BeiDou B2b-PPP user distance precision factor refinement and user positioning random model optimization method

Abstract

The present invention relates to a Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method; it belongs to the field of satellite navigation technology, and the method includes: a system end receives observation data of multiple Beidou / GNSS reference stations and B2b correction numbers and broadcast ephemeris, re-estimates Beidou B2b-PPP user distance precision factor URA in real time, and refines the user distance precision factor URA; the system end encodes the refined user distance precision factor URA and broadcasts it to the user end; the user end establishes a Beidou B2b positioning function model for B2b precise single point positioning; in the optimized random model corresponding to the positioning function model, the user uses the decoded user distance precision factor URA to calculate the observation noise. The present invention solves the problem of low accuracy of URA broadcast by B2b through re-estimation, broadcasts re-estimated URA through short messages, and does not occupy B2b signal bandwidth; the URA index is used for determining the B2b-PPP random model, and the user positioning random model is refined to improve positioning accuracy.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation technology, and in particular to a Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method. Background Art

[0002] Precise point positioning (PPP) only needs one receiver to obtain centimeter-level position information. Since the 21st century, it has attracted extensive attention from scholars in the field of global navigation satellite system (GNSS) due to its user-friendly operation and high accuracy. Since the International GNSS Service (IGS) formally proposed the real-time service (RTS) in 2013, real-time PPP (RT-PPP) technology has begun to attract more attention from scholars. RT-PPP relies on broadcast ephemeris and state space representation (SSR) parameters such as real-time orbit and clock correction, and is broadcast over the network in accordance with the format standard established by the Radio Technical Commission for Maritime Services (RTCM) using the Networked Transport of RTCM via Internet Protocol (NTRIP). RT-PPP has been widely used since its introduction and can achieve decimeter to centimeter-level accuracy.

[0003] With the continuous development of satellite-based augmented positioning services, many navigation satellite systems have integrated RT-PPP services into their signals. For example, Europe's Galileo high accuracy service (HAS) began trial operation on January 24, 2023, with a 95% accuracy target of 0.2 meters and 0.4 meters for global horizontal and vertical positioning, respectively. Since the completion of the construction of Beidou-2 regional navigation satellite system (BDS-2), the Beidou navigation satellite system (BDS) has also begun to provide decimeter-level positioning services to users in China and surrounding areas. However, this enhanced service is only open to authorized users. With the official launch of the Beidou-3 global navigation satellite system (BDS-3), the public RT-PPP service was officially announced to be launched in July 2021. The SSR correction number is modulated in the BDS-3B2b signal and broadcast by the BDS-3 geostationary orbit (GEO) satellite, providing decimeter to centimeter-level positioning services for users in China and surrounding areas.

[0004] In the RT-PPP scenario, a random model that conforms to the actual situation can better improve the positioning accuracy. Traditional random model determination methods include altitude angle model, signal-to-noise ratio model and comprehensive model. The altitude angle model takes into account the impact of satellite altitude angle on the quality of satellite signal observation. The reason is that at low altitude angles, satellite signals need to pass through a thicker atmosphere, and the quality deteriorates. This method is too simple and does not consider important indicators such as multipath effect and satellite orbit clock error accuracy. The model accuracy is limited. The signal-to-noise ratio model uses the signal-to-noise ratio index provided in the observation file as the basis for weighting, which can dynamically reflect the quality of satellite signals. However, this method focuses too much on the changes at the receiver end and considers less the impact on the satellite end and the propagation path. The current mainstream positioning algorithm generally uses a comprehensive model, which takes into account the difference in the quality of observation values ​​of satellites of different systems at different altitude angles, and also considers the impact of the troposphere and ionosphere on the propagation path. However, this method does not consider the difference in orbit clock errors between satellites, and there is still room for improvement. The user range accuracy index (URAI) broadcast by B2b-PPP is used as an integrity indicator of the positioning service system. While providing satellite availability information, its value itself reflects the orbital clock accuracy of the satellite. This index is difficult to obtain in traditional precise point positioning and pseudo-range point positioning. It is a unique index of B2b-PPP, and its existence provides an opportunity to further improve the accuracy of the random model. URAI consists of two parts: user accuracy level and user accuracy index. Users can restore the user range accuracy (URA) of the satellite based on these two parameters. This index is the embodiment of the satellite orbital clock accuracy at the user end, and its essence is the same, that is, this index can be directly incorporated into the random model to improve the accuracy of the random model. However, there has been no actual research on the above scheme. Summary of the invention

[0005] In view of the above analysis, the present invention aims to disclose a Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method; solve the problem of low accuracy of URA broadcast by B2b by re-estimation, and re-estimate URA by short message broadcasting without occupying B2b signal bandwidth; use URA index to determine B2b-PPP random model, refine user positioning random model and improve positioning accuracy.

[0006] The present invention discloses a Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method, comprising the following steps:

[0007] Step S1, the system receives observation data of multiple Beidou / GNSS reference stations as well as B2b corrections and broadcast ephemeris, re-estimates the Beidou B2b-PPP user range precision factor URA in real time, and refines the user range precision factor URA;

[0008] Step S2: The system end encodes the refined user distance precision factor URA and broadcasts it to the user end;

[0009] Step S3: The user terminal establishes a Beidou B2b positioning function model to perform B2b precise point positioning; in the optimized random model corresponding to the positioning function model, the user uses the decoded user distance precision factor URA to calculate the observation noise.

[0010] Furthermore, the step S1 comprises:

[0011] Step S101: The system receives in real time the BeiDou B2b orbit clock correction, broadcast ephemeris, and real-time observation data of each BeiDou / GNSS reference station;

[0012] Step S102, performing Kalman filter calculation on each Beidou / GNSS reference station epoch by epoch to obtain the phase post-verification residual of each frequency point of the Beidou satellite carrier;

[0013] Step S103: Establish a sliding window with a window width of m epochs and a sliding step length of 1 epoch, and perform sliding window acquisition on the phase post-verification residuals solved for each epoch starting from the time when the system is initialized;

[0014] Step S104, performing a first screening on the phase post-verification residual in each sliding window acquisition, and re-estimating the user distance precision factor;

[0015] The first screening is performed based on the satellite altitude angle and the magnitude of the phase post-verification residual;

[0016] Step S105, performing a second screening on the re-estimated user distance precision factor, and selecting the re-estimated user distance precision factor of the available satellite as the refined user distance precision factor;

[0017] The second screening is performed based on the size of the re-estimated user distance precision factor and the determined satellite orbit clock error accuracy.

[0018] Furthermore, the reorganized positioning function model is:

[0019]

[0020] in, and They represent the pseudorange and phase observations of the ionosphere-free combination, respectively; Indicates the geometric distance from the satellite to the receiver; represents the satellite clock error in the ionosphere-free combination; d trop,r and represent the zenith tropospheric delay and projection coefficient, respectively; represents the satellite's ionosphere-free combined hardware delay; and represent the observation noise of the pseudorange and phase ionospheric-free combined observations, respectively;

[0021] Receiver clock error after parameter reorganization Ionospheric combined floating point ambiguity after parameter reorganization

[0022] dt r,IF represents the receiver clock error in the ionosphere-free combination, B IF,r Indicates the receiver has no ionospheric combined hardware delay; and λ IF are the phase ambiguity and wavelength of the ionosphere-free combination; b IF,r and is the ionospheric-free combined hardware delay of the BeiDou / GNSS reference station and satellite phases.

[0023] Furthermore, the satellite s user range precision value URA is calculated based on the data in the sliding window. s It is expressed as:

[0024]

[0025] Among them, URA s is the URA value of satellite s; is the post-verification residual error of the phase of the i-th sample in the sliding window of the observable satellite s at station r, m is the length of the sliding window, n S is the total number of BeiDou / GNSS reference stations that observed satellite s in the current epoch.

[0026] Furthermore, in the first screening,

[0027] Remove all phase a posteriori residuals of satellites whose elevation angles do not meet the set requirements;

[0028] All the post-verification residuals of the satellites whose post-verification residuals are greater than the residual threshold are removed; and the number of monitoring stations that observe the satellites whose phase residuals are greater than the residual threshold is recorded.

[0029] Furthermore, in the second screening,

[0030] 1) Mark the re-estimated user distance precision factor that exceeds the URA threshold as unavailable;

[0031] 2) According to the records in step S104, any satellite that fails the residual screening after phase verification of a certain satellite by more than three Beidou / GNSS reference stations is determined to be a satellite with an accuracy problem in orbital clock error and is marked as unavailable.

[0032] Furthermore, the user distance precision factor refined by the system side is broadcast through the network or data is broadcast through Beidou short message communication equipment.

[0033] Furthermore, in the encoding process of data broadcasting by Beidou short message communication equipment in step S2,

[0034] 1) Encode the epoch time information in the form of a header, including the reference time of the epoch and the total number of URA satellites included in the epoch;

[0035] 2) Further coding regulations are made for the information that a single satellite needs to broadcast; the content that a single satellite needs to broadcast includes: system identification, satellite number and satellite URA.

[0036] Further, the Beidou B2b positioning function model used in step S3 is the same positioning function model as that in step S1 after parameter reorganization of the traditional dual-frequency ionospheric elimination combination;

[0037] In parameter estimation, the parameter vector to be estimated is:

[0038]

[0039] in is the position vector, is the receiver clock error after parameter reorganization, d trop,r is the zenith tropospheric wet delay, is the ionospheric combined floating point ambiguity after parameter reorganization; ISB is the inter-system bias between GPS and BDS-3, which is estimated as white noise in the Kalman filter;

[0040] The random model corresponding to the function model:

[0041]

[0042] in is the observation variance of satellite s; is the error factor in the observation noise and altitude angle; σ Orb , σ Clk , σ Trop and σ Ion They are the correction errors of orbit, clock, troposphere and ionosphere respectively.

[0043] Furthermore, the random model of the pseudorange observation value of the sth satellite can be expressed as:

[0044]

[0045] Ele represents the altitude angle of satellite s; is the coefficient factor related to the altitude angle, URA s It is the user distance precision factor URA obtained after decoding by the user end.

[0046] The present invention can achieve one of the following beneficial effects:

[0047] In the B2b precise single-point positioning method disclosed in the present invention, a random model is adopted that fully considers the orbital clock error accuracy of the satellite and can truly reflect the quality of the observation value, so that the observation value weight setting is more reasonable, thereby improving the positioning accuracy;

[0048] The present invention proposes for the first time to use the URA index to determine the B2b-PPP random model and refine the existing random model to improve the positioning accuracy.

[0049] The present invention innovatively utilizes the URA index to achieve the refinement of the random model. In order to better integrate the URA index into the random model, the mathematical expression of the random model is correspondingly modified, and the details of the model are reconstructed at the mathematical level.

[0050] To address the low accuracy of the URA index broadcast by B2b, multiple BeiDou / GNSS reference stations were used to re-estimate the B2b URA, and a set of URA indexes were recalculated independently of the B2b system end, thereby improving the accuracy of URA.

[0051] Since this set of URA indexes is calculated independently of the B2b system and cannot occupy the B2b signal bandwidth for broadcasting, a method of URA encoding and broadcasting based on the Beidou short message system is further proposed, so that the re-estimated URA can be broadcast to users through the Beidou short message system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0053] Figure 1 It is a flow chart of the Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method in an embodiment of the present invention;

[0054] Figure 2 Flow chart of the URA refinement method based on B2b product in an embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram of a single broadcast encoding format using GSMC in an embodiment of the present invention;

[0056] Figure 4 It is a single-day time series diagram of the re-estimated URA in an embodiment of the present invention (2023, annual cumulative day: 319);

[0057] Figure 5 A time series comparison diagram of single-station single-day positioning error with and without refined URA weighting in the embodiments of the present invention;

[0058] Figure 6 This is a statistical diagram of the early convergence accuracy and station convergence percentage of positioning using refined URA weighting and not using URA weighting in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used to illustrate the principles of the present invention together with the embodiments of the present invention.

[0060] An embodiment of the present invention discloses a Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method, such as Figure 1 As shown, the following steps are included:

[0061] Step S1, the system receives observation data of multiple Beidou / GNSS reference stations as well as B2b corrections and broadcast ephemeris, re-estimates the Beidou B2b-PPP user range precision factor URA in real time, and refines the user range precision factor URA;

[0062] Step S2: The system end encodes the refined user distance precision factor URA and broadcasts it to the user end;

[0063] Step S3: The user terminal establishes a Beidou B2b positioning function model to perform B2b precise point positioning; in the optimized random model corresponding to the positioning function model, the user uses the decoded user distance precision factor URA to calculate the observation noise;

[0064] Based on the current situation that the URA broadcasted by B2b itself has poor accuracy and cannot meet the demand for B2b precise point positioning, in step S1, a URA refinement method based on B2b products is used, such as Figure 2 As shown, including:

[0065] Step S101: The system receives in real time the BeiDou B2b orbit clock correction, broadcast ephemeris, and real-time observation data of each BeiDou / GNSS reference station;

[0066] Specifically, the precise coordinates of each Beidou / GNSS reference station can be calibrated using relevant technologies, with an accuracy of up to millimeter level.

[0067] Receive observation data from BeiDou / GNSS reference stations distributed around the world through real-time data streams. These observation data mainly provide pseudorange and carrier observation values. At the same time, receive B2b corrections and broadcast ephemeris, which can be synthesized into real-time precise ephemeris based on B2b corrections to provide satellite positions and satellite clock errors.

[0068] Among them, the real-time data stream is received through the Internet, and the B2b correction number and broadcast ephemeris require the use of a receiver that supports the reception of the above signals.

[0069] Step S102, performing Kalman filter calculation on each Beidou / GNSS reference station epoch by epoch to obtain the phase post-verification residual of the Beidou satellite carrier;

[0070] Using the real-time received Beidou B2b orbit clock corrections and the real-time observation data of each Beidou / GNSS reference station, the ionospheric-free combined Kalman filter solution of the fixed station coordinates is performed for each station epoch by epoch; the parameters to be estimated by the Kalman filter include the receiver clock error, the zenith tropospheric wet delay and the ionospheric-free combined ambiguity of each satellite.

[0071] Specifically, the Beidou B2b positioning function model used in this embodiment is a positioning function model after parameter reorganization of the traditional dual-frequency ionospheric elimination combination; wherein the parameter reorganization includes the reorganization of the receiver clock difference and the reorganization of the floating point ambiguity of the ionospheric elimination combination;

[0072] The reorganized positioning function model is:

[0073]

[0074] in, and They represent the pseudorange and phase observations of the ionosphere-free combination, respectively; Indicates the geometric distance from the satellite to the receiver; represents the satellite clock error in the ionosphere-free combination; d trop,r and represent the zenith tropospheric delay and projection coefficient, respectively; represents the satellite's ionosphere-free combined hardware delay; and represent the observation noise of the pseudorange and phase ionospheric-free combined observations, respectively;

[0075] Receiver clock error after parameter reorganization Ionospheric combined floating point ambiguity after parameter reorganization

[0076] dt r,IF represents the receiver clock error in the ionosphere-free combination, B IF,r Indicates the receiver has no ionospheric combined hardware delay; and λ IF are the phase ambiguity and wavelength of the ionosphere-free combination; b IF,r and is the ionospheric-free combined hardware delay of the phases of the BeiDou / GNSS reference station and the satellite;

[0077] It should be noted that here and The B2b orbital clock error was used for correction in the calculation. It can be corrected and eliminated through DCB (Differential Code Bias); It is the geometric distance between the satellite and the BeiDou / GNSS reference station, which can be obtained through the satellite position and precise coordinates of the station mentioned above. is the satellite clock error, which is also provided in the precise ephemeris and can be corrected. After removing the above known quantities, the equation is rewritten as:

[0078]

[0079] L is the corrected observation vector, is the pseudorange observation value, is the carrier observation value.

[0080] Through Kalman filtering, the unknown number on the right side of the rewritten equation is estimated by parameter estimation, and the phase post-verification residual of the observable satellite s at the jth frequency point of the observation station r is obtained.

[0081] Among them, the Kalman filtering process adopts a conventional filtering process.

[0082] Step S103: Establish a sliding window with a window width of m epochs and a sliding step length of 1 epoch, and perform sliding window acquisition on the phase post-verification residuals solved for each epoch starting from the time when the system is initialized;

[0083] The data obtained in each sliding window includes the phase posterior residual of each Beidou / GNSS reference station to the observed satellite at each epoch within the window width.

[0084] Preferably, the window width is 30 epochs. When the duration of one epoch is 1 second, the window width is 30 seconds.

[0085] Step S104, performing a first screening on the phase residual after verification in each sliding window acquisition, and re-estimating the user distance precision factor, which is used to characterize the accuracy index of the Beidou / GNSS reference station phase residual;

[0086] The first screening is performed based on the satellite altitude angle and the magnitude of the phase post-verification residual;

[0087] Specifically, in the first screening,

[0088] Remove all phase a posteriori residuals of satellites whose elevation angles do not meet the set requirements;

[0089] All the post-verification residuals of the satellites whose post-verification residuals are greater than the residual threshold are removed; and the number of monitoring stations that observe the satellites whose phase residuals are greater than the residual threshold is recorded.

[0090] Preferably, the phase residuals of satellites with elevation angles less than 15° are removed to avoid large phase observation value noise or multipath effect contamination of samples;

[0091] Eliminate the phase residuals of satellites whose post-verification residuals are greater than the residual threshold; record the number of stations that observed satellites with residuals greater than the residual threshold.

[0092] In the re-estimation of the user range precision factor URA, the user range precision factor value URA of satellite s s It is expressed as:

[0093]

[0094] Among them, URA s is the URA value of satellite s; is the post-verification residual error of the phase of the i-th sample in the sliding window of the observable satellite s at station r, m is the length of the sliding window, n S is the total number of BeiDou / GNSS reference stations that observed satellite s in the current epoch.

[0095] Step S105, performing a second screening on the re-estimated user distance precision factor, and selecting the re-estimated user distance precision factor of the available satellite as the refined user distance precision factor;

[0096] The second screening is performed based on the size of the re-estimated user distance precision factor and the determined satellite orbit clock error accuracy.

[0097] In the second screening,

[0098] 1) Mark the re-estimated user distance precision factor that exceeds the URA threshold as unavailable;

[0099] The URA threshold is 3σ; σ is obtained by URA statistics calculated at multiple stations and multiple days;

[0100] Preferably, the threshold is set to 17.8 mm, which can be adjusted appropriately according to actual conditions.

[0101] 2) According to the records in step S104, any satellite that fails the residual screening after phase verification of a certain satellite by more than three Beidou / GNSS reference stations is determined to be a satellite with an accuracy problem in orbital clock error and is marked as unavailable.

[0102] The above refinement process is repeated each time the sliding window slides, and a refined user distance precision factor is generated for each epoch of each satellite.

[0103] In step S2, the user distance precision factor refined by the system side can be broadcast through the network, and data can also be broadcast through Beidou short message communication equipment.

[0104] The former has better communication efficiency and transmission frequency; while the latter can get rid of dependence on the Internet and maintain the ability to obtain refined URA parameters in special areas such as oceans and deserts.

[0105] In this embodiment, taking BDS-3 global short message communication (GSMC) as an example, a refined URA coding broadcasting method is designed, which is the first of its kind.

[0106] Specifically, the encoding process in step S2 includes:

[0107] 1) Encode the epoch time information in the form of a header, including the reference time of the epoch (BDT week + seconds within the BDT week) and the total number of URA satellites included in the epoch. The encoding information of each parameter in the header is shown in Table 1.

[0108] Table 1 Information header encoding format design

[0109]

[0110] 2) Further coding regulations are made for the information that a single satellite needs to broadcast. The content that a single satellite needs to broadcast includes: system identification, satellite number and satellite URA. The URA accuracy is set to 0.1mm, with a range of 0 to 17.8mm;

[0111] In particular, when the broadcast value exceeds 17.8 mm, the system marks the satellite as unavailable. The specific coding design information of a single satellite is shown in Table 2.

[0112] Table 2 Single satellite information coding format design

[0113]

[0114]

[0115] In summary, the encoding format of using GSMC single broadcast is as follows Figure 3 As shown, the header occupies 46 bits and the single satellite information occupies 16 bits. According to this encoding method, the URA information of up to 30 satellites can be broadcast in a single communication, meeting the current use requirements of B2B GPS+BDS-3 satellites. In particular, when the bandwidth is further limited, the present invention can also adopt the same method as the Beidou system to calculate it to urai for broadcasting to save bandwidth. It should also be noted that the encoding method proposed by the present invention is only an optimal encoding method under the current short message bandwidth. Similar or easily conceivable encoding and broadcasting methods in this field should be covered within the protection scope of the present invention.

[0116] Specifically, in step S3, B2b precise point positioning is performed, and the method for using Beidou B2b precise positioning corrections includes:

[0117] Beidou B2b precise positioning service corrections consist of satellite mask, satellite orbit correction, satellite URA, satellite clock correction and satellite DCB. The five corrections are currently broadcast to users through B2b signals in four types. The information of each type is shown in Table 3.

[0118] Table 3 BeiDou-3B2b precise positioning service correction information Unit: s

[0119]

[0120] The satellite mask needs to be used in conjunction with the clock correction number when decoding the B2b precise correction information; the orbit and clock correction numbers need to be used in conjunction with the broadcast ephemeris. The formula for using the correction numbers to restore the precise coordinates of the satellite can be expressed as:

[0121]

[0122] in represents the satellite coordinates calculated using broadcast ephemeris, δX s is the B2b orbit correction in the Earth-center-Earth-fixed (ECEF) coordinate frame.

[0123] The original B2b orbit correction number is based on the satellite coordinate system when it is broadcast, so it needs to be converted from the satellite orbit coordinate system to the ECEF coordinate system. The conversion relationship can be expressed as:

[0124] δX s =[e r ,e a ,e c ]·δ s ;

[0125]

[0126] where r and Represents the position and velocity vector of the satellite in the ECEF coordinate system, which can be calculated by broadcast ephemeris. The URAI in the B2b correction number is calculated by the user accuracy level URA class and the user accuracy index URA value The two parameters, together with the orbit correction number, are broadcast to the user. The formula for recovering URA at the user end can be expressed as:

[0127]

[0128] The unit of the URA broadcast by B2b is meter. In addition, the formula for accurately correcting the clock error using the B2b clock error can be expressed as:

[0129]

[0130] Among them C BRD The satellite clock error is calculated using the broadcast ephemeris; δC0 represents the clock correction of B2b, c is the speed of light.

[0131] The above are the steps to recover high-precision orbit clock error and URA using the B2b frequency point.

[0132] In another solution of this embodiment, the URA uses a re-estimated URA broadcasted via a short message or a network.

[0133] The re-estimated URA obtained through short messages or the network has a valid value of 1 to 178. In actual use, it should be restored to URA in meters according to the following formula:

[0134] URA [RD] =URA [RD_value] ×10 -4

[0135] Among them, URA [RD_value] It is the original URA value decoded directly from the short message and cannot be directly used in the random model; URA [RD] It is URA [RD_value] The URA in meters obtained after unit conversion can be directly used for random model establishment.

[0136] In summary, the decoded user distance precision factor URA used in step S3 can be the URA recovered from the information of the B2b frequency point [B2b] Or a re-estimated and re-issued URA [RD] , both can be directly used to establish the random model, and the use method is the same. Therefore, in the following of the embodiments of the present invention, the two URA values ​​mentioned above are no longer distinguished, and URA is used uniformly. s To express.

[0137] The Beidou B2b positioning function model used in step S3 is the same positioning function model as that in step S1 after parameter reorganization of the traditional dual-frequency ionospheric elimination combination; wherein the parameter reorganization includes the reorganization of the receiver clock difference and the reorganization of the floating point ambiguity of the ionospheric elimination combination;

[0138] The reorganized positioning function model is:

[0139]

[0140] Considering that B2b supports both BDS-3 and GPS systems, the parameter vector to be estimated can be expressed as:

[0141]

[0142] in is the position vector, is the receiver clock error after parameter reorganization, d trop,r is the zenith tropospheric wet delay, is the combined floating point ambiguity after parameter reorganization and the ionospheric elimination. ISB is the inter-system bias between GPS and BDS-3. In this paper, ISB is estimated as white noise in the Kalman filter. It should be noted that since the BDS-3 clock reference is B3I, and the TGD correction in CNAV1 is based on the B1C and B2a frequencies, when using B1I / B3I for dual-frequency positioning, it is necessary to use the DCB broadcasted by B2btype 3 for code bias correction.

[0143] In addition to the ionosphere-free combined function model mentioned above, the method of this embodiment is generally applicable to non-difference non-combined precise point positioning function models, single-frequency precise point positioning function models and other multi-frequency and multi-system precise point positioning function models. The reason is that although the function models of the various precise point positioning function models mentioned above are inconsistent, the processing methods of the random models are generally consistent.

[0144] Based on the above reasons, this embodiment takes the more widely used ionosphere-free combined function model as an example to introduce the optimization scheme of the random model. The same method can be directly extended to the precise single-point positioning of various positioning models that are consistent with the random model processing method of this embodiment.

[0145] The random models corresponding to the various function models mentioned above can be expressed as:

[0146]

[0147] in is the observation variance of satellite s; σ Orb , σ Clk , σTrop and σ Ion They are the correction errors of orbit, clock, troposphere and ionosphere respectively.

[0148] Since the orbit and clock accuracy are unknown a priori, σ Orb ,σ Clk The values ​​are all set to 0; in addition, the parameters of the troposphere are estimated, and the ionosphere-free combination eliminates the first-order term of the ionospheric delay, so σ Trop and σ Ion By default it is also 0.

[0149] is the error factor in the observation noise and altitude angle, and its calculation formula can be expressed as:

[0150]

[0151] Among them, σ Obs is the observation noise, for the original pseudorange observation σ Obs It can be generally set to 0.3m, and the raw phase observation value is set to 0.003m. When a dual-frequency ionosphere-free combination is used, the observation noise will be amplified; Ele represents the altitude angle of satellite s; is the coefficient factor related to the altitude angle.

[0152] Therefore, without using satellite URA weighting, the user positioning random model can be expressed as:

[0153]

[0154] Due to σ Obs It is a fixed empirical value, which does not vary with different satellites or time. This will cause the user positioning random model to not conform to the actual situation.

[0155] In this embodiment, in the optimized random model, the user uses the decoded user distance precision factor URA s ;

[0156] Calculate the observation noise; the random model of the pseudorange observation value of the sth satellite can be expressed as:

[0157]

[0158] In the pseudo-range single point positioning (SPP) of this application, the user range precision factor URA of the sth satellite is sIt is calculated through pseudorange residuals. Its value reflects the influence of the satellite orbit, clock error, phase noise and other unmodeled deviations on the radial direction of the satellite from the station. Therefore, when it is used to construct a positioning random model, its practical significance is the same as the observed value noise σ Obs The difference between the satellites is the same, but its advantage is that it can change with the actual situation and can distinguish the differences between satellites. This improvement is effective.

[0159] In summary, the embodiment of the present invention adopts a random model that fully considers the orbital clock error accuracy of the satellite and can truly reflect the quality of the observation value, so that the observation value weight setting is more reasonable, thereby improving the positioning accuracy;

[0160] For the first time, it is proposed to use the URA index to determine the B2b-PPP random model, refine the existing random model, and improve positioning accuracy. In the current navigation field, the research and engineering practice of integrity are still in the exploratory stage, and URA is considered to be an important component of integrity. As an important indicator of satellite orbit clock accuracy, URA is provided in the services of various systems, but the use of the user end has not yet formed a mature system. The present invention proposes to use URA for the establishment of random models, which is innovative and theoretically rigorous, and is also very convenient in engineering practice.

[0161] The URA index is innovatively used to refine the random model. In order to better integrate the URA index into the random model, the mathematical expression of the random model is modified accordingly, and the details of the model are reconstructed at the mathematical level. The changes to the model are mainly reflected in changing the random model that has been fixed to a specific value to a dynamic adaptive model that changes with the actual situation. This is more in line with the actual situation and will inevitably improve the positioning accuracy. This optimization has low requirements for engineering implementation and small changes to the code, and has practical significance.

[0162] In order to solve the problem of low accuracy of URA index broadcast by B2b, multiple Beidou / GNSS reference stations were used to re-estimate B2b URA, and a set of URA indexes were recalculated independently of the B2b system, which improved the accuracy of URA. This method uses more IGS observation data, so that the URA solution result will not be affected by a single station. The sliding window averages the multi-epoch post-verification residuals, so that the URA value tends to be stable, and there will be no obvious jumps that affect the positioning results.

[0163] Since this set of URA indexes is calculated independently of the B2b system and cannot occupy the B2b signal bandwidth for broadcasting, a method of URA encoding and broadcasting based on the Beidou short message system is further proposed, so that the re-estimated URA can be broadcast to users through the Beidou short message system. This encoding method comprehensively considers the global short message bandwidth limitation and maximizes the information collection efficiency. Within the limit of 560 bits, it provides key information including satellite system, PRN, URA value, etc. The encoding format is concise and easy for users to use.

[0164] The B2b-PPP positioning verification results were conducted using positioning verification stations mainly concentrated around China, such as Figures 4 to 6 As shown;

[0165] Figure 4 The URA values ​​of BDS-3 and GPS estimated by the system are shown, which are basically concentrated within 0.01m. Different colors in the figure represent different satellites.

[0166] Figure 5 The comparison of PPP positioning accuracy when using the re-estimated URA to establish a random model is shown. The left figure is the positioning error time series using the URA random model. The ENU three-directional accuracy is 0.062, 0.016, and 0.066m respectively, and the three-dimensional direction accuracy is 0.092m.

[0167] The right figure is the positioning error time series without using the URA random model. The ENU three-directional accuracy is 0.071, 0.016, and 0.071m, respectively, and the three-dimensional accuracy is 0.102m. It can be found that the ENU three-directional accuracy has been improved, with an overall improvement of about 8.9%.

[0168] Figure 6 The statistical results of the convergence accuracy in the first half hour and the percentage of converged stations for all stations using two weighting methods are shown.

[0169] As can be seen from the figure, using refined URA to determine weights can effectively increase the convergence speed and positioning performance of users in the early stage of using B2b-PPP. Compared with not using URA for determination of weights, the average three-dimensional convergence accuracy of using refined URA in the first half hour is improved by about 0.1m, the average station convergence percentage is increased by 29% in the first twenty minutes, and 100% of the stations converge within 25 minutes. Table 3 shows the statistical results of all stations. Using refined URA significantly improves the convergence time of B2b-PPP. The dynamic positioning convergence time is shortened from 17.3min to 12.6min, an increase of 27.2%, and the static positioning convergence time is increased by 22.2%. Because URA provides more accurate observation value random models and satellite integrity information, the convergence accuracy is also improved compared with before, and the dynamic and static convergence accuracy are improved by 8.5% and 10.3% respectively.

[0170] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method, characterized in that: The following steps are involved: Step S1, the system receives observation data of multiple Beidou / GNSS reference stations as well as B2b corrections and broadcast ephemeris, re-estimates the Beidou B2b-PPP user range precision factor URA in real time, and refines the user range precision factor URA; Step S2: The system end encodes the refined user distance precision factor URA and broadcasts it to the user end; Step S3: The user terminal establishes a Beidou B2b positioning function model to perform B2b precise point positioning; in the optimized random model corresponding to the positioning function model, the user uses the decoded user distance precision factor URA to calculate the observation noise; The step S1 comprises: Step S101: The system receives in real time the BeiDou B2b orbit clock correction, broadcast ephemeris, and real-time observation data of each BeiDou / GNSS reference station; Step S102, performing Kalman filter calculation on each Beidou / GNSS reference station epoch by epoch to obtain the phase post-verification residual of each frequency point of the Beidou satellite carrier; Step S103: Establish a sliding window with a window width of m epochs and a sliding step length of 1 epoch, and perform sliding window acquisition on the phase post-verification residuals solved for each epoch starting from the time when the system is initialized; Step S104, performing a first screening on the phase post-verification residual in each sliding window acquisition, and re-estimating the user distance precision factor; The first screening is performed based on the satellite altitude angle and the magnitude of the phase post-verification residual; Step S105, performing a second screening on the re-estimated user distance precision factor, and selecting the re-estimated user distance precision factor of the available satellite as the refined user distance precision factor; The second screening is performed based on the size of the re-estimated user distance precision factor and the determined satellite orbit clock error accuracy; The reorganized positioning function model is: in, and They represent the pseudorange and phase observations of the ionosphere-free combination, respectively; Indicates the geometric distance from the satellite to the receiver; represents the satellite clock error in the ionosphere-free combination; d trop,r and represent the zenith tropospheric delay and projection coefficient, respectively; represents the satellite's ionosphere-free combined hardware delay; and represent the observation noise of the pseudorange and phase ionospheric-free combined observations, respectively; Receiver clock error after parameter reorganization Ionospheric combined floating point ambiguity after parameter reorganization dt r,IF represents the receiver clock error in the ionosphere-free combination, B IF,r Indicates the receiver has no ionospheric combined hardware delay; and λ IF are the phase ambiguity and wavelength of the ionosphere-free combination; b IF,r and is the ionospheric-free combined hardware delay of the phases of the BeiDou / GNSS reference station and the satellite; The Beidou B2b positioning function model used in step S3 is the same positioning function model as that in step S1 after parameter reorganization of the traditional dual-frequency ionospheric elimination combination.

2. The Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method according to claim 1 is characterized in that: Satellite s user range precision value URA calculated based on the data in the sliding window s It is expressed as: Among them, URA s is the URA value of satellite s; is the post-verification residual error of the phase of the i-th sample in the sliding window of the observable satellite s at station r, m is the length of the sliding window, n S is the total number of BeiDou / GNSS reference stations that observed satellite s in the current epoch.

3. The Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method according to claim 1 is characterized in that: In the first screening, Remove all phase a posteriori residuals of satellites whose elevation angles do not meet the set requirements; Remove all post-verification residuals of satellites whose post-verification residuals are greater than the residual threshold; And record the number of monitoring stations that observed satellites with phase residuals greater than the residual threshold.

4. The Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method according to claim 1 is characterized in that: In the second screening, 1) Mark the re-estimated user distance precision factor that exceeds the URA threshold as unavailable; 2) According to the records in step S104, any satellite that fails the residual screening after phase verification of a certain satellite by more than three Beidou / GNSS reference stations is determined to be a satellite with an accuracy problem in orbital clock error and is marked as unavailable.

5. The Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method according to claim 1 is characterized in that: The user distance precision factor refined by the system is broadcast through the network or through Beidou short message communication equipment.

6. The Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method according to claim 5 is characterized in that: In the encoding process of data broadcasting through Beidou short message communication equipment in step S2, 1) Encode the epoch time information in the form of a header, including the reference time of the epoch and the total number of URA satellites included in the epoch; 2) Further coding regulations for the information to be broadcast by a single satellite; The content that a single satellite needs to broadcast includes: system identification, satellite number and satellite URA.

7. The Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method according to claim 1 is characterized in that: The Beidou B2b positioning function model used in step S3 is the same positioning function model as that in step S1 after parameter reorganization of the traditional dual-frequency ionospheric elimination combination; In parameter estimation, the parameter vector to be estimated is: in is the position vector, is the receiver clock error after parameter reorganization, d trop,r is the zenith tropospheric wet delay, is the ionospheric combined floating point ambiguity after parameter reorganization; ISB is the inter-system bias between GPS and BDS-3, which is estimated as white noise in the Kalman filter; The random model corresponding to the function model: in is the observation variance of satellite s; is the error factor in the observation noise and altitude angle; σ Orb , σ Clk , σ Trop and σ Ion They are the correction errors of orbit, clock, troposphere and ionosphere respectively.

8. The Beidou B2b-PPP user distance precision factor refinement and user positioning random model optimization method according to claim 7 is characterized in that: The random model of the pseudorange observation value of the sth satellite can be expressed as: Ele represents the altitude angle of satellite s; is the coefficient factor related to the altitude angle, URA s It is the user distance precision factor URA obtained after decoding by the user end.

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