list to be fixed (see 612, Figure 6A).
7. Recognize the DD ambiguities, the variance and the W 5 ratio for the remaining doubly differentiated integral ambiguities in the DD list to be fixed (see 614, Figure 6A).
8. Remove redundant integral DD ambiguities from the DD list to be fixed (see 615, Figure 6A). Once a sufficient number of fbi integral constraints are imposed on the DD integral ambiguities, some of the remaining DD integral ambiguities will be redundant, as they correspond to linear combinations of DD-integral restrictions for which full restrictions have already been imposed. In some modalities, this operation is performed by removing, from the DD list to be fixed, all remaining DD ambiguities for which (A) the fractional part of the DD ambiguity equals zero ({DD} ~ 0) and (B) 15 variance 0. Alternatively, if a nonzero value of R is used in equations (25) and (26), supra, remove from the DD list to be fixed, all remaining DD ambiguities for which (A) the fractional part of the DD ambiguity is less than the square root of R (or another predefined limit value corresponding to the square root of R) and (B) variance ~ 0. If the DD list to be fixed is not empty (616 20 - No), continue processing in step 4 (618,620, Figure 6B).
9. The ambiguity resolution for (622, Figure 6A).
Figure 4 is a flow chart of a method 400 performed by a computer system, such as computer system 130 (Figure 1, Figure 3), to compute an estimated state of a plurality of global navigation satellites 25 and to adjust the estimated state computed by applying restrictions on one or more doubly differentiated integral ambiguities that satisfy predefined criteria according to some modalities. In some embodiments, operations 402 ~ 408 of Figure 4 are performed by filter module 320 (Figure 3) of computer system 130. operation 412 is performed by correction module 330 and operation 414 is performed by communication module 314 ,
The computer system receives (402), from a plurality of reference stations (140, Figure 1) in known locations, a plurality of satellite navigation measurements of signals from a plurality of global navigation satellites (110, Figure 1). The computer system computes (404) a state of the plurality of global navigation satellites based on the received satellite navigation measurements. In some modalities, the computer uses an Àuànun filter to perform this computation. For example, the Ko / mon 320 filter module (Figure 3) uses a Rh / nmn filter to calculate an estimated state of the plurality of global navigation satellites (for example, the state 326 of global navigation satellites 110-1 through 110 -N) for the current measurement time based on the plurality of satellite navigation measurements. In some embodiments, state 326 of the plurality of global navigation satellites includes a position of each global navigation satellite in the plurality of global navigation satellites, a speed of each global navigation satellite in the plurality of global navigation satellites and a reported time for each global navigation satellite in the plurality of global navigation satellites. In some embodiments, the state of. The plurality of global navigation satellites for the measurement time is calculated using a closed form update equation.
Operation 404 is sometimes referred to here as initial computation of the state of the plurality of global navigation satellites, which is followed by an adjustment operation (408), described below. In some modalities, the initial computation of the status of the plurality of global navigation satellites includes computing and resolving broadway ambiguities with individual differences (406). As explained, broadway ambiguities are resolved first, for example, using conventional techniques. above-described. Then the resolved wide track ambiguities are validated, and all incorrect broad track ambiguity resolutions are fixed, for example, by restoring the corresponding floating ambiguity values. By carrying out the broadway ambiguity resolution, in conjunction with the known locations of the reference stations, the estimated state of the global navigation satellites, which is also called the state of the Kafoxm filter, becomes ready for the application of doubly differentiated integral ambiguity constraints (408).
As explained below, no referential (or, 10 equivalently, none, double-differentiated integral ambiguity) corresponding to a “reference station - satellite” pair for which broadway ambiguity resolution was unsuccessful is a candidate for the application of a doubly differentiated integral ambiguity constraint. Stated in another way, since each respective doubly differentiated Integral 15 ambiguity is dependent on accurate measurements by two reference stations of the same two signals coming from a reference satellite, if any of these measurements (for a respective doubly differentiated integral ambiguity) leave In order to satisfy predefined requirements for resolving ambiguity of integral wide track, this ambiguity 20 integral doubly differentiated is not one. candidate for. application of a full restriction.
After computing an estimated state of the global navigation satellites (406), process 400 continues by performing a double-differentiated integral ambiguity resolution procedure 25 (408) to identify double-differentiated integral ambiguities that satisfy a set of predefined conditions and to adjust the computed state of global navigation satellites by adding an integral constraint on the Àcdmon filter for each doubly differentiated integral ambiguity that satisfies the set of predefined conditions. In some modalities, the doubly differentiated integral ambiguity resolution procedure is implemented according to the flowchart shown in Figures 5A-5B, as discussed in more detail below. In some modalities, the doubly differentiated Integral ambiguity resolution procedure is implemented according to the flow chart shown in Figures 6A-6B, as discussed in more detail below. It can be seen that the flowchart of Figures 5A-5B is not inconsistent with the flowchart shown in Figures 6A-6B, but instead these two flowcharts address the procedure for resolving integral ambiguity doubly differentiated from different points of view and at different levels of detail.
The completion of the double-differentiated integral ambiguity resolution procedure for a respective measurement period adjusts the estimated state of the plurality of global navigation satellites by considering the impact of the integral restrictions imposed on one or more double-differentiated integral ambiguities. In some modalities, when an integral constraint is imposed on a single selected doubly differentiated integral ambiguity, adjustments in the estimated state are made according to the Knfomn filter update equations (16) and (18), as shown. In some modalities, when integral restrictions are applied in two or more doubly differentiated integral ambiguities, the adjustments in the estimated state are made according to equations (23) and (24), as explained.
In some modalities, upon completion of the doubly differentiated integral ambiguity resolution procedure (408) for the current measurement time, the status of the revised global navigation satellites is used to calculate correction signals (for example, correction signals 132) that correct orbital deviations from global navigation satellites 110-1 through 110-N (412). The correction module 330 uses the revised status of the plurality of global navigation satellites to calculate correction signals that compensate for orbital deviations from the plurality of global navigation satellites (412). As explained, in some modalities, corrections of tropospheric and / or ionospheric effects are computed using techniques well known to those skilled in the art, and these corrections are included in the correction signals. Then, computer system 130 transmits (414) correction signals to one or more satellite navigation receivers 120. In some embodiments, correction signals are transmitted via one or more communication satellites 160. In other embodiments , other networks or communications media are used to route correction signals to satellite navigation receivers 120.
In some modalities, upon completion of the doubly differentiated integral ambiguity resolution procedure (408) for the current measurement time, process 400 is repeated, starting with operation 402, for the next measurement time.
In relation to Figure 5A. in some modalities, the performance of the doubly differentiated integral ambiguity resolution procedure (408) includes identifying a plurality of references, each reference corresponding to a pair of reference stations, and for each identified reference, computing floating and integral values for a doubly differentiated integral ambiguity corresponding to the identified framework (520). In some modalities, the identification of references (520) is implemented by the identification of a set of mathematically independent references, as shown. In other modalities, a set of benchmarks is identified without requiring them to be independent, and instead a larger set of benchmarks is initially identified. In the latter modalities, whenever full restrictions are imposed on. one or more doubly differentiated integral ambiguities, both the corresponding frameworks and all frameworks that are linear combinations of the frameworks for which integral constraints have been applied are marked as processed (see operations 612 and 615, Figure 6A).
After identifying the referentials (520), the double ambiguity resolution procedure 5 (408) identifies according to the floating and integral values computed for the doubly differentiated integral ambiguities corresponding to the plurality of identified referentials, a set of one or more doubly differentiated integral ambiguities that satisfy a set of 10 predefined conditions (522). The predefined conditions are superscribed in relation to equations (28), (29) and (30).
In some embodiments, the set of predefined conditions for a respective double-differentiated integral ambiguity includes a requirement that a fractional difference between the 15 integral and fluctuating values of the respective double-differentiated integral ambiguity does not exceed a first predefined limit value (504, Figure 5B) . For example, see equation (28) and the corresponding discussion exposed. Furthermore, in some modalities, the method includes computing a variance and standard deviation of the respective doubly differentiated integral ambiguity, and the set of predefined conditions for the respective doubly differentiated integral ambiguity includes a requirement that the standard deviation of the respective integral ambiguity doubly differentiated do not exceed a second predefined limit value (506, Figure 5B). For example, see equation (29) and the corresponding discussion exposed. In some modalities, the first limit is not greater than 0.25 cycle and the second limit is not greater than 0.1 cycle (510, Figure 5B).
Additionally, in some modalities, operation 522 includes computing a predefined W ratio (for example, see equation (27) exposed) for the respective doubly differentiated integral ambiguity, where the W ratio has a numerator corresponding to the fractional difference between the integral and floating values of the respective doubly differentiated integral ambiguity and a denominator corresponding to the standard deviation of the respective doubly differentiated integral ambiguity, and in which the set of predefined conditions for the respective doubly differentiated integral ambiguity includes a requirement that the W ratio for the respective ambiguity doubly differentiated integral is greater than a third limit (508). Furthermore, the set of predefined conditions for the respective doubly differentiated integral ambiguity includes a requirement that a redefined W ratio has a value that exceeds a third predefined limit value (for example, see equations (27) and (30) and the corresponding discussion exposed). In some modalities, the third predefined limit is not less than 2.5; in some other modalities, the third predefined limit is not less than 3.0.
In some modalities, after identifying the set of one or more doubly differentiated integral ambiguities that satisfy the predefined set of conditions (522), the doubly differentiated integral ambiguity resolution procedure (408) adjusts the computed state of the plurality of navigation satellites global according to a full-value constraint applied to each double-differentiated integral ambiguity in the identified set of one or more double-differentiated integral ambiguities that satisfy the set of predefined conditions, to produce a computed state adjusted for the plurality of global navigation satellites (524 ).
In some modalities, the set of predefined conditions includes a requirement, for a respective doubly differentiated integral ambiguity, that both corresponding wide track ambiguities have been fixed. Any double differentiated integral ambiguity corresponding to a non-fixed broadway ambiguity is ignored, and there is not even a candidate to be identified as a double differentiated integral ambiguity that satisfies the set of predefined conditions. In addition, in some modalities, the doubly differentiated integral ambiguities corresponding to the 5 identified references are filtered out, before the operation 522 is carried out, or at the beginning of operation 522, to prevent any doubly differentiated integral ambiguities that no longer meet filtering criteria. predefined numbers are included in the identified set (512). For example, predefined filtering criteria may include the requirement, 10 for a respective doubly differentiated integral ambiguity, that corresponding wide track ambiguities have been processed for at least a predefined length of time eg 4 to 6 hours) to provide a reliable basis for determining a variance of the respective doubly differentiated integral ambiguity and for having a high level of confidence in resolving the corresponding wide track ambiguities.
The process represented by the flowchart in Figures 6A-6B corresponds to operations 522 and 524 (Figure 5 A) of a doubly differentiated integral ambiguity resolution procedure (408) to identify doubly differentiated integral ambiguities that satisfy a set of predefined conditions and for adjust the computed state of the global navigation satellites by adding an integral restriction on the Kalman filter for each doubly differentiated integral ambiguity that satisfies the set of predefined conditions. Figures 6A-6B are described above.
In relation to Figure 7, in some embodiments, a satellite navigation receiver 25 (Figure 1, Figure 2) receives navigation signals from a plurality of global navigation satellites (702) and also receives correction signals (for example, correction signals 132) that correct orbital deviations from global navigation satellites (704). For example, the satellite navigation receiver 1.20 can receive correction signals 132 from computer system 130 via one or more communication satellites 160-1 through 160-P. Figure 7 is a flow chart of a method 700 for adjusting the status of the satellite navigation receiver 120 based on the correction signals received according to some modalities. The GNSS 218 module receives navigation signals from a plurality of global navigation satellites (702) and also receives (704) correction signals that compensate for orbital deviations from the plurality of global navigation satellites. Then, the GNSS 218 module incorporates the correction signals (706) in its. computation of the updated status of the satellite navigation receiver. For example, the GNSS 218 module can incorporate correction signals by reviewing measurements from the navigation satellite prior to its use in computing a satellite navigation receiver 120 update status, to compensate for various sources of error, such as orbital deviation of satellites from their predicted orbits (for example, predicted orbits using ephemeris data disseminated by navigation satellites; in some modalities, ephemeris data for navigation satellites are also available from the GNSS operations center (for example, GPS) or other busy service with timely distribution of ephemeris data), tropospheric effects and ionospheric effects.
The description presented, for the purpose of explanation, was carried out in relation to specific modalities. However, the illustrative discussions exposed are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the exposed precepts. The modalities were chosen and described in order to better explain the principles of the invention and their practical applications, thereby enabling others skilled in the art to make better use of the invention, and several modalities with various modifications are suitable for the particular use contemplated.

权利要求:
Claims (28)
[1]
1, Method implemented by computer, performed by a computer system that includes one or more processors and memory that stores one or more programs, the one or more processors to run
5 one or more programs, characterized by the fact that it comprises:
receiving, from a plurality of reference stations in known locations, a plurality of satellite navigation measurements of signals from a plurality of global navigation satellites;
compute a state of the plurality of navigation satellites
10 global based on satellite navigation measurements received;
identify a plurality of references, each reference corresponding to a pair of reference stations, and for each identified reference, compute fluctuating and integral values for a double-differentiated integral ambiguity corresponding to the identified reference;
1.5 identify, according to the floating and integral values computed for the doubly differentiated integral ambiguities corresponding to the plurality of identified references, a set of one or more doubly differentiated integral ambiguities that satisfy a set of predefined conditions; and
20 adjust the computed state of the plurality of global navigation satellites according to an integral value constraint applied to each integrally doubly differentiated ambiguity in the identified set of one or more doubly differentiated integral ambiguities that satisfy the set of predetermined conditions, to produce a state
25 computed adjusted of the plurality of global navigation satellites,
[2]
2. Method according to claim 1, characterized by the fact that it additionally includes:
compute correction information according to the adjusted computed state of the plurality of global navigation satellites; and transmitting correction information to a plurality of navigation receivers.
[3]
3. Method according to claim I, characterized by the fact that the correction information includes correction values for each
5 global navigation satellites in the plurality of global navigation satellites.
[4]
4. Method according to claim 1, used by the fact that the correction information includes correction values for two or more of the global navigation satellites in the plurality of satellites of
10 global navigation.
[5]
5. Method according to claim 1, characterized by the fetus that the set of predefined conditions, for a respective doubly differentiated integral ambiguity. It includes a requirement that a fractional difference between the integral and fluctuating values of the respective
15 doubly differentiated integral ambiguity does not exceed a first limit.
[6]
6. Method according to claim 5, characterized by the fact that it includes computing a variance and a standard deviation of the respective doubly differentiated integral ambiguity, in which the set of predefined conditions for the respective doubly integral ambiguity
20 differentiated includes a requirement that the standard deviation of the respective doubly differentiated integral ambiguity does not exceed a second limit.
[7]
7. Method according to claim 6, characterized by the fact that it includes computing a predefined W ratio for the respective doubly differentiated integral ambiguity, where the W ratio has a
25 numerator corresponding to the fractional difference between the integral and fluctuating values of the respective doubly differentiated integral ambiguity and a denominator corresponding to the standard deviation of the respective doubly differentiated integral ambiguity, and in which the set of predefined conditions for the respective doubly differentiated integral ambiguity a
it includes a requirement that the reason for the respective differentiated integral plan ambiguity is greater than a third limit.
[8]
8. Method according to claim 7, characterized by the fact that the first limit is not greater than 0.25 cycle and the -second limit is not greater than 0.1 cycle.
[9]
9. Method according to claim 1., characterized by the fact that the identified references include only mathematically independent references.
[10]
10. Method according to claim 1, characterized by the fact that the identification of the set of one or more doubly differentiated integral ambiguities that satisfy the set of predefined conditions includes filtering of the doubly differentiated integral ambiguities corresponding to the identified references to Prevent any ambiguities doubly differentiated integrals that no longer meet predefined filtering criteria are included in the identified set.
[11]
11. Method according to claim 1, characterized by the fact that the identification of the set of one or more doubly differentiated integral ambiguities that satisfy the set of predefined conditions and the adjustment of the computed state of the plurality of global navigation satellites comprises:
generate an ordered list of doubly differentiated integral ambiguities by ordering doubly differentiated integral ambiguities according to a first parameter computed for each, unia of the differentiated integral ambiguities corresponding to the identified referentials, select one. first doubly differentiated integral ambiguity in the ordered list and also select any other doubly differentiated integral ambiguities in the ordered list with a value of the first parameter equal to the first doubly differentiated integral ambiguity in the ordered list, determine which, if any, of the unia or more selected doubly differentiated integral ambiguities satisfy the set of predefined conditions to identify a set of fixed doubly differentiated integral ambiguities;
adjust the computed state of the. plurality of global navigation satellites according to the full value restriction applied, in each integral ambiguity doubly differentiated in the set of integral ambiguities doubly differentiated fixed;
remove fixed doubly differentiated integral ambiguities from the ordered list;
recomputing floating and integral values for one or more of the remaining doubly differentiated integral ambiguities in the ordered list;
selecting a next doubly differentiated integral ambiguity in the ordered list and also selecting any other doubly differentiated integral ambiguities in the ordered list with a value of the first parameter equal to said next doubly differentiated integral ambiguity in the ordered list; and repeat at least the determination and adjustment operations in relation to one or more selected doubly differentiated integral ambiguities.
[12]
12. Method according to claim 1, characterized by the fact that the method is repeated for each of a sequence of measurement times.
[13]
13. Method according to claim 1, characterized by the fact that the status of the plurality of global navigation satellites includes a position of each global navigation satellite in the plurality of global navigation satellites, a speed of each global navigation satellite in the plurality of global navigation satellites and a time reported by each global navigation satellite in the plurality of global navigation satellites.
[14]
14. Method according to claim 1, characterized by the fact that computing the status of the plurality of global navigation satellites comprises computing the status of the plurality of satellites
5 global navigation using a closed form update equation.
[15]
15. Computer system, characterized by the fact that it comprises:
one or more processors;
memory that stores one or more programs for execution by the one or more processors, the one or more programs comprising instructions that, when executed by the one or more processors, cause the computer system to:
receive, from a plurality of reference stations in known locations, a plurality of satellite navigation measurements 15 of the signals from a plurality of global navigation satellites;
compute a state of the plurality of global navigation satellites based on the received satellite navigation measurements;
identify a plurality of references, each reference corresponding to a pair of reference stations, and for each identified reference 20, compute floating and integral values for a doubly differentiated integral ambiguity corresponding to the identified reference;
identify, according to the floating and integral values computed for the doubly differentiated integral ambiguities corresponding to the plurality of identified references, a set of one or more doubly differentiated integral ambiguities that satisfy a set of predefined conditions; and adjust the computed state of the plurality of global navigation satellites according to an integral value constraint applied to each doubly differentiated integral ambiguity in the identified set of one or more doubly differentiated integral ambiguities that satisfy the set of predefined conditions, to produce a state computed value of the plurality of global navigation satellites,
[16]
16. Computer system according to claim 15, characterized by the fact that the one or more programs additionally comprise instructions that, when executed by one or more processors, cause the computer system to:
compute correction information according to the adjusted computed state of the plurality of global navigation satellites; and transmit the correction information to a plurality of navigation receivers,
[17]
17. Computer system according to claim 15, characterized by the fact that the correction information includes correction values for each of the global navigation satellites in the plurality of global navigation satellites.
[18]
18. Computer system according to claim 15, activated by the fact that the correction information includes correction values for two or more of the global navigation satellites, in the plurality of global navigation satellites,
[19]
19. Computer system according to claim 15, characterized by the fact that the set of predefined conditions, for a respective doubly differentiated integral ambiguity, includes a requirement that tuna, fractional difference between the integral and fluctuating values of the respective integral ambiguity doubly differentiated does not exceed a first limit,
[20]
20. Computer system according to claim 15, characterized by the fact that the one or more programs additionally comprise instructions that, when executed by one or more processors, cause the computer system to compute a variance and a standard deviation of the respective doubly differentiated integral ambiguity, in which the set of predefined conditions for the respective doubly differentiated integral ambiguity, includes a requirement that the standard deviation of the respective doubly differentiated integral ambiguity does not exceed a second limit.
5
[21]
21. Computer system according to claim 20, characterized in that the one or more programs additionally comprise instructions which, when executed by one or more processors, cause the computer system to compose a predefined W ratio for the respective doubly differentiated integral ambiguity 10, where the ratio W has a numerator corresponding to. fractional difference between the integral and fluctuating values of the respective doubly differentiated integral ambiguity and a denominator corresponding to the standard deviation of the respective doubly differentiated integral ambiguity, and in which the set of predefined conditions for the respective doubly differentiated integral ambiguity includes a requirement that the reason W for the respective ambiguity integrates! doubly differentiated is greater than a third limit.
[22]
22. Non-computer-readable storage media, characterized by the feeling that it stores one or more programs 20 configured for execution by a computer system, the one or more programs comprising instructions that, when executed by one or more processors, cause that the computer system;
receive, from a plurality of reference stations in known locations, a plurality of satellite navigation measurements 25 of the signals from a plurality of global navigation satellites ·, compute a state of the plurality of global navigation satellites based in the satellite navigation measurements received;
identify a plurality of references, each reference corresponding to a pair of reference stations, and for each identified reference, compute floating and integral values for a doubly differentiated integral ambiguity corresponding to the identified reference;
identify, according to the floating and integral values computed for the doubly differentiated integral ambiguities corresponding to the plurality of identified references, a set of one or more doubly differentiated integral ambiguities that satisfy a set of predefined conditions; and adjust the computed state of the plurality of global navigation satellites according to an integral value constraint applied to each doubly differentiated integral ambiguity in the identified set of one or more doubly differentiated integral ambiguities that satisfy the set of predefined conditions, to produce a state computed value of the plurality of global navigation satellites.
[23]
23. Non-temporary computer-readable storage media according to claim 22, characterized in that the one or more programs additionally comprise instructions that, when executed by one or more processors, cause the computer system to:
compute correction information according to the adjusted computed state of the plurality of global navigation satellites; and transmit the correction information to a plurality of navigation receivers.
[24]
24. Non-temporary computer-readable storage media according to claim 22. characterized by the fact that the correction information includes correction values for each of the global navigation satellites in the plurality of global navigation satellites.
[25]
25. Non-temporary computer-readable storage media according to claim 22, characterized by the fact that the correction information includes correction values for two or more of the global navigation satellites in the plurality of global navigation satellites
[26]
26. Non-temporary computer-readable storage media according to claim 2.2, characterized by the fact that the set of predefined conditions, for a respective integral ambiguity
5 doubly differentiated, includes a requirement that a fractional difference between the integral and fluctuating values of the respective doubly differentiated integral ambiguity does not exceed a first limit.
[27]
27. Non-temporary, computer-readable storage media according to claim 22, characterized by the fact that
10 the one or more programs additionally comprise instructions that, when executed by one or more processors, cause the computer system to compute a variance and standard deviation of the respective doubly differentiated integral ambiguity, in which the set of predefined conditions for the respective integral, doubly differentiated ambiguity includes a requirement that the standard deviation of the respective doubly differentiated integral ambiguity does not exceed a second limit.
[28]
28. Non-temporary computer-readable storage media according to claim 27, characterized in that the one or more programs additionally comprise instructions that, when executed by one or more processors, cause the computer system to compute a predefined W ratio for the respective integral ambiguity, doubly differentiated, in which the W ratio has a numerator corresponding to the fractional difference between the integral and fluctuating values of the respective doubly differentiated integral ambiguity and a denominator corresponding to the standard deviation of. respective doubly differentiated integral ambiguity, and in which the set of predefined conditions for the respective doubly differentiated integral ambiguity includes a requirement that the ratio W for the respective doubly differentiated integral ambiguity is greater than a third limit.

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法律状态:
2020-03-10| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|

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2020-03-31| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|

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2021-08-10| B06A| Patent application procedure suspended [chapter 6.1 patent gazette]|

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2021-10-13| B350| Update of information on the portal [chapter 15.35 patent gazette]|

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2021-12-21| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|

优先权:

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申请号 | 申请日 | 专利标题

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US201161432172P| true| 2011-01-12|2011-01-12|

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US61/432172|2011-01-12|

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US13/299,324|US8659474B2|2011-01-12|2011-11-17|Navigation system and method for resolving integer ambiguities using double difference ambiguity constraints|

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US13/299324|2011-11-17|

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PCT/US2011/066720|WO2012096773A1|2011-01-12|2011-12-22|Navigation system and method for resolving integer ambiguities using double difference ambiguity constraints|

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