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    • 专利摘要:
    The invention relates to a method of satellite-based satellite separation, each transmitting bi-frequency signals. The method comprises, for each satellite, a step of calculating four pseudo-distances from the two codes and the two carriers of the two-frequency received signals received, a step of correcting the ionospheric delays on each pseudo-distance calculated by applying an ionospheric error propagation model; a carrier code smoothing step using a Kalman filter to provide a pseudo-distance measurement without measurement noise and to correct the ionospheric error residue. The position is estimated using the corrected pseudo-distances calculated for each satellite.
    公开号:FR3049354A1
    申请号:FR1600503
    申请日:2016-03-25
    公开日:2017-09-29
    发明作者:Nicolas Martin；Denis Bouvet；Herve Guichon
    申请人:Thales SA；
    IPC主号:
    专利说明:

    SATELLITE GEOPOSITIONING METHOD AND ASSOCIATED TERMINAL
    The present invention relates to the field of satellite positioning. The invention more particularly relates to a satellite-based method and an associated terminal.
    A satellite positioning system (or GNSS for Global Navigation Satellite System ®) uses a constellation of satellites that revolve around the Earth in very precisely determined orbits, that is, for which it is possible to know at all times the position. The orbits of the satellites are chosen so that at any given time, six to twelve satellites are visible everywhere on the Earth. Each satellite, called bi-frequency, emits electromagnetic signals of geopositioning on two different frequencies (for example, Li = 1 575.42 MHz and Î2 = 1 227.6 MHz for the GPS system and Li = 1 575.42 MHz and Es = 1,227.6 MHz for the GALILEO system).
    A GNSS receiver receives signals from visible satellites and measures the propagation time required for a time stamp transmitted by a satellite to reach it. Time marks are encoded on carrier waves by the phase modulation technique. Each satellite transmits a pseudo-random code of its own. A replica of the code sequence is generated by the receiver and the offset that the replica must undergo in order to coincide with the received code corresponds to the duration of propagation of the signal to traverse the satellite-receiver distance. This duration multiplied by the speed of light in the medium traversed gives a measure of distance called pseudo-distance. From the measurements of the pseudo-distances separating it from each visible satellite, and from the knowledge of the position of the satellites, the receiver deduces its precise position in latitude, longitude, and altitude in a terrestrial reference by a numerical resolution close to the triangulation. It can also deduce the exact date and time in the time reference system of the GNSS system.
    The time reference of the receiver, provided by its clock, does not coincide perfectly with the time reference of the satellites of the constellation, which induces a bias in the measurements of propagation time, therefore of distance, equal to the delay of the reference receiver time relative to the satellite time reference. The term "pseudo-distance" is used for this purpose. The time bias, common to all measurements, constitutes a fourth unknown, in addition to the three unknowns of position, which requires at least four measurements to calculate the position.
    In addition, the position of the receiving terminal is estimated by making a number of approximations. The measurement of the pseudo-distance can not for example, overcome the errors related to the system such as the lack of precision ephemeris or clocks embedded in satellites. The measurement of the pseudo-distance is also tainted by errors related to the interactions between the signals and the atmospheric layers (troposphere and ionosphere) that they pass through. The propagation delay of the signal in the troposphere and the ionosphere depends on the inclination of the path and the time at which it takes place. Typically, GNSS positioning errors related to the atmosphere are more pronounced by day than by night and more sensitive when a satellite is near the horizon than at the zenith. In some applications, such as the precision approach in aeronautics, the positioning accuracy obtained by a direct or absolute measurement of the pseudo-distance is not sufficient. The use of a differential measurement substantially improves the positioning accuracy. It consists of transmitting via a dedicated channel (VHF, UHF or cellular telephony) corrections of the pseudo-distance measurements developed from measurements of pseudoranges originating from receivers arranged in ground stations and whose positions are known very precisely and closely of the onboard receiver. The measurement of the pseudo-distance separating a receiver on the ground and a satellite is compared with the theoretical distance separating these two devices. The theoretical distance is calculated from the respective spatial coordinates of the ground receiver and the satellite which are known at all times. The difference between the distance measurement and the theoretical distance represents the measurement error, it is calculated for each satellite at each observation period. These differences in distances constitute correction terms (also called differential corrections) which are subtracted from the pseudo-distance measurements made by the mobile receiver. These corrections have the effect of virtually eliminating the errors that have a significant spatial correlation regardless of their origin, system or atmospheric. The corrections are all the more effective as the two receivers are close. However, the differential measurement does not eliminate errors related to signal reflections on objects near the receiver antenna, nor errors specific to the receiver (thermal noise). These errors are present on the reference receiver as well as on the onboard receiver, they degrade the positioning measurement during the differential correction; the precision obtained is of the order of a few meters.
    To improve the accuracy of positioning, ground receivers and mobile receivers, embedded on carriers, can also benefit from a second information developed by the receiver which is the measurement of the phase of the carrier, for each received satellite signal . Measurement of the instantaneous phase of the received carrier makes it possible to calculate a pseudo-distance, called pseudo-carrier distance, between the receiver and the satellite, in the same way as the measurement of the instantaneous phase of the pseudo-random code. This pseudo-carrier distance undergoes the same variations as the pseudo-code distance, when the distance between the receiver and the satellite or the time bias due to the receiver clock vary. This pseudo-distance measured by the phase is a priori ambiguous since the phase is known module 2π but it is much less noisy than the pseudo-code distance measurements.
    A known solution to improve the pseudo-distance measurements consists of smoothing the noisy pseudo-distance measurement carried out on the code by the noisy phase measurements. For this, the receiver applies a low-pass filter, generally of the first order, to the difference between the pseudo-distance code and carrier pseudo-distance measurements, and then adds this filtered difference to the pseudo-distance measurement of carrier to restore the code phase measurement. This treatment is carried out satellite axis by satellite axis. If the measurement is differential, an identical smoothing is applied on the receivers of the ground station so that the error of the filter passing low, due to the divergence between the code and the carrier related to the fluctuations of the ionospheric delay, is identical to the ground and on the mobile receiver, and does not disturb the positioning measurement after the application of the correction.
    The main advantage of the axis-axis smoothing method lies in its simplicity and in the absence of a coupling effect between the measurements of the pseudo-distances of the different satellites or channels, nevertheless it is not completely satisfactory. Indeed, the gain on the accuracy of the measurement is significant only when the smoothing is performed with a long time constant; and in this case, the reset time to recover the accuracy after a sudden modification of all available measurements, is also long, for example when a satellite disappears by masking, a failure of a satellite or a failure of a receiver on the ground in the case of differential GNSS.
    In the presence of ionospheric scintillation, particularly near the equator, the carrier phase measurements on the signals emitted by the satellites have discontinuities. Indeed, in the ionosphere, the solar radiation causes a partial ionization of the atoms and molecules of gas. We are thus in the presence of charged particles (ions and free electrons) which are likely to interact with an incident electromagnetic wave and to influence its propagation. Ionospheric scintillation is manifested as more or less localized events, typically lasting a few hours and affecting Earth-satellite links. During a scintillation event, due to the presence of ionized particles in the ionosphere, the signal received on Earth, has amplitude and phase fluctuations that can strongly disrupt the link and compromise its operation. Due to fluctuations in the amplitude of the signals received by the terminal of geopositioning, the tracking loops which ensure the demodulation of the signal drop off when the amplitude is too low. Thus, the terminal can no longer be based on the continuity of the phase to perform the carrier code smoothing. Whenever the loop picks up the carrier code smoothing filter must be reset and we must wait for this filter to converge again. For example, it takes at least a second to re-hook the signal and find a valid pseudo-distance measurement. These ionospheric scintillations thus cause a regular degradation of the accuracy.
    In the case of a standard receiver this phenomenon may not be inconvenient because the satellites of geopositioning do not all drop at the same time, and if the stall times are short, the receiver always has at least four measurements available.
    In the case of an aeronautical receiver each stall is problematic because it is necessary to reset the carrier code smoothing filter and wait for the filter to converge again. During this convergence time, which is typically about one hundred seconds, the satellite measurement is not available to calculate the point, or very degraded. Knowing that the average duration between two stalls is ten seconds, we are left with satellite measurements still unavailable. With current aeronautical receivers the positioning becomes unavailable in the presence of strong ionospheric scintillations.
    Receptors designed to resist ionospheric scintillation are known in the prior art by means of narrow tracking loop strips. It works for fixed receivers, with stable clocks, but not for aeronautical receivers.
    An object of the invention is in particular to correct all or part of the disadvantages of the prior art by proposing a solution allowing a receiver of geopositioning to ensure the continuity of its positioning even in the presence of ionospheric scintillation. For this purpose, the subject of the invention is a satellite-based method implemented by a tolling terminal from at least four satellites each emitting electromagnetic signals of "bi-frequency", on two different frequencies, each of said signals being formed of a carrier frequency modulated by a spreading code, said terminal comprising at least one receiving module configured to receive the electromagnetic signals from each satellite on two different frequencies and at least one calculation module configured to processing said signals, said method comprising, for each satellite of geopositioning, a step of calculating, in nominal mode, four pseudoranges from the two codes and the two carriers of the bi-frequency reception signals received, a correction step ionospheric delays on each pseudo-cal distance abutment by applying an ionospheric error propagation model, - a carrier code smoothing step using a Kalman filter to provide a pseudo-distance measurement without measurement noise and to correct the residue of ionospheric error, the position of the terminal of geopositioning being estimated by using the pseudo-corrected distances calculated for each satellite.
    According to one embodiment, the method comprises a tropospheric delay correction step.
    According to one embodiment, the method comprises a step of correcting system errors.
    According to one embodiment, the method comprises a step of compensating an inter-frequency bias between the two-frequency signals.
    According to one embodiment, the Kalman filter is configured to propagate a state vector comprising the value of the pseudo-distance without measurement noise, the value of the ionospheric error on a frequency after correction by the model of propagation of the ionospheric error and the values of the floating ambiguities of the carrier phase measurements on each of the two frequencies.
    According to one embodiment, the Kalman filter is configured to propagate a state vector further comprising the value of the interfrequency bias between the two-frequency signals.
    According to one embodiment, the propagation model of the ionospheric error is a Kobuchar model.
    According to one mode of implementation, the propagation model of the ionospheric error is a Nequick model.
    According to one embodiment, the method comprises a step of storing the ionospheric error. The subject of the invention is also a satellite-based terminal comprising at least one receiving module configured to receive two-frequency geomagnetic reception signals transmitted by at least four satellites on two different frequencies and at least one module. calculation configured to process said signals of geopositioning and implement the above described desegregation process. Other features and advantages of the present invention will appear more clearly on reading the following description, given for illustrative and non-limiting purposes, and with reference to the accompanying drawings, in which:
    FIG. 1 represents an example of a block diagram of a satellite geopositioning method according to the invention;
    FIG. 2 represents an example of an operating flow diagram of a receiver Kalman filter according to the invention.
    Subsequently it will be assumed that each sateilite of geopositioning emits electromagnetic signals of geopositioning on two different frequencies. We will talk about two-frequency signals and we will note these two frequencies. Fa and Fb.
    It will also be assumed that there is never a stall of the two carriers at the same time, (or for a very short period of time), and that the terminal of geopositioning receives at least one of the two signals of geopositioning.
    FIG. 1 represents an example of a synoptic diagram, for each satellite axis, of a satellite optimization method according to a preferred embodiment of the invention.
    This method is implemented by a receiver or terminal of geopositioning from electromagnetic signals of geopositioning issued by at least four satellites of geopositioning. As stated above, each of these two-frequency signals is formed of a carrier frequency modulated by a spreading code.
    The method comprises, for each visible satellite, a first step Etp1 for measuring pseudo-distances. For this, the terminal of geopositioning comprises at least one receiving module configured to receive these electromagnetic signals from each satellite on two different frequencies.
    In known manner, this receiving module can comprise at least one antenna, an analog circuit performing amplifications and frequency changes, an analog-digital converter and at least N digital processing channels. Each channel being assigned to a satellite, the integer N will be chosen greater than the number of satellites from which it is desired to receive signals of geopositioning.
    Each digital channel receives a digitized signal containing all the satellite signals that it submits to a double servo loop, on the one hand synchronizing in phase a locally generated carrier with the carrier coming from the satellite in question and secondly to synchronize a pseudo-random code generated locally with an identical code present in the satellite signal and specific to this satellite.
    The two servo loops may each comprise two oscillators whose content represents for the first oscillator, the instantaneous phase of the local pseudo-random code (aligned with the code present in the signal), which constitutes the measurement of the instantaneous phase of the code received and for the second oscillator, the instantaneous phase of the local carrier (aligned with the phase of the carrier present in the signal received from the satellite, with the phase shift introduced by the receiver circuits), which constitutes the measurement of the instantaneous phase of the the carrier received.
    The measurement of the instantaneous phase of the code in each channel is used to calculate a first digital datum called PDcode pseudodistance representing a first measurement of the pseudorange between the receiver and the satellite in question. This measure is unambiguous but quite noisy.
    The measurement of the instantaneous phase of the carrier in the channel in question is used to calculate a second digital datum, called pseudorange PD carrier carrier, representing a second measurement of the pseudo-distance between the receiver and the satellite in question. This measure is not very noisy but ambiguous.
    Since the signals are bi-frequency, in nominal mode, two code pseudo-resistors and two carrier pseudo-distances can be calculated for each visible satellite of the receiver. Thus, in each digital channel, there are four independent measurements (PDcode a PDcode b .B carrier carrier b) of the same pseudo-distance separating the receiver from each visible satellite of the latter. For each satellite, the two types of pseudorange measurements (PDcode and carrierDB) are obtained in the form of signal propagation times between the satellite considered and the receiver along the axis (satellite axis) joining the satellite considered and the receiver. At time n, the pseudo-distances PDcode a (n), PDcode b (n) and carrierPN (n), carrierPB (n), respectively measured on the code and the carrier at the frequency Fa and the frequency Fb , are given by the formulas: PDcode a (π) - (Receiving (n) - Tsata (n)) PDcode b (n) = (Receiving (n) - Tsatb (n)) PD carrier a (Γ ·) - (Receiving (n) - φ carrier a (n) / Fa) B carrier (π) = (Receipt (n) - 9 carrier b (n) / Fb)
    In which ; - Treception (n) represents the date, at the moment n taken of measurement, given by the clock of the receiver. - Tsata (n) represents the date of transmission, by the satellite, of the signal received at time n by the receiver, given by the local code phase, - 9report a (n) represents the phase of the local carrier, reduced in carrier frequency (cycles).
    For each visible satellite of the receiver of geopositioning, the method of geopositioning comprises a step Etp2 of correction of the ionospheric delays on each pseudo-distance calculated by applying a propagation model of the ionospheric error. For this, the terminal also comprises at least one calculation module configured to process the received signals.
    The propagation model of the ionospheric error can be a Kobuchar model, a Nequick model or any other equivalent model known to those skilled in the art.
    The ionospheric error propagation model provides an estimate of the ionospheric error on the pseudo-code distance measurement of a frequency, for example the frequency Fa, as a function of the on-land position and the time in the the day. This ionospheric error represents the group delay induced on the propagation of the signal during the crossing of the ionosphere. This delay is proportional to the total electronic content of the atmosphere column traversed by the signal and inversely proportional to the square of the carrier frequency. The delay is found on the code measure. The effect on the carrier is a phase advance of the same absolute value.
    If we consider the ionospheric error on the frequency Fa; on the carrier measurement Fa, we have a phase advance of - Biono on the carrier measurement Fb, we have a phase advance of -γ.Β, οηο. on the measure of code Fa, we have a delay of Β, οηο = a. CET / Fa ^ and on the measure of code Fb, we have a delay of γ.Β, οηο = a CET / Fb ^ with γ = Fa ^ / Fb ^
    So to correct the carrier code and phase measurements the following corrections are applied:
    On PDcode a (π) 1 Bjono model
    On PD carrier a (n) - * "Bjono model On PDcode b (n)." Γ Bjono model
    On PD carrier b (π) - + γ Bjono model
    Of course, a similar resonance can be made by considering a model providing an estimate of the ionospheric error on the frequency Fb.
    In order to process the signals emitted by the satellites of geopositioning at the two frequencies Fa and Fb, the reception module comprises two distinct analog channels. When this receiving module receives two signals of different frequencies, the propagation times in each of the analog channels of the module may be different. This has the effect of introducing group delay differences between the two frequencies Fa and Fb, called RF bias.
    When the measurements are homogeneous, that is to say all frequencies on the same frequency or all bi-frequencies, RF bias, common to all satellites, have no effect on the resolved position. By cons when mixing single-frequency measurements on different frequencies or single-frequency and dual frequencies are introduced distortions between the satellite measurements so an error on the resolved position. This case may occur for example when one of the two bi-frequency signals is not received by the terminal of, for example, due to ionospheric scintillation problems.
    In order to avoid this, for each visible satellite, the method may comprise a step Etp3 of compensating an inter-frequency bias between the two-frequency signals.
    During this step, the calculation module identifies the RF bias difference between the two frequencies on the two-frequency signal measurements and corrects this difference on all the satellites by compensating the difference between the two frequencies only on the measurements. code and only on one of the two frequencies. The identification of the RF bias and its correction can be implemented using the techniques known to those skilled in the art, in particular that developed in the patent application FR 2 943 868. The RF bias difference can be estimated by making the average of all bi-frequency signal measurements available from the beginning (average in time and on satellites) and considering that the RF kisses are constant over time.
    The bl-frequency code measurements of each satellite are then smoothed by the bi-frequency carrier measurements during an etp4 step of smoothing the pseudo-distances compensated using a Kalman filter. During this step, the Kalman filter simultaneously performs carrier code smoothing and correction of the ionospheric error residue.
    Referring to Figure 2, we recall the operating principle of Kalman filtering.
    Kalman filtering uses a state model, established on the basis of the knowledge (actual or expected) of the behavior of the unknown physical quantities that are sought to be determined and available measurements.
    This state model consists of: a state vector Xn representing the physical quantities modeled at time tn, comprising a number of state components of a propagation model, of the form:
    Xn + 1 "F | T Xn Vn
    In which :
    Xn + 1 is the state vector at time tn + i
    Fn is the propagation matrix over the interval [tn, tn + i], of dimension N state x Nate (in which the operator * x "represents the multiplied sign).
    Vn is the noise vector of propagation on the interval [tn, tn + i], white, Gaussian, of zero mean, of covariance matrix Qn = E [Vn ^. Vn] (where Vn ^ is the transposed vector of Vn) and the state dimension · - of an observation model, of the form:
    Zn = Hn.Xn + Wn
    In which :
    Zn is the observation vector at time tn, of dimension Nobs Hn is the observation matrix at time tn, of dimension Nobs x Nstate
    Wn is the measurement noise vector at time tn, white, Gaussian, of zero mean, covariance matrix Rn = E [Wn ^. Wn] (where Wn ^ is the transposed vector of Wn) and of size Nobs.
    In this state model, the state vector Xn is of unknown prior value. It is not directly accessible by measurement, unlike the observation vector Zn, but only through the observation model.
    The Kalman filter realizes the estimation of the state vector by a computation of propagation, starting from the propagation model, and by a calculation of registration, from the observations and the model of observation.
    For this purpose, the filter uses two variables: the estimated state vector, denoted Xn / n after resetting at time tn, denoted by Xn + 1 / n after propagation between times tn and tn + i, and denoted Xn + i / n + i after resetting at time tn + i, of dimension N · state - the covariance matrix of the estimated state, denoted Pn / n after the registration at time t ", denoted by Pn + i / n after the propagation between instants tn and tn + i, and noted Pn + i / n + i after resetting at instant tn + i, of dimension Nétat x Nétat
    To perform the propagation calculation, the filter uses the formulas: - For the estimated state vector: Χπ + 1 / η "Fn-Xn / n
    The propagation matrix Fn serving to establish a linear relationship between the state vector before propagation and after propagation. - For the covariance matrix:
    Pn + 1 / n "Fn. Pn / n Fp + Qn (in which Fn ^ represents the transposed matrix of Fn)
    In this formula, the coefficients of the covariance matrix Ρ "represent the variance of each of the components of the estimated state vector (diagonal terms) and the covariance of the different pairs of components of this vector (non-diagonal terms). This matrix Pn represents the degree of confidence that is attributed to the estimated state vector.
    The covariance matrix Qn of the propagation noise makes it possible to quantify the random part and the approximations made in the propagation model for each component of the state vector Xn / n-
    To perform the registration calculation, the filter uses the formulas: - For the registration gain: ^ n + 1 ~ Pn + 1 / n ^ n + 1 (n + 1 ^ n + l / n ^ n + 1 ^ n +1) (where Hn + / is the transposed matrix of Hn + i)
    The observation matrix Hn serves to establish a linear relationship between the state vector and the measurements
    The covariance matrix Rn characterizes the noise of the measurements.
    Rn is a square matrix of dimension Nobs x Nobs- - For the estimated state vector:
    Xn + 1 / n + 1 "Xn + 1 / n ^ Kn + 1 · (Zn + 1Hn + 1 · Xn + 1 / n) - For the covariance matrix;
    Pn + 1 / n + 1 "(I ^ Nate" Kn + 1 Hn + 1) · Pn + 1 / π (in which Id Nate represents the state-state identity matrix Nate-State)
    Initially, the state vector Xo is initialized to zero and the matrix Po is initialized with the variances and covariances representative of the uncertainty on the physical quantities modeled in the state vector.
    The propagation computation uses the matrices Fn and Qn to determine the estimated propagated state vector Xn + i / n from the estimated estimated state vector Xnm and the propagated covariance matrix Pn + i / n from the rectified covariance matrix Pn / n.
    The recalculation calculation uses the observations Zn + i from the measurements and the matrices Hn + i and Rn + i, to determine the estimated estimated state vector Xn + i / n + i from the estimated state vector propagated Xn + i / n and the recalibrated covariance matrix Pn + i / n + i from the propagated covariance matrix Pn / n- The index n is then incremented by 1 (ie n = n + 1) and the propagation , then the registration is repeated. This is followed by an alternation of propogation, registration and incrementation of the index.
    In Figure 2, there is shown a memory collecting Xn + i / n + i and providing Xn / n to indicate that the state vector value introduced into the propagation model for calculating Xn + i / n dated the moment tn + i is the value of the state vector Xn / n which has been calculated at the previous instant tn- Similarly, a memory is represented for the covariance matrix Pn / n and for the estimated position Gn /not-
    In the carrier code smoothing filter according to the preferred embodiment of the invention, the state vector X and the evolution model represented by the matrices F and Q are as follows:
    In which: PD represents the pseudo-distance without receiving measurement noise (thermal noise, interference, multipath) and without ionospheric error; PD = receiver satellite distance + receiver clock bias x c + tropospheric error + satellite bias - Biono represents the ionospheric error on a frequency (for example on the frequency Fa) after correction by the model (residue); δa represents the floating ambiguity of the carrier phase measurement on the frequency Fa; δb represents the floating ambiguity of the carrier phase measurement on the frequency Fb; Brf represents the inter-frequency bias of the analog channel of the frequency Fb by supplying the analog channel of the frequency Fa; - λίοηο represents the attenuation factor of the Markov model of the ionospheric error.
    - ΔΤ representing the filter resetting period and τ the Markov model time constant (of the 1st order) of the ionospheric error after correction by the model. We take ΔΤ = 1s and τ = 2000 s - qpD represents the state noise on the pseudo-distance true. We will choose a very large value for this component because we do not have a reliable evolution model. For example qpo = (1000 m);; - qiono represents the state noise of the Markov model (of the ® · "order) of the ionospheric error after correction by the model;
    σ, οηο representing the standard deviation of the ionospheric error after correction by the model. Oiono = (20 m) ^ - qa represents the state noise of the ambiguity of the carrier phase measurement on the frequency Fa. We will take qa = (10 '^ m) ^ - qb represents the noise of state of ambiguity of the carrier phase measurement on the frequency Fb. We will take qb = (10 '^ m) ^
    According to an alternative implementation mode, the state vector may comprise four states. In this case, the matrices F and Q of the evolution model are also of dimension four. The state vector X and the evolution model become:
    In the absence of a cycle jump of the carrier phase loop, the state noise on the ambiguities is zero. To take into account the risk of cycle hopping (nonlinear and non-Gaussian model), we limit the time constant of the smoothing filter, which naturally tends towards infinity. Indeed, if the time constant is too great the effect of the cycle jump on the filtered pseudorange lasts a long time, the time that the filter converges and recalibrates on the code measure (without jump). In case of repeated cycle jump, at low signal-to-noise ratio, the jumps will accumulate before the filter has time to converge on the code measurement, which can introduce an unacceptable measurement error. For example, five cycle breaks give 1 meter of error.
    To limit the time constant, a non-zero state noise value is used.
    The matrices representative of the measurements and the observation model are written:
    In which: - PDcode a (n) represents the pseudo-distance measured on the code of the frequency Fa, at the instant t (n) = η.ΔΤ - the carrier a (n) represents the pseudo-distance measured on the code of the frequency Fa, at the instant t (n) = η.ΔΤ - PDcode b (n) represents the pseudo-distance measured on the code of the frequency Fb, at the instant t (n) = η.ΔΤ - PD carrier b (n) represents the pseudo-distance measured on the code of the frequency Fb, at time t (n) = η.ΔΤ - CTcode a and acode b respectively represent the standard deviation of the error of measurement of the receiver specific code phase for the frequencies a and b. - Carrier a and Carrier b respectively represent the standard deviation of the measurement error of the carrier phase specific to the receiver for the frequencies a and b.
    During initialization, the state vector X and the covariance matrix P have the value:
    Oiono represents the standard deviation of the ionospheric error on the sight axis of the satellite considered. Its value is given by models of the atmosphere, depending on the inclination of the satellite axis, the latitude of the receiver, and the time of day. We take Ojono = 30 m. σΐοηο represents the standard deviation of the error on the inter-frequency bias on the visual axis of the satellite in question. We take orf = 3 m.
    When a satellite picks up on a frequency, that is to say that one of two bi-frequency signals transmitted by a satellite of geopositioning is not received by the terminal or is received with an amplitude too small, the model observation is modified. The two lines corresponding to these measurements in matrices Z, R and H are deleted.
    If, for example, on a satellite axis, the measurements on the frequency Fb become unavailable and only the measurements on the frequency Fa are available, the matrices representative of the observation model become:
    Similarly, when the measurements on the frequency Fa become unavailable, the observation model is written:
    When the measurements become available, the two lines associated with these measurements in matrices Z, R and H are restored. In addition, for the reappeared frequency, the estimate of the ambiguity of the phase measurement of the state vector X is reset to 0 before the resetting on the 4 measurements. The diagonal coefficient (variance) associated with the carrier phase ambiguity in the matrix P is reset to a very large value in front of the orders of magnitude commonly used. This value is artificially increased to indicate to the filter that the estimate of the ambiguity is no longer good, and that it must therefore be reset. This variance can be set to a value of 1000 ^
    When the measurements on the frequency Fa become available again, the state vector X and the covariance matrix P are written:
    Similarly, when the measurements on frequency Fb become available once again, we obtain:
    When Γοη observes a discontinuity on the carrier phase measurement of one of the two frequencies, the state vector and the covariance matrix are reset in the same way before resetting to the new carrier phase measurement.
    The Kalman filter makes it possible to carry out both the carrier code smoothing and the "iono-free" combination. At the output of the carrier code smoothing, the useful datum is the filtered and corrected pseudo-distance PD of the ionospheric error, corresponding to the first coordinate of the estimated state vector after resetting Xn + i / n + i-
    Advantageously, since the corrections of the model are applied upstream of the Kalman filter, the latter only has to identify the residue of the ionospheric error. The error computed by the model obeys the same Markov model as the ionospheric error, so a fortiori the difference too, with a smaller amplitude. Since the correction itself is proportional to the inverse of the square of the frequency, the residual error is too, so the observation model linking the state of the filter to the two-frequency measurements remains unchanged.
    The Kobuchar or Nequick models are ionospheric error prediction models that are normally used to correct single-frequency measurements. In the case of bi-frequency measurements, the fact of combining ion-free signals on two different frequencies normally eliminates the ionospheric error. In the method according to the invention, this model is useful when one of the two frequencies is no longer available and the measurement is found single-frequency.
    When one of the two frequencies is no longer available over a long period of time, for example as a result of jamming or an amplitude that is too low, a change from a dual-frequency operation to a single-frequency operation is carried out. At first, the filter will function naturally on the bi-frequency operation acquired with the ionospheric bias estimated during the presence of the bi-frequency signals and little by little this estimate of the bias will expire because this bias has changed. The fact of applying a correction of the ionospheric error upstream of the Kalman filter, makes that the process will pass gradually from a combination * iono free '' on two frequencies to an operation equivalent to a mono-frequency case but corrected thanks to the prediction model of the ionospheric error.
    A tropospheric delay correction step Etp5 can then be applied to the pseudo-distance measurement at the output of the Kalman filter for each satellite. This compensation is obtained by applying a conventional model depending inter alia on the time of day, the geographical position of the satellite in question.
    An etp6 error correction step * system * can then be applied for each satellite axis. These errors are related to the principle of GNSS. This is for example the Sagnac effect of the time shift of the reception of two signals rotating in the opposite direction. Among these errors, there is also the inaccuracy of atomic clocks. During this step the relativistic effect is also corrected. These corrections to be made are given by the satellite considered through a * navigation message containing the corrective terms. These corrective terms are common to all four measurements from the same satellite.
    Once these corrections have been made, the calculation module estimates the position of the terminal of addition by combining the filtered and corrected pseudoranges calculated for each visible satellite of the terminal by means of the algorithm of resolution, PVT for * Position, Veiocity and Time * according to the English terminology. As previously stated, at least four pseudo-distances are required.
    According to an alternative embodiment, the tropospheric delay correction Etp5 and "system * Etp6" errors can be performed upstream of the Kalman filtering, provided that these corrections are applied to the four pseudo-distance measurements.
    The computing module may comprise one or more microprocessors, processors, computers or any other equivalent means programmed in a timely manner.
    权利要求:
    Claims (10)
    [1" id="c-fr-0001]
    1. Satellite separation method implemented by a terminal of geopositioning from at least four satellites each transmitting electromagnetic signals of geopositioning, said bi-frequencies, on two different frequencies, each of said signals being formed of a frequency a carrier modulated by a spreading code, said terminal comprising at least one receiving module configured to receive the electromagnetic signals from each satellite on two different frequencies and at least one calculation module configured to process said signals, iedit method being characterized in what it understands, for each satellite of geopositioning, - a step of computation (Etp1), in nominal regime, of four pseudo-distances from the two codes and the two carriers of the signals of bi-frequency reception received, - a step of correction (Etp2) ionospheric delays on each pseudo-dist calculated by applying an ionospheric error propagation model, - a carrier code smoothing step (Etp4) using a Kalman filter to provide a pseudo-distance measurement without measurement noise and to correct the ionospheric error residue, the position of the terminal of geopositioning being estimated using the pseudo-corrected distances calculated for each satellite.
    [2" id="c-fr-0002]
    2. Method according to the preceding claim wherein said method comprises a step (Etp5) tropospheric delay correction.
    [3" id="c-fr-0003]
    3. Method according to one of the preceding claims wherein said method comprises a step (Etp6) for correcting system errors.
    [4" id="c-fr-0004]
    4. Method according to one of the preceding claims wherein said method comprises a step of compensation (Etp3) of an interfrequency bias between the two-frequency signals, upstream of step (Etp4) smoothing carrier code.
    [5" id="c-fr-0005]
    5. Method according to one of the preceding claims wherein the Kalman filter is configured to propagate a state vector comprising the value of the pseudo-distance (PD) without measurement noise, the value of the ionospheric error (Biono) on a frequency after correction by the propagation model of the ionospheric error and the values of the floating ambiguities (δg, δb) of the carrier phase measurements on each of the two frequencies.
    [6" id="c-fr-0006]
    6. Method according to one of the preceding claims wherein the Kalman filter is configured to propagate a state vector further comprising the value of the inter-frequency bias (Brf) between the two-frequency signals.
    [7" id="c-fr-0007]
    7. Method according to one of the preceding claims, in which the propagation model of the ionospheric error is a Kobuchar model.
    [8" id="c-fr-0008]
    8. Method according to one of claims 1 to 6 wherein the propagation model of the ionospheric error is a model of Nequick.
    [9" id="c-fr-0009]
    9. Method according to one of the preceding claims wherein said method comprises a step of storing the ionospheric error.
    [10" id="c-fr-0010]
    10. Terminal of geopositioning characterized in that it comprises at least one receiving module configured to receive signals of electromagnetic separation, said bi-frequencies, emitted by at least four satellites on two different frequencies and at least one configured calculation module to process said signals of geopositioning and implement the method of geopositioning according to one of the preceding claims.
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    同族专利:
    公开号 | 公开日
    US20170276799A1|2017-09-28|
    FR3049354B1|2018-03-16|
    EP3223038A1|2017-09-27|
    US10670733B2|2020-06-02|
    EP3223038B1|2018-08-29|
    引用文献:
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    法律状态:
    2017-02-27| PLFP| Fee payment|Year of fee payment: 2 |
    2017-09-29| PLSC| Publication of the preliminary search report|Effective date: 20170929 |
    2018-02-27| PLFP| Fee payment|Year of fee payment: 3 |
    2019-03-05| PLFP| Fee payment|Year of fee payment: 4 |
    2020-12-18| ST| Notification of lapse|Effective date: 20201109 |
    优先权:
    申请号 | 申请日 | 专利标题
    FR1600503A|FR3049354B1|2016-03-25|2016-03-25|SATELLITE GEOPOSITIONING METHOD AND ASSOCIATED TERMINAL|
    FR1600503|2016-03-25|FR1600503A| FR3049354B1|2016-03-25|2016-03-25|SATELLITE GEOPOSITIONING METHOD AND ASSOCIATED TERMINAL|
    EP17158918.7A| EP3223038B1|2016-03-25|2017-03-02|Satellite geopositioning method and associated terminal|
    US15/465,151| US10670733B2|2016-03-25|2017-03-21|Satellite geopositioning method and associated terminal|
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