High-precision product conversion method between satellite navigation signals

Abstract

The present invention relates to a method for converting satellite navigation signals into high-precision products, comprising: using observation data of some frequency points of GNSS satellites from a wide-area monitoring station to determine the orbit of the GNSS satellite, performing real-time filtering and solving for clock error parameters, and solving for inter-code deviation parameters and phase deviation parameters; using observation data of some frequency points of GNSS satellites from the wide-area monitoring station to perform precise single-point positioning processing, and using a real-time filtering method to solve for ionospheric delay parameters and tropospheric delay parameters; using observation data of all frequency points of GNSS satellites from a local-area monitoring station to solve for pseudo-range frequency differences and phase frequency differences between some frequency points and all other frequency points; predicting the pseudo-range frequency differences and phase frequency differences to generate inter-code deviation parameters and phase deviation parameters for all frequency points in the entire arc segment of the GNSS satellite; broadcasting satellite orbit parameters, clock error parameters, delay parameters, and deviation parameters, and performing positioning and solving. The present invention can provide high-precision spatiotemporal services to GNSS users at different frequency points around the world.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation and positioning technology, and in particular to a method for converting satellite navigation signals into high-precision products. Background Art

[0002] Satellite navigation systems are essential space infrastructure that provide users with meter-level positioning, navigation, and timing services. With the advancement of society and technology, the demand for navigation and location-based services has exploded. Satellite navigation systems alone are no longer able to meet user requirements for real-time, accurate, ubiquitous, and reliable services. Satellite navigation augmentation (NASA) enhances and improves the performance of NASA through real-time monitoring of satellite navigation signals and error source correction, building upon NASA services to improve user navigation and positioning performance. Scientific organizations at home and abroad, such as the International Global Navigation Satellite System (GNSS) Service and the International GNSS Monitoring and Assessment Organization (IGNSS Monitoring and Assessment), have established globally distributed, wide-area ground monitoring stations to monitor downlinked GNSS satellite navigation signals. They have made their raw observation data, data processing strategies, and high-precision navigation satellite products freely available to the scientific community and the public. This has enabled the high-precision calculation of satellite orbit parameters, clock parameters, inter-symbol bias, and phase float bias, as well as the high-precision modeling of parameters such as ionospheric and tropospheric delays. However, these ground stations' monitoring of GNSS downlink signals often fails to cover all GNSS downlink navigation signals. For signals that are not monitored and difficult to provide high-precision products, users cannot use high-precision products to achieve high-precision navigation positioning when using them. Summary of the Invention

[0003] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a method for converting high-precision products between satellite navigation signals. Taking into account the differences between signals received by different ground stations, the method can realize the conversion of high-precision products between GNSS signals and provide high-precision space-time services to GNSS users at different frequencies around the world.

[0004] To achieve the above-mentioned object, the present invention provides a method for converting satellite navigation signals into high-precision products, comprising:

[0005] S1. Use observation data from wide-area monitoring stations on some of the GNSS satellite's frequencies to determine the GNSS satellite's orbit;

[0006] S2. Use the wide-area monitoring station to perform real-time filtering on the observation data of some GNSS satellite frequencies to obtain clock error parameters;

[0007] S3. Calculate the inter-symbol deviation parameters and phase deviation parameters using observation data of some frequency points of GNSS satellites from wide-area monitoring stations;

[0008] S4. Perform precise point positioning on observation data from selected GNSS satellite frequencies using wide-area monitoring stations, and calculate ionospheric and tropospheric delay parameters using real-time filtering.

[0009] S5. Calculate the pseudo-range frequency difference and phase frequency difference between some frequencies and all other frequencies using the observation data of all GNSS satellite frequencies from the local monitoring station;

[0010] S6. Predict the pseudo-range frequency difference and phase frequency difference to generate the inter-code deviation parameters and phase deviation parameters for all frequencies of the GNSS satellite full arc segment;

[0011] S7. Broadcast satellite orbit parameters, clock error parameters, delay parameters and deviation parameters and perform positioning calculations.

[0012] Beneficial effects:

[0013] According to the solution of the present invention, the method takes into account the differences between the signals received by different ground stations, and can realize the conversion of high-precision products between GNSS signals, and then realize the real-time solution of high-precision products of unmonitored signals, providing high-precision products for users corresponding to unmonitored signals, thereby improving their navigation and positioning performance, and providing high-precision space-time services to GNSS users at different frequency points around the world. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0015] Figure 1 A flowchart schematically illustrates a method for converting satellite navigation signals into high-precision products according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The description of the embodiments in this specification should be combined with the corresponding drawings, which should be considered a complete part of this specification. In the drawings, the shapes and thicknesses of the embodiments may be exaggerated and indicated for simplicity or convenience. Furthermore, the various structural components in the drawings will be described separately. It is worth noting that components not shown in the drawings or not described in words are known to those of ordinary skill in the art.

[0017] The description of the embodiments herein and any references to directions and orientations are for ease of description only and are not to be construed as limiting the scope of the present invention. The following description of the preferred embodiments may involve combinations of features, which may exist independently or in combination. The present invention is not specifically limited to the preferred embodiments. The scope of the present invention is defined by the claims.

[0018] An embodiment of the present invention discloses a method for converting satellite navigation signals into high-precision products. The method comprehensively utilizes observation data of some GNSS satellite frequencies by wide-area monitoring stations (such as global monitoring stations or large-area monitoring stations) and observation data of all GNSS satellite frequencies by local monitoring stations (referring to monitoring stations in a smaller area, where the distribution scale of monitoring stations is much smaller than the distribution range of wide-area monitoring stations) to calculate high-precision spatiotemporal service products for all GNSS satellite arcs and frequencies, thereby providing high-precision spatiotemporal services to GNSS users at different frequencies around the world.

[0019] According to the concept of the present invention, the method uses the observation data of some GNSS satellite frequency points by the wide-area monitoring station to calculate the common parameters of different frequency points such as high-precision satellite orbits, clock error parameters, ionospheric delay and tropospheric delay, as well as phase deviation parameters and inter-code deviation parameters of some GNSS satellite frequency points in real time according to the description of the following steps S1, S2, S3 and S4. Then, the observation data of all GNSS satellite frequency points by the local monitoring station is used to calculate the difference in inter-code deviation parameters and phase deviation parameters between other GNSS satellite frequency points and some frequency points and make a forecast, and finally generate the inter-code deviation parameters and phase deviation parameters of all frequency points of the GNSS satellite in the entire arc segment, and broadcast the above products to users of all frequency points to provide high-precision spatiotemporal information services. The partial frequency points described here refer to the navigation signals of some GNSS satellite frequency points that can be observed by the wide-area monitoring station. All frequency points refer to all navigation signals broadcast by GNSS satellites. Other frequency points refer to the navigation signals other than the partial frequency points of GNSS satellites that can be observed by the wide-area monitoring station. Other frequency points can only be observed by local monitoring stations within a small range.

[0020] Reference Figure 1 , the process of the above method specifically includes:

[0021] Step S1: Orbit determination of a GNSS satellite is performed using observation data of some frequency points of a GNSS satellite from a wide area monitoring station.

[0022] Furthermore, the observation data in step S1 is an ionospheric-free combination of pseudoranges and carrier phases for some frequencies of the GNSS satellite. In step S1, the observation data is used to perform statistical dynamic orbit determination and orbit prediction on the GNSS satellite, solving the GNSS satellite's initial orbit, dynamic parameters, and measurement parameters to obtain a precisely predicted GNSS satellite orbit.

[0023] Specifically, (1) the satellite broadcast ephemeris is used as the initial orbit parameter for satellite orbit determination processing, and the differential equation of the satellite state transfer matrix is ​​constructed.

[0024] Assume that there is a state vector representing the position and velocity of the satellite at time t:

[0025]

[0026] Satisfies the first-order differential equation

[0027]

[0028] So,

[0029]

[0030] Assume that the state transfer matrix is

[0031]

[0032] According to the above formula, we can get the differential equation of Φ(t,t0):

[0033]

[0034] The initial conditions are:

[0035] Φ(t0,t0)=I 3*3

[0036] (2) Using the satellite broadcast ephemeris as the initial orbit parameter for satellite orbit determination, calculate the differential equation of the satellite sensitivity matrix at time t

[0037] The sensitivity matrix refers to the satellite state vector to the dynamic parameter p i (i=1,…,n p )'s dependencies:

[0038]

[0039] The differential equation of the sensitivity matrix gives the partial derivative relationship of the state vector to the dynamic model parameters. The calculation process is similar, which is:

[0040]

[0041] or:

[0042]

[0043] Since the satellite state vector at time t0 is independent of the dynamic parameter P, the initial condition of the sensitivity matrix is

[0044] S(t0)=0

[0045] (3) According to the differential equations of the satellite state transfer matrix and the sensitivity matrix, the variational equation is constructed and the orbital integral is performed to obtain the approximate value of the satellite orbit at the current moment and its partial derivatives with respect to the satellite initial orbit parameters and perturbation acceleration parameters.

[0046] Combining the differential equations of the state transfer matrix and the sensitivity matrix gives the following form:

[0047]

[0048] The above equation is a first-order initial value problem and can be solved by numerical integration. If you want to use the direct integration method of the second-order differential equation to solve it, you need to transform the form of the variational equation. First, decompose Φ and S into:

[0049]

[0050]

[0051] because

[0052]

[0053] The variational equation can be written as:

[0054]

[0055] Since acceleration is independent of velocity, is zero, which means that the right side of the second-order variational equation is No. The calculation of satellite orbits can be done using numerical scoring methods (such as the Runge-Kutta integrator).

[0056] (4) Circularly process the available wide-area monitoring station pseudo-range phase ionosphere-free combined observation data, combine the satellite orbit approximation and partial derivatives obtained in step (3), construct the normal equation, and use the least squares estimation to calculate the satellite initial orbit parameters and dynamic parameter estimation results

[0057] In addition to the satellite initial orbit and dynamic parameters, the precise orbit determination of navigation satellites should also include the measurement parameters q i (i=1,…,n q The measurement parameters include the tropospheric delay scale factor, phase data ambiguity parameters, satellite and station clock errors, inter-satellite two-way ranging, and equipment delay of satellite-to-ground two-way clock error measurement results.

[0058] For the observation data L(t), it can be regarded as a nonlinear function of the satellite's state vector and measurement parameters, such as:

[0059] L(t)=G(y(t),q)

[0060] The state vector of the satellite can be obtained by numerical integration based on the satellite's initial orbit and dynamic parameters. The above formula can be obtained:

[0061] L(t)=G(y(t0),p,q)

[0062] There are reference initial values ​​y for the satellite initial orbit, dynamic parameters and measurement parameters respectively * (t0), p * and q * , linearizing the above equation, we can get:

[0063]

[0064] Where Δy(t0) is the initial orbit parameter of the satellite relative to y * where Δp is the correction of the kinetic parameter relative to p*, and Δq is the correction of the measurement parameter relative to q*.

[0065] make

[0066]

[0067] x=(Δy(t0) Δp Δq) T

[0068] l(t)=L(t)-G(y * (t0),p*,q*)

[0069] Then the above formula can be written as:

[0070] v(t)=B(t)·xl(t)

[0071] It can be seen that x is the parameter vector to be estimated for precise orbit determination, B is the coefficient matrix of the observation data L(t), and l(t) is the constant matrix of the observation data L(t).

[0072] For all observation data, the observation equation is:

[0073]

[0074] Among them, P i is the observed data 1(t i )’s weight.

[0075] In addition, considering the satellite initial orbit, dynamic parameters and measurement parameters reference initial value y * (t0), p * The weights of q* are:

[0076] v0=x-x0,P0

[0077] x0 is the initial value of the parameter to be estimated. The matrix is ​​constructed as follows:

[0078]

[0079]

[0080] Using the least squares principle, the optimal estimate of the parameter to be estimated is:

[0081]

[0082] Step S2: Using the wide-area monitoring station, the observation data of some frequency points of the GNSS satellite are filtered and calculated in real time to obtain clock error parameters.

[0083] Furthermore, the observation data in step S2 is an ionospheric-free combination of pseudoranges and carrier phases for some of the GNSS satellite's frequencies. Specifically, step S2 includes: step S21, based on step S1, fixing the GNSS satellite's precise predicted orbit, using the ionospheric-free combination of pseudoranges and carrier phases for some of the GNSS satellite's frequencies from the wide-area monitoring station as input, using Kalman filtering to solve the GNSS satellite clock error parameters epoch by epoch, and simultaneously estimating the ambiguity parameters of the carrier phase data. Step S22, extrapolating the GNSS satellite's precise orbit to generate a predicted orbit, while simultaneously performing gross error removal, cycle slip detection, and repair based on the undifferenced ionospheric-free combined observations. Then, by fixing the extrapolated predicted orbit, introducing common parameters such as the wide-area monitoring station coordinates and ERP, and adding various corrections such as tropospheric delay, station tidal correction, satellite antenna phase center deviation, relativistic periodic term, and relativistic gravitational delay, parameter estimation is performed until a set residual threshold is met, and the iteration is exited to obtain precise GNSS satellite clock error parameters.

[0084] Step S21 includes: Step S211, preprocessing the pseudorange and carrier phase data of the current wide-area monitoring station's receiver. The preprocessing includes two steps: eliminating gross errors and detecting cycle slips. The MW combination of the undifferenced dual-frequency pseudorange phase and the ionospheric residual combination observations are primarily used to eliminate gross errors and detect and repair cycle slips. While this method can detect most cycle slips and gross errors, some minor cycle slips may still exist. These cycle slips will be addressed through rigorous quality control methods in later stages.

[0085] Step S212: For the ionospheric-free combination of the pseudorange and carrier phase data of the wide-area monitoring station receiver obtained in step S211, Lagrangian interpolation is performed on the predicted orbit to obtain the GNSS satellite orbit at the satellite launch time, and then the satellite-to-ground distance is calculated in combination with the precise coordinates of the ground monitoring receiver to obtain a priori residual sequence.

[0086] In step S213, the observation data of all wide-area monitoring stations currently being used are processed in step S212 to obtain a coefficient matrix and a constant matrix, construct a normal equation matrix, and use Kalman filtering to estimate the clock error, atmosphere, and ambiguity parameters of the GNSS satellite in real time.

[0087] Step S3: Calculate the inter-symbol deviation parameter and the phase deviation parameter using the observation data of some frequency points of the GNSS satellites from the wide-area monitoring station.

[0088] Furthermore, the inter-code bias parameter solution process in step S3 includes: calculating the ionospheric residual combination of the pseudo-range data of the wide-area monitoring station for some frequency points of the GNSS satellite, separating the ionospheric delay from the code bias parameter by using ionospheric delay modeling, and obtaining a high-precision estimate of the inter-code bias parameter;

[0089] Specifically, the pseudorange observation equation of the GNSS satellite is shown as follows:

[0090]

[0091] Among them, P i is the pseudorange observation, is the geometric distance between receiver j and satellite k, δt j is the clock error of receiver j, δt k is the clock error of satellite k, Δt cor is the error corrected by the available model, d trop is the tropospheric delay, is the channel delay of the i-th frequency point of satellite k, τ i,j is the channel delay of the i-th frequency point of receiver j, TEC is the total electron content on the oblique path, f i is the carrier frequency of the ith frequency point, ε i is the measurement error including multipath, c is the speed of light;

[0092] According to the above formula, the difference between different frequency points can obtain a dual-frequency geometry-free combination:

[0093]

[0094] in, IFB j =τ 1,j -τ 2,j ;

[0095] According to the ionospheric map GIM published by IGS, the TEC in the above formula can be used as a known quantity to obtain the combined delay value of the GNSS satellite and receiver hardware:

[0096]

[0097] Based on a predetermined spherical harmonic model (either low-order or high-order), the TEC is considered to be a known quantity containing unknown ionospheric model parameters. A system of equations is established for the combined hardware delays obtained from all GNSS satellites and all receivers. The system has a rank deficiency of one, and appropriate constraints are required to make the equations full-rank.

[0098] Furthermore, the phase deviation parameter solution process in step S3 includes: using the carrier phase data ambiguity parameter obtained in step S2 as input, and calculating a high-precision phase deviation parameter by multi-station and multi-satellite joint solution;

[0099] Specifically, the ambiguity fixation on the system side is performed together with the orbit and clock error resolution. The input data for orbit determination calculation include the initial orbit, initial clock error, observation data from several globally distributed receivers, and the precise coordinates of the receivers. The undifferenced ionospheric-free combination is used as the basic observation quantity.

[0100] First, the floating-point solutions of satellite orbits and clock errors are calculated. This step does not perform ambiguity fixation, and the ambiguity parameter results are in real number form:

[0101] Fit the initial orbit to obtain the initial orbital elements and dynamic model parameters, perform orbital integration to obtain the reference orbit, state transfer matrix, and sensitivity matrix;

[0102] The observation data are preprocessed to remove obvious unreasonable gross errors, cycle slip detection and repair are performed on the phase observation data, and antenna phase center correction, antenna phase winding correction, tropospheric correction, relativistic correction, and tidal correction are performed on the observation data. The dual-frequency observation data are used to form pseudo-range ionosphere-free combined observation quantities and phase ionosphere-free combined observation quantities respectively.

[0103] Based on the reference orbit, initial clock error, station coordinates, and ionosphere-free combined observations, the observation equation is constructed to calculate the OC value, and then the parameters are estimated to obtain new orbit parameters and clock error parameters. After 5 to 7 iterations, gross errors are eliminated to obtain more accurate floating-point orbit and clock error results.

[0104] Then, the satellite orbit and clock error fixed solution are calculated, and the satellite UPD parameters are estimated at the same time: the floating point solution orbit is integrated to obtain a more accurate reference orbit, state transfer matrix and sensitivity matrix;

[0105] The ionospheric-free combined ambiguity obtained from the float solution is decomposed into wide-lane and narrow-lane ambiguities. Independent baselines are selected to form station-satellite double differences, making the ambiguities have integer characteristics. Since the wavelength of wide-lane ambiguities is 0.86m, they are easier to fix. Wide-lane ambiguities are calculated from MW combined observations. To reduce the effects of pseudorange noise and multipath, the wide-lane ambiguities are smoothed between epochs and fixed by rounding to the nearest integer. The narrow-lane ambiguities are searched and fixed using the LAMDA method.

[0106] The observation equation is constructed based on the floating-point orbit and clock solutions, station coordinates, and ionosphere-free combined observations. The OC value is calculated, and the double-difference integer ambiguity fixation results are used as constraints to perform parameter estimation to obtain new orbit parameters and clock error parameters. Through 3 to 5 iterations, the success rate of integer ambiguity fixation is improved, gross errors are eliminated, and the final fixed orbit and clock error solutions are obtained.

[0107] Finally, based on the ambiguity fixation results, the UPD parameters of the GNSS satellite and receiver are calculated. To solve the rank deficiency problem during the solution, a certain satellite or a certain receiver is selected as a reference benchmark, and its UPD parameter is considered to be 0, or the UPD mean of the entire constellation is selected as 0 as the benchmark; the obtained wide-lane and narrow-lane UPD parameters are converted to obtain the UPD parameter estimates corresponding to the two frequency points of the satellite.

[0108] In step S4, the precise predicted GNSS satellite orbit obtained in step S1, the precise GNSS clock error obtained in step S2, and the inter-code deviation parameter and phase deviation parameter obtained in step S3 are fixed, and the observation data of some frequency points of the GNSS satellite are subjected to precise single-point positioning processing using the wide-area monitoring station. The ionospheric delay parameter and the tropospheric delay parameter are calculated using the real-time filtering method.

[0109] Furthermore, the observation data in step S4 is a multi-frequency undifferenced undifferenced combination of pseudorange and carrier phase data of some frequency points of the GNSS satellites obtained by the wide-area monitoring station.

[0110] After the solution is completed in step S4, the ionospheric delay parameters and the tropospheric delay parameters are gridded in real time to generate the ionospheric delay model background field and the tropospheric delay model background field, the gridded residual ionospheric slant path delay and the tropospheric delay.

[0111] Specifically, by correcting various errors in the original pseudorange and carrier observations, taking the precise orbit and clock errors as known values ​​and linearizing the equations, the pseudorange and carrier observation equations of the non-combined PPP of any satellite navigation system can be simplified to

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] Where, and is the pseudorange and carrier observation after adding various error corrections, γ i =f1 2 / f i 2 , is the ionospheric delay at the L1 frequency point, including the hardware delay (DCB) at the receiver and satellite ends, where: are the pseudorange hardware delay bias at the receiver and satellite ends, respectively.

[0118] When performing non-differential non-combined positioning, the processing method is consistent with that when eliminating the ionospheric combination, and the receiver clock error of other systems is expressed as the form of receiver clock error plus system time deviation. The non-combined PPP pseudorange and carrier equation of satellite navigation system s can be further expressed as

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] In the above formula, the system time bias parameter is consistent with the ISB parameter in the ionospheric elimination combination model. At the same time, the above formula provides a unified summary of the non-combined PPP model of any satellite navigation system and is applicable to any satellite navigation system and multi-system combined PPP.

[0126] In summary, the non-combined PPP model and the estimated parameters are

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] In this case, X is the parameter vector to be estimated in the equation. The parameters in the X vector are the station coordinate correction, receiver clock error, system time bias including hardware delay, zenith tropospheric delay, ionospheric delay, and ambiguity.

[0133] Step S5 , using the observation data of all frequency points of the GNSS satellites at the local monitoring station, the pseudo-range frequency intra-difference and phase frequency intra-difference between some frequency points and all other frequency points are calculated.

[0134] Furthermore, the observation data in step S5 is a multi-frequency undifferenced undifferenced combination of pseudorange and carrier phase data of all frequency points of the wide-area monitoring station for GNSS satellites.

[0135] For example, taking the B1 frequency of the BeiDou system as an example, assuming and They represent the pseudorange observations of satellite i at B1C and B1I by receiver r, respectively. Difference is obtained as follows:

[0136]

[0137] In the above formula, is the intra-frequency difference parameter between the B1C frequency point and the B1I frequency point of satellite i, is the intra-frequency difference parameter between the B1 frequency point and the B1I frequency point of receiver r.

[0138] The intra-frequency difference parameters of the B1 frequency point and B1I frequency point of all satellites and receivers are obtained by using the least squares estimation of the simultaneous observation equations of multiple monitoring receivers observing multiple satellites.

[0139] The observation equation of the phase difference between B1C and B1I frequency points is:

[0140]

[0141] Similarly, by constructing the observation equation, the pseudorange frequency intradifference and phase frequency intradifference parameters of all frequency points of satellite i can be obtained using least squares estimation.

[0142] Step S6: Predict the pseudo-range frequency intra-difference and phase frequency intra-difference obtained in step S5 to generate inter-code deviation parameters and phase deviation parameters for all frequency points in the entire arc segment of the GNSS satellite.

[0143] Specifically, the pseudorange frequency and phase frequency interpolations are directly obtained by interpolating the raw observations from regional monitoring stations across China. Time series prediction methods are then used to obtain the code and phase deviations for the entire arc segment. A time series is a sequence of data that changes randomly over time. Currently, there are three main types of time series models: the AR(p) model (autoregressive model), the MA model (moving average model), and the ARMA model (autoregressive moving average model or hybrid model). The AR(p) model is the most well-developed forecasting model.

[0144] The AR(p) model can be defined as follows: for a stationary, normal, zero-mean time series {yt},

[0145]

[0146] Where y i (i=t,t-1,…,tp) represents the value at time i; is the coefficient; ε(k) is the NID(0,σ ε 2 ) white noise; p is the order.

[0147] Because the AR(p) model analyzes and forecasts stationary or quasi-stationary time series, and random fluctuations caused by random noise can play a significant role in the short-term forecast of clock error time series (particularly for clocks with poor stability), this present invention proposes using this model for short-term forecasts of satellite clock errors and updates its algorithm. Based on the fact that satellite clock errors conform to the properties of a quadratic polynomial in the short term, the present invention's modeling concept employs a quadratic polynomial model to extract trend terms when stationary data is used, while the random terms are modeled using a differential AR(p) model. This allows the AR(p) model to model residuals even for clocks whose clock error trends do not change linearly, thereby improving the accuracy of clock error forecasts.

[0148] Step S7: broadcast satellite orbit parameters, clock error parameters, delay parameters, and bias parameters and perform positioning calculation. Here, any communication form can be selected for broadcasting.

[0149] The serial numbers of the above-mentioned steps involved in the method of the present invention do not mean the order of execution of the method. The execution order of each step should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0150] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for converting satellite navigation signals into high-precision products, comprising: Step S1. Determine the orbit of the GNSS satellite using observation data of some frequencies of the GNSS satellite from the wide-area monitoring station; Step S2. Using the wide-area monitoring station to perform real-time filtering on the observation data of some frequencies of the GNSS satellite to obtain the clock error parameters; Step S3. Calculate the inter-symbol deviation parameter and the phase deviation parameter using the observation data of some frequency points of the GNSS satellite at the wide-area monitoring station; Step S4. Using the wide-area monitoring station to perform precise point positioning on the observation data of some frequencies of the GNSS satellite, the ionospheric delay parameters and the tropospheric delay parameters are calculated using a real-time filtering method; Step S5. Calculate the pseudo-range frequency difference and phase frequency difference between some frequency points and all other frequency points using the observation data of all GNSS satellite frequencies at the local monitoring station; Step S6. Predict the pseudorange frequency intra-frequency difference and phase frequency intra-frequency difference to generate inter-symbol deviation parameters and phase deviation parameters for all frequency points of the entire arc segment of the GNSS satellite; Step S7. Broadcast satellite orbit parameters, clock error parameters, delay parameters and bias parameters and perform positioning calculation; The observation data in step S1 and step S2 are ionospheric-free combinations of pseudoranges and carrier phases of some frequency points of the GNSS satellite; The observation data in step S4 and step S5 are multi-frequency non-differenced non-combinations of pseudorange and carrier phase data of partial and all frequency points of GNSS satellites respectively obtained by the wide area monitoring station.

2. The method according to claim 1, characterized in that In step S1, the GNSS satellite is subjected to statistical dynamic orbit determination and orbit prediction using observation data, and the initial orbit, dynamic parameters and measurement parameters of the GNSS satellite are calculated to obtain a precise predicted orbit of the GNSS satellite.

3. The method according to claim 2, characterized in that The measurement parameters include tropospheric delay scale factor, phase data ambiguity parameter, satellite and monitoring station clock difference, inter-satellite two-way ranging and satellite-ground two-way clock difference measurement results.

4. The method according to claim 2, characterized in that The step S2 comprises: Step S21. Fix the precisely predicted orbit of the GNSS satellite, use the ionospheric-free combination of pseudoranges and carrier phases of some frequency points of the wide-area monitoring station for the GNSS satellite as input, use Kalman filtering to solve the GNSS satellite clock error parameters on an epoch-by-epoch basis, and simultaneously estimate the ambiguity parameters of the carrier phase data; Step S22. Extrapolate the precise orbit of the GNSS satellite to generate a predicted orbit, and at the same time, perform gross error elimination, cycle slip detection and repair based on the non-differenced ionospheric combination observation values. Then, by fixing the extrapolated predicted orbit, introducing the wide-area monitoring station coordinates and ERP public parameters, adding corrections for tropospheric delay, station tide correction, satellite antenna phase center deviation, relativistic periodic term and relativistic gravitational delay, perform parameter estimation until the set residual threshold is met, exit the iteration, and obtain the precise GNSS satellite clock error parameters.

5. The method according to claim 4, characterized in that The step S21 includes: Step S211. Preprocessing the pseudorange and carrier phase data of the receiver of the current wide area monitoring station, wherein the preprocessing includes removing gross errors and detecting and repairing cycle slips from the MW combination of the undifferenced dual-frequency pseudorange phase and the ionospheric residual combination observation values; Step S212. For the ionospheric-free combination of the wide-area monitoring station receiver's pseudorange and carrier phase data obtained in step S211, Lagrangian interpolation is performed on the predicted orbit to obtain the GNSS satellite orbit at the satellite launch time. The satellite-to-ground distance is then calculated in combination with the precise coordinates of the ground monitoring receiver to obtain a priori residual sequence. Step S213. Perform the processing of step S212 on the observation data of all wide-area monitoring stations currently in use to obtain the coefficient matrix and the constant matrix, construct the normal equation matrix, and use Kalman filtering to estimate the clock error, atmosphere and ambiguity parameters of the GNSS satellite in real time.

6. The method according to claim 1, characterized in that The inter-code bias parameter calculation process in step S3 includes: calculating the ionospheric residual combination of the pseudo-range data of the wide-area monitoring station for some frequency points of the GNSS satellite, separating the ionospheric delay from the code bias parameter by using ionospheric delay modeling, and obtaining a high-precision estimate of the inter-code bias parameter; The pseudorange observation equation of the GNSS satellite is shown as follows: Among them, P i is the pseudorange observation, is the geometric distance between receiver j and satellite k, δt j is the clock error of receiver j, δt k is the clock error of satellite k, Δt cor is the error corrected by the available model, d trop is the tropospheric delay, is the channel delay of the i-th frequency point of satellite k, τ i,j is the channel delay of the i-th frequency point of receiver j, TEC is the total electron content on the oblique path, f i is the carrier frequency of the ith frequency point, ε i is the measurement error including multipath, c is the speed of light; According to the above formula, the difference between different frequency points can obtain a dual-frequency geometry-free combination: Among them, IFB j =t 1,j -t 2,j ; According to the ionospheric map GIM published by IGS, the TEC in the above formula can be used as a known quantity to obtain the combined delay value of the GNSS satellite and receiver hardware: Among them, TGD k is the hardware delay value of the GNSS satellite, IFB j is the hardware delay value of the receiver; According to a predetermined low-order spherical harmonic model or a high-order spherical harmonic model, the TEC is considered as a known quantity containing unknown ionospheric model parameters; a system of equations is established for the combined values ​​of hardware delays obtained from all GNSS satellites and all receivers, whose rank is deficient-one, and appropriate constraints need to be added to make the equations full-rank.

7. The method according to claim 1, characterized in that The phase deviation parameter calculation process in step S3 includes: using the carrier phase data ambiguity parameter obtained in step S2 as input, and calculating a high-precision phase deviation parameter through a multi-station and multi-satellite joint calculation method; Ambiguity fixation on the system side is performed together with orbit and clock error resolution. The input data for orbit determination include the initial orbit, initial clock error, observation data from several globally distributed receivers, and the precise coordinates of the receivers. The undifferenced, ionospheric-free combination is used as the basic observation quantity. First, the floating-point solutions of satellite orbits and clock errors are calculated. This step does not perform ambiguity fixation, and the ambiguity parameter results are in real number form: Fit the initial orbit to obtain the initial orbital elements and dynamic model parameters, perform orbital integration to obtain the reference orbit, state transfer matrix, and sensitivity matrix; The observation data are preprocessed to remove obvious unreasonable gross errors, cycle slip detection and repair are performed on the phase observation data, and antenna phase center correction, antenna phase winding correction, tropospheric correction, relativistic correction, and tidal correction are performed on the observation data. The dual-frequency observation data are used to form pseudo-range ionosphere-free combined observation quantities and phase ionosphere-free combined observation quantities respectively. Based on the reference orbit, initial clock error, station coordinates, and ionosphere-free combined observations, the observation equation is constructed to calculate the OC value, and then the parameters are estimated to obtain new orbit parameters and clock error parameters. After 5 to 7 iterations, gross errors are eliminated to obtain more accurate floating-point orbit and clock error results. Then, the satellite orbit and clock error fixed solution are calculated, and the satellite UPD parameters are estimated at the same time: the floating point solution orbit is integrated to obtain a more accurate reference orbit, state transfer matrix and sensitivity matrix; The ionospheric-free combined ambiguity obtained from the float solution is decomposed into wide-lane and narrow-lane ambiguities. Independent baselines are selected to form station-satellite double differences, making the ambiguities have integer characteristics. Since the wavelength of wide-lane ambiguities is 0.86m, they are easier to fix. Wide-lane ambiguities are calculated from MW combined observations. To reduce the effects of pseudorange noise and multipath, the wide-lane ambiguities are smoothed between epochs and fixed by rounding to the nearest integer. The narrow-lane ambiguities are searched and fixed using the LAMDA method. The observation equation is constructed based on the floating-point orbit and clock solutions, station coordinates, and ionosphere-free combined observations. The OC value is calculated, and the double-difference integer ambiguity fixation results are used as constraints to perform parameter estimation to obtain new orbit parameters and clock error parameters. Through 3 to 5 iterations, the success rate of integer ambiguity fixation is improved, gross errors are eliminated, and the final fixed orbit and clock error solutions are obtained. Finally, based on the ambiguity fixation results, the UPD parameters of the GNSS satellite and receiver are calculated. To solve the rank deficiency problem during the solution, a certain satellite or a certain receiver is selected as a reference benchmark, and its UPD parameter is considered to be 0, or the UPD mean of the entire constellation is selected as 0 as the benchmark; the obtained wide-lane and narrow-lane UPD parameters are converted to obtain the UPD parameter estimates corresponding to the two frequency points of the satellite.

8. The method according to claim 1, characterized in that After the solution is completed in step S4, the ionospheric delay parameters and the tropospheric delay parameters are gridded in real time to generate the ionospheric delay model background field and the tropospheric delay model background field, the gridded residual ionospheric slant path delay and the tropospheric delay.

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