Positioning time service method, system and device based on PPP-B2B and readable storage medium

Abstract

The invention provides a PPP-B2B-based positioning time service method, system and device and a readable storage medium, and relates to the technical field of information, and the method comprises the steps: receiving a PPP-B2b message and GNSS original observation data, extracting key features from the self-matched PPP-B2b message, and carrying out the learning and matching of the key features through a support vector machine; the satellite position and the satellite clock error are calculated by using the PPP-B2b orbit and the clock error correction data, an error model containing an error term is constructed, and the error model is optimized by using a nonlinear least square method; establishing a dual-frequency ionosphere-free combination model by using the calculated satellite position and satellite clock difference; and performing parameter estimation by adopting Kalman filtering to complete real-time positioning time service. Under the non-networking condition, single-station real-time high-precision positioning is achieved, and meanwhile synchronization of a local clock and a satellite system clock is achieved in a phase modulation mode.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a positioning and timing method, system, device and readable storage medium based on PPP-B2B. Background Art

[0002] With the rapid development of the Global Navigation Satellite System (GNSS) in recent decades, GNSS navigation, positioning and timing services have been integrated into all aspects of human life and social production and construction, from the establishment of a global reference frame to vehicle navigation. Precision Point Positioning (PPP) is currently a widely used navigation and positioning method, as it can achieve centimeter-level positioning accuracy with only a single station. In addition, my country issued the "Beidou Satellite Navigation System Precision Point Positioning Service Performance Specification (Version 1.0)" in 2020, broadcasting the Beidou-3 system and other global satellite navigation system precision orbit and clock correction parameters (PPP-B2b) through the Beidou-3 geostationary orbit (GEO) satellite. It can achieve real-time PPP only through Beidou satellites without the need for networking, and has great application prospects in the military field.

[0003] At present, most of the precise point positioning methods based on PPP-B2b are only used for positioning, and are rarely used for timing. In addition, the residual quality control methods (carrier phase, pseudorange) commonly used in quality control use fixed parameter thresholds, which are difficult to adapt to different complex scenarios. For example, the invention patent with application publication number [CN114280644A] discloses a precise point positioning system and method based on PPP-B2b service, which receives the PPP-B2b signal broadcast by the BeiDou-3 system, decodes and preprocesses the PPP-B2b signal, and obtains the correction number; then, it corrects based on the broadcast ephemeris, and finally outputs the corresponding precise point positioning result. Summary of the invention

[0004] The purpose of the present invention is to provide a positioning and timing method, system, device and readable storage medium based on PPP-B2B to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, the present application provides a positioning and timing method based on PPP-B2B, including:

[0006] Receive PPP-B2b messages and GNSS raw observation data, self-match each information type of the PPP-B2b message through a data version number, extract key features from the self-matched PPP-B2b message, learn and match the key features using a support vector machine, analyze the matching results, and associate the matching results with the broadcast ephemeris, wherein the data version number includes SSR, IODP, IODN, and IOD Corr, and the key features include data version numbers of different information types;

[0007] Based on the matching results, the satellite position and satellite clock error are calculated using the PPP-B2b orbit and clock correction data, and an error model including error terms is constructed. The error model is optimized using the nonlinear least squares method, where the error terms include ionospheric delay, tropospheric delay, and satellite and receiver hardware delay.

[0008] The data quality control is performed on the original GNSS observation data obtained from the receiver at fixed time intervals over a period of time, and a dual-frequency ionosphere-free combination model is established using the calculated satellite positions and satellite clock errors;

[0009] Based on the dual-frequency ionosphere-free combination model, Kalman filtering is used for parameter estimation to complete real-time positioning and timing.

[0010] Preferably, each information type of the PPP-B2b message is self-matched through a data version number, including:

[0011] Traverse the saved satellite mask messages and clock correction messages to determine whether the SSR and IODP of the satellite mask message are the same as the SSR and IODP of the clock correction message. If they are the same, save the corresponding satellite and proceed to the next step. If they are not the same, continue to determine.

[0012] Traverse the saved orbit correction messages and inter-code deviation correction messages, select the orbit correction message and inter-code deviation correction message that are consistent with the SSR successfully matched in the previous step, and form the final pairing group;

[0013] Based on the final pairing group, for the satellites with PPP-B2b correction messages, determine whether the IOD Corr in the orbit correction message is the same as the IOD Corr in the clock correction message. If they are the same, save the corresponding satellite orbit, clock correction and inter-code deviation data and proceed to the next step;

[0014] For all satellites saved in the previous step, determine whether the IODN in each satellite orbit correction message is the same as the known broadcast ephemeris IODC. If they are the same, continue to determine whether the satellite orbit and clock correction data exceed the available validity period. If not, use the orbit correction and clock correction data to calculate the satellite position and satellite clock error.

[0015] Preferably, extracting key features from the self-matched PPP-B2b message, learning and matching the key features using a support vector machine, and analyzing the matching results include:

[0016] Extracting key features from the self-matched PPP-B2b message to obtain a first key feature;

[0017] Using the first key feature, a support vector machine model is trained, wherein the SVM model is used to analyze and learn to distinguish different data types or categories according to the feature;

[0018] The result obtained by analyzing the first key feature in the message according to the SVM model is compared with the pattern learned in the training phase to determine the best matching result;

[0019] Analyze the matching results provided by the SVM model, verify the accuracy of the matching, and then evaluate the performance of the SVM model.

[0020] Preferably, the data quality control of the GNSS raw observation data acquired at fixed time intervals by the receiver within a period of time includes:

[0021] Preprocess the original GNSS observation data of the receiver at fixed time intervals, including removing outliers, filling missing values ​​and data smoothing, to obtain the original error data for error model construction;

[0022] The pseudorange / carrier phase observations are subjected to gross error elimination and cycle slip detection, and qualified observation data are screened out according to the preset data quality standards to provide input items for model building.

[0023] Preferably, the error model including the error term is constructed, and the error model is optimized using a nonlinear least squares method, which includes:

[0024] Based on the original error data, after the error source identification and parameterization processing, the error terms of ionospheric delay, tropospheric delay, satellite and receiver hardware delay are defined, and the error model is constructed to solve the parameter θ so that S(θ) reaches the minimum;

[0025] The error model is iteratively solved using the Gauss-Newton method, and the parameter estimates are updated until convergence to minimize the objective function.

[0026] In the second aspect, the present application also provides a positioning and timing system based on PPP-B2B, including:

[0027] Matching module: used for receiving PPP-B2b messages and GNSS original observation data, each information type of the PPP-B2b message is self-matched through the data version number, key features are extracted from the self-matched PPP-B2b message, and the key features are learned and matched by a support vector machine, the matching results are analyzed, and the matching results are associated with the broadcast ephemeris, wherein the data version number includes SSR, IODP, IODN and IOD Corr, wherein the key features include data version numbers of different information types;

[0028] Calculation module: used to calculate satellite position and satellite clock error based on the matching results and PPP-B2b orbit and clock correction data, build an error model including error terms, and optimize the error model using nonlinear least squares method. The error terms include ionospheric delay, tropospheric delay, satellite and receiver hardware delay.

[0029] Establishment module: used to perform data quality control on the original GNSS observation data obtained from the receiver at fixed time intervals over a period of time, and establish a dual-frequency ionosphere-free combination model using the calculated satellite positions and satellite clock errors;

[0030] Timing module: It is used to perform parameter estimation based on the dual-frequency ionosphere-free combination model using Kalman filtering to complete real-time positioning and timing.

[0031] In a third aspect, the present application also provides a positioning and timing device based on PPP-B2B, including:

[0032] Memory for storing computer programs;

[0033] A processor is used to implement the steps of the positioning and timing method based on PPP-B2B when executing the computer program.

[0034] In a fourth aspect, the present application further provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned positioning and timing method based on PPP-B2B are implemented.

[0035] The beneficial effects of the present invention are:

[0036] The present invention proposes an improved positioning and timing method based on PPP-B2b. The method utilizes PPP-B2b correction information and flexibly adjusts the residual quality control threshold according to actual conditions. In the absence of networking, not only single-station real-time high-precision positioning is achieved, but also the synchronization of the local clock and the satellite system clock is achieved by phase modulation.

[0037] The present invention is based on broadcast ephemeris and uses BeiDou-3 PPP-B2b correction message to calculate accurate satellite position and satellite clock error; then, the original GNSS observation data (carrier phase, pseudorange) is subjected to observation data quality control; then, for satellites with good observation data, accurate satellite position and satellite clock error are used to establish a dual-frequency ionosphere-free model; finally, Kalman filtering is used for parameter estimation, and positioning and timing results are obtained through residual quality control after filtering verification, so that PPP positioning is not only realized, but also single-station timing service is realized, and the receiver clock error is used to complete the synchronization of the local clock and the satellite system clock by phase modulation. The present invention flexibly adjusts the residual quality control threshold according to actual conditions to realize high-precision positioning and timing, and compared with the residual quality control method of traditional fixed parameters, the proposed method can effectively improve the positioning and timing accuracy.

[0038] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A schematic flow chart of a positioning and timing method based on PPP-B2B described in an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of the structure of a positioning and timing system based on PPP-B2B described in an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of the structure of a positioning and timing device based on PPP-B2B described in an embodiment of the present invention;

[0043] Figure 4 This is a diagram of receiver clock error in a positioning and timing experiment based on PPP-B2B described in an embodiment of the present invention.

[0044] In the figure: 701, matching module; 702, calculation module; 703, establishment module; 704, timing module; 800, positioning and timing equipment based on PPP-B2B; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0046] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0047] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an improved positioning and timing method based on PPP-B2b. Different from the conventional positioning method based on PPP-B2b, the method of the present invention can realize the real-time timing function and can flexibly adjust the residual quality control threshold according to the actual situation, so as to effectively improve the positioning and timing accuracy.

[0048] Embodiment 1:

[0049] This embodiment provides a positioning and timing method based on PPP-B2B.

[0050] See also Figure 1 , the figure shows that the method includes step S100, step S200, step S300, step S400 and step S500.

[0051] S100, receiving PPP-B2b messages and GNSS raw observation data, self-matching each information type of the PPP-B2b message through the data version number, extracting key features from the self-matched PPP-B2b message, and using a support vector machine to learn and match the key features, analyzing the matching results, and associating the matching results with the broadcast ephemeris, wherein the data version number includes SSR, IODP, IODN and IOD Corr, and wherein the key features include data version numbers of different information types.

[0052] It can be understood that step S100 includes S101, S102, S103 and S104, which include:

[0053] S101, traverse the saved satellite mask message and clock correction message, and determine whether the SSR and IODP of the satellite mask message are the same as the SSR and IODP of the clock correction message. If they are the same, save the corresponding satellite and proceed to the next step. If they are not the same, continue to determine.

[0054] S102, traverse the saved orbit correction messages and inter-code deviation correction messages, select the orbit correction message and inter-code deviation correction message that are consistent with the SSR successfully matched in the previous step, and form a final pairing group;

[0055] S103, based on the final pairing group, for the satellite with PPP-B2b correction message, determine whether the IODCorr in the orbit correction message is the same as the IOD Corr in the clock correction message. If they are the same, save the corresponding satellite orbit, clock correction and inter-code deviation data, and proceed to the next step;

[0056] S104. For all satellites saved in the previous step, determine whether the IODN in each satellite orbit correction message is the same as the known broadcast ephemeris IODC. If they are the same, continue to determine whether the satellite orbit and clock correction data exceed the available validity period. If not, use the orbit correction and clock correction data to calculate the satellite position and satellite clock error.

[0057] It should be noted that the key features are the data version number, the carrier-to-noise ratio of the satellite signal and the signal-to-noise ratio of the observation value. In particular, the orbit correction and clock correction data are represented by δO and C0 respectively, δO = [δO radial δO along δO cross ] T are the orbit corrections in the radial, tangential and normal satellite-fixed (Along-Cross-Radial, ACR) coordinate systems.

[0058] It should be noted that step S100 also includes S105, S106, S107 and S108, which include:

[0059] S105, extracting a key feature from the self-matched PPP-B2b message to obtain a first key feature;

[0060] S106, using the first key feature, training a support vector machine model, wherein the SVM model is used to analyze and learn to distinguish different data types or categories according to the feature;

[0061] S107, analyzing the result of the first key feature in the message according to the SVM model, and comparing it with the pattern learned in the training phase to determine the best matching result;

[0062] S108. Analyze the matching results provided by the SVM model, verify the accuracy of the matching, and then evaluate the performance of the SVM model.

[0063] It can be understood that, first, key features are extracted from the PPP-B2b message, which may include but are not limited to data version numbers (SSR, IODP, IODN, and IOD Corr). These features help describe the characteristics of the message and are used in the subsequent matching process; after training, the SVM model will be used to classify and match new PPP-B2b messages. In this step, the SVM model will analyze the features in the message and compare them with the patterns learned in the training phase to determine the best match; in terms of matching result analysis, the performance of the SVM model is evaluated, including its classification accuracy, precision, recall and other indicators. These indicators help understand the performance of the model in practical applications. For cases of matching errors, in-depth analysis is performed to identify possible causes. This may include checking for outliers in the data, limitations of the model, or deficiencies in the feature extraction process. Through these three steps, SVM can be effectively used to perform feature matching on PPP-B2b messages and ensure the accuracy and efficiency of the matching process.

[0064] S200. Based on the matching results, the satellite position and satellite clock error are calculated using the PPP-B2b orbit and clock correction data, an error model including error terms is constructed, and the error model is optimized using the nonlinear least squares method, wherein the error terms include ionospheric delay, tropospheric delay, and satellite and receiver hardware delay.

[0065] It can be understood that in this step, the calculation formula of the satellite position is:

[0066] X orbit =X broadcast -δX

[0067] Among them, X orbit is the exact position of the satellite in the Earth-fixed system after orbit correction, broadcast is the satellite position in the ECEF coordinate system calculated by broadcast ephemeris, δX is the orbit correction number of the satellite in the ECEF coordinate system; it needs to use the PPP-B2b orbit correction data δO to convert the coordinates to obtain:

[0068] δX=[e radial e along e cross ]·δO

[0069] Among them, [eradial e along e cross ] correspond to the radial, tangential and normal unit vectors respectively, expressed as:

[0070]

[0071] e along =e cross ×e radial

[0072] Where r is the satellite position vector calculated by the broadcast ephemeris; is the satellite velocity vector calculated from the broadcast ephemeris.

[0073] The calculation formula of satellite clock error is:

[0074]

[0075] Among them, t b is the satellite clock error calculated using the broadcast ephemeris, t s is the satellite clock error after PPP-B2b clock error correction, c is the speed of light, and C0 is the PPP-B2b clock error correction data.

[0076] Step S200 also includes:

[0077] Based on the original error data, after the error source identification and parameterization processing, the error terms of ionospheric delay, tropospheric delay, satellite and receiver hardware delay are defined, and the error model is constructed to solve the parameter θ so that S(θ) is minimized. The calculation formula is as follows:

[0078]

[0079] Where S(θ) is the objective function, y i is the observed data, x i is the corresponding independent variable, f is the nonlinear model function, θ is the model parameter vector, and n is the number of observations;

[0080] The error model is iteratively solved using the Gauss-Newton method, and the parameter estimates are updated until convergence to minimize the objective function.

[0081] It should be noted that, according to the satellite orbit and clock information provided by the PPP-B2b message, after data preprocessing, including removing outliers, filling missing values, data smoothing, etc., the original error data used to construct the error model is obtained; through the above formula, our goal is to solve the parameter S(θ) to the minimum; and according to the constructed error model, after iterative solution and parameter estimation, use nonlinear least squares optimization algorithms, such as Gauss-Newton method or Levenberg-Marquardt algorithm, to gradually update the parameter estimation until convergence to the minimized objective function.

[0082] S300, performing data quality control on the GNSS raw observation data obtained at fixed time intervals by the receiver within a period of time, and establishing a dual-frequency ionosphere-free combination model using the calculated satellite positions and satellite clock errors.

[0083] It should be noted that data quality control is performed on the GNSS raw observation data (pseudorange / carrier phase) obtained every 6 seconds over a period of time, and the accurate satellite position and clock error calculated in the above steps are used to establish a dual-frequency ionosphere-free combination model.

[0084] It can be understood that step S300 includes S301, S302, S303, S304 and S305, wherein:

[0085] S301, preprocessing the original GNSS observation data of the receiver at fixed time intervals, including removing outliers, filling missing values ​​and data smoothing, to obtain original error data for error model construction;

[0086] S302: performing gross error elimination and cycle slip detection on the pseudorange / carrier phase observation values, respectively, and selecting qualified observation data according to preset data quality standards to provide input items for establishing the model.

[0087] S303, processing the GNSS original observation data to obtain a preliminary processing result, wherein the GNSS original observation data includes pseudo-range observations and carrier phase observations, wherein the calculation formula for processing the pseudo-range observations by using a gross error elimination method is as follows:

[0088] |P1-P2|>50

[0089] Among them, P1 and P2 represent the pseudoranges of two frequency points respectively. If the absolute value of the difference between the pseudoranges of two frequency points of a satellite is greater than 50m, the observation of the satellite in this epoch is considered invalid.

[0090] It is understandable that data quality control is an important factor affecting the accuracy of PPP, and it is crucial to perform real-time correlation processing of observed data before parameter estimation.

[0091] Carrier phase observations may have cycle slips, and the TurboEdit algorithm is used for cycle slip detection; the TurboEdit algorithm constructs GF (Geometry-free) combination and MW (Melbourne Wubben) combination for cycle slip detection.

[0092] The calculation formula of the carrier phase observation using the TurboEdit algorithm is as follows:

[0093] L GF =λ1L1-λ2L2

[0094] Among them, L GF is the GF combined observation, L1 and L2 are the carrier observation values ​​of two frequency points, λ1 and λ2 are the wavelengths of the corresponding frequencies;

[0095]

[0096] Among them, N MW is the MW wide lane ambiguity, N1 and N2 are the ambiguities of the two frequencies, f1 and f2 are the corresponding frequencies, L1 and L2 are the carrier observation values ​​of the two frequencies, λ1 and λ2 are the wavelengths of the corresponding frequencies, and P1 and P2 represent the pseudoranges of the two frequencies respectively;

[0097] It can be understood that in this step, the ambiguity of the GF combination observations and the MW combination between adjacent epochs does not change much. If the difference between the GF combination observations of adjacent epochs or the difference between the ambiguity of the MW combination exceeds a certain threshold, it is considered that a cycle slip occurs. Otherwise, the carrier phase data is considered normal, that is:

[0098]

[0099] Among them, L GF,d =L GF,k -L GF,k-1 , N MW,d =N MW,k -N MW,k-1 , k represents the kth epoch, L GF,k is the GF combined observation at the kth epoch, L GF,k-1 is the GF combined observation at the k-1th epoch, L GF,d is the difference between the GF combination observations of two adjacent epochs, th GF is the GF combination threshold; N MW,k is the MW widelane ambiguity at the kth epoch, N MW,j-1 is the MW widelane ambiguity at the k-1th epoch, N MW,d is the difference in MW wide lane ambiguity between two adjacent epochs, th MW is the MW combined ambiguity threshold;

[0100] S304. Using satellite positions, clock errors and preliminary processing results, a dual-frequency ionosphere-free combination model is established:

[0101]

[0102] in, f1 and f2 are the frequencies of two frequency points, P IF is the pseudo-range observation value after P1 and P2 are combined, L IF is the phase observation value after L1 and L2 are combined, dt r is the deviation of the receiver clock relative to the satellite system time, that is, the receiver clock error, t s is the satellite clock error, δ trop is the tropospheric delay, c is the speed of light, λ IF represents the ionosphere-free combined wavelength, N IF is the ionospheric-free combined ambiguity, ε P,IF and ε L,IF They represent the noise of the dual-frequency ionosphere-free combined pseudorange and phase observation values, is the geometric distance between the receiver and the satellite;

[0103] S305, according to the coordinates of the receiver [x r y r z r ] and satellite position X orbit =[x s y s z s The calculation formula of ] is as follows:

[0104]

[0105] In the formula, is the geometric distance between the receiver and the satellite, [x r y r z r ] is the approximate coordinate of the receiver, [x s y s z s ] is the satellite position.

[0106] S400, based on the dual-frequency ionosphere-free combination model, uses Kalman filtering for parameter estimation to complete real-time positioning and timing.

[0107] 1) It can be understood that in this step, the linearization of the observation model:

[0108] The premise of using Kalman filtering is that the observation equation is linear, but the observation model established in the above steps is nonlinear, so linearization processing is required before applying Kalman filtering. The observation model is rewritten as follows:

[0109]

[0110] Among them, p IF and l IF are the combined pseudorange and phase observations minus the geometric distance between the receiver and the satellite, the satellite clock error, and various error corrections (tropospheric delay, relativistic effect, phase entanglement, earth rotation correction, etc.); δr is the receiver coordinate increment; M trop,w represents the tropospheric wet delay projection function, which is calculated using the Saastamoinen model; δ trop,w is the tropospheric wet delay in the zenith direction; e is based on the approximate coordinates of the receiver [x r y r z r ] and accurate satellite position X orbit =[x s y s z s ] is calculated as follows:

[0111] e=[αβγ]

[0112]

[0113] From the linearized observation model, we can know that the parameters to be estimated include the receiver coordinate increment δr, the receiver clock error dt r , Zenith tropospheric wet delay δ trop,w , fuzziness N IF , which is expressed as follows:

[0114]

[0115] Where n is the number of satellites, and each satellite has an ambiguity parameter.

[0116] 2) Based on the linearized observation model, the observation equation is expressed as:

[0117]

[0118] Where P and L are the p of n BDS-3 satellites. IF and l IF H is the observation matrix, which is obtained by taking partial derivatives of the estimated vector according to the linearized observation model; ε P and ε L represents the ε of n BDS-3 satellites P,IF and ε L,IF The vectors are as follows:

[0119]

[0120] in, and are the values ​​of the combined pseudorange and carrier phase of n satellites minus the satellite-to-ground distance and various errors, and are the combined pseudorange noise and carrier phase noise of n satellites, is the tropospheric wet delay projection of n satellites.

[0121] 3) Based on the above observation equation, the Kalman filter algorithm is used for filtering estimation.

[0122] 4) Quality control of filtering estimation results:

[0123] The quality control of the filter estimation result is performed using the filter post-test residual, and the residual is calculated as follows:

[0124] V=L-Hx

[0125] The quality analysis of the maximum residuals of pseudorange and carrier phase is performed respectively. If the maximum residual of pseudorange / carrier phase is greater than a certain threshold, it is considered that there is a gross error. After removing the corresponding satellite, re-filtering is performed until all residuals are less than the threshold. The filtering is considered successful, that is, the following formula is satisfied:

[0126]

[0127] Among them, V P,max is the maximum pseudorange residual, V L,max is the maximum residual error of the carrier phase, th P and L They are the pseudorange and carrier phase control thresholds respectively, and the filtering result is the positioning and timing result.

[0128] Usually, th P ,th L is a constant, but in practical engineering applications, a set of th P ,th L It is difficult to meet the positioning and timing requirements in various environments and for a long time, so the present invention proposes to P ,th L Make appropriate adjustments based on actual conditions and verify that P ,th L Appropriate adjustment can greatly improve the positioning and timing accuracy:

[0129] In this embodiment, the threshold th P ,th L The specific adjustment strategies are as follows:

[0130] The standard deviation (STD) of the original observations of a period of epoch number m is calculated by double difference method. Assume that the pseudorange / carrier phase of the i-th epoch is P i , L i, the pseudorange / carrier phase of the i-1th epoch is P i-1 , L i-1 , the pseudorange / carrier phase of the i-2 epoch is P i-2 , L i-2 , the standard deviation (STD) is calculated by processing the pseudorange / carrier phase using the double difference method with m epochs as a sliding window.

[0131] The double difference method formula is as follows: (taking pseudorange as an example, the carrier phase is similar)

[0132] dP i =P i -P i-1

[0133] dP i-1 =P i-1 -P i-2

[0134] dP i =dP i -dP i-1

[0135] Among them, dP i is the pseudorange single difference of the ith epoch, dP i-1 is the pseudorange single difference of the i-1th epoch, ddP i is the double difference of pseudorange at the i-th epoch.

[0136] Next, the processed pseudorange double difference ddP i Calculate the standard deviation (STD):

[0137]

[0138] Among them, P STD is the standard deviation of the pseudorange double difference, ddP i is the double difference of pseudorange at the ith epoch, is the average value of the pseudorange double difference of m epochs, where m is the number of epochs, and th P The value of the pseudorange double difference standard deviation P STD Sure:

[0139]

[0140] The above are only the algorithm steps for one epoch (sampling time). The receiver position estimated by Kalman filtering is the positioning result, and the estimated receiver clock error is the timing result. Phase adjustment (phase synchronization) based on the timing result can achieve synchronization between the local clock and the satellite system clock. For multiple epochs (fixed time intervals), the receiver position and receiver clock error can be iteratively estimated according to the above steps to achieve real-time positioning and timing for a single station.

[0141] In some embodiments, in order to evaluate the performance of the improved positioning and timing method based on PPP-B2b, the positioning and timing results are compared with those of the commercial Zhongsen PPP receiver (MT0303T1). The specific experimental process is as follows:

[0142] The experimental time is from 12:00:00 on August 27, 2024 to 20:00:00 the next day (UTC), and the location is on the roof of a building in Chengdu. The antenna is connected to the Sinan K823 receiver and the Zhongsen PPP receiver through a power divider for zero baseline measurement. The Sinan K823 receiver is used to obtain the original observation information for real-time positioning and timing, and the Zhongsen receiver saves the real-time PPP positioning results as a positioning reference value. At the same time, the output seconds of the Sinan K823 receiver and the output seconds of the Zhongsen receiver are connected to a multi-channel counter to analyze the timing accuracy. Since the proposed positioning method has a certain convergence time, this experiment selects the results 0.5 hours after the start of the experiment for positioning accuracy evaluation. The evaluation time is 7.5 hours, as shown in Table 1:

[0143] Table 1 Positioning accuracy statistics of positioning experiments

[0144]

[0145] Table 1 gives the RMS statistics of the positioning error sequence of the PPP-B2b positioning and timing method in the north-south, east-west, vertical and 3D dimensions. In addition, Figure 4 The receiver clock error curve and RMS statistical value of the proposed method are given. It can be seen that the positioning accuracy of the proposed method can reach the centimeter level, and the timing accuracy can reach 5ns. It has high positioning and timing accuracy and can be widely used in the military field without network communication environment.

[0146] In summary, the present invention provides an improved positioning and timing method based on PPP-B2b. First, the proposed method is based on broadcast ephemeris and uses BeiDou-3 PPP-B2b correction messages to calculate accurate satellite positions and satellite clock errors; then, the original GNSS observation data (carrier phase, pseudorange) is subjected to observation data quality control; then, for satellites with good observation data, a dual-frequency ionosphere-free model is established using accurate satellite positions and satellite clock errors; finally, Kalman filtering is used for parameter estimation, and positioning and timing results are obtained by filtering post-verification residual quality control. The present invention not only realizes PPP positioning, but also realizes single-station timing service, and uses the receiver clock error to synchronize the local clock with the satellite system clock by phase modulation. The present invention flexibly adjusts the residual quality control threshold according to actual conditions to achieve high-precision positioning and timing. Compared with the traditional residual quality control method with fixed parameters, the proposed method can effectively improve the positioning and timing accuracy.

[0147] Embodiment 2:

[0148] like Figure 2 As shown, this embodiment provides a positioning and timing system based on PPP-B2B, see Figure 2 The system comprises:

[0149] Matching module 701: used for receiving PPP-B2b message and GNSS original observation data, each information type of PPP-B2b message is self-matched through data version number, key features are extracted from the self-matched PPP-B2b message, and the key features are learned and matched by support vector machine, the matching result is analyzed, and the matching result is associated with the broadcast ephemeris, wherein the data version number includes SSR, IODP, IODN and IOD Corr, wherein the key feature includes data version number of different information types;

[0150] Calculation module 702: used to calculate satellite position and satellite clock error based on the matching result and using PPP-B2b orbit and clock error correction data, construct an error model including error terms, and optimize the error model using nonlinear least squares method, wherein the error terms include ionospheric delay, tropospheric delay, satellite and receiver hardware delay;

[0151] Establishing module 703: for performing data quality control on the original GNSS observation data acquired at fixed time intervals by the receiver within a period of time, and establishing a dual-frequency ionosphere-free combination model using the calculated satellite positions and satellite clock errors;

[0152] Timing module 704: used to perform parameter estimation based on the dual-frequency ionosphere-free combination model using Kalman filtering to complete real-time positioning and timing.

[0153] Specifically, the matching module 701 includes:

[0154] Extraction unit: used for extracting key features from the self-matched PPP-B2b message to obtain a first key feature;

[0155] Training unit: used to train a support vector machine model using the first key feature, wherein the SVM model is used to analyze and learn to distinguish different data types or categories according to the feature;

[0156] Comparison unit: used to analyze the result of the first key feature in the message according to the SVM model, and compare it with the pattern learned in the training stage to determine the best matching result;

[0157] Matching unit: used to analyze the matching results provided by the SVM model, verify the accuracy of the matching, and then evaluate the performance of the SVM model.

[0158] Specifically, the establishment module 703 includes:

[0159] Preprocessing unit: used to preprocess the original GNSS observation data of the receiver at fixed time intervals, including removing outliers, filling missing values ​​and data smoothing, to obtain the original error data for error model construction;

[0160] Screening unit: used to remove gross errors and detect cycle slips for pseudorange / carrier phase observations, and screen out qualified observation data according to preset data quality standards to provide input items for model building.

[0161] Specifically, the calculation module 702 includes:

[0162] Error construction unit: Based on the original error data, after the error source identification and parameterization processing, the error terms of ionospheric delay, tropospheric delay, satellite and receiver hardware delay are defined, and the error model is constructed to solve the parameter θ so that S(θ) is minimized. The calculation formula is as follows:

[0163]

[0164] Where S(θ) is the objective function, y i is the observed data, x i is the corresponding independent variable, f is the nonlinear model function, θ is the model parameter vector, and n is the number of observations;

[0165] Solution update unit: used to iteratively solve the error model using the Gauss-Newton method and update the parameter estimates until they converge to the minimized objective function.

[0166] Specifically, the establishment module 703 includes:

[0167] Preliminary processing unit: used to process the original GNSS observation data to obtain preliminary processing results, where the original GNSS observation data includes pseudo-range observations and carrier phase observations, where the pseudo-range observations are subjected to gross error elimination, and the calculation formula is as follows:

[0168] |P1-P2|>50

[0169] Among them, P1 and P2 represent the pseudoranges of two frequency points respectively. If the absolute value of the difference between the pseudoranges of two frequency points of a satellite is greater than 50m, the observation of the satellite in this epoch is considered invalid.

[0170] The calculation formula of the carrier phase observation using the TurboEdit algorithm is as follows:

[0171] L GF =λ1L1-λ2L2

[0172] Among them, L GFis the GF combined observation, L1 and L2 are the carrier observation values ​​of two frequency points, λ1 and λ2 are the wavelengths of the corresponding frequencies;

[0173]

[0174] Among them, N MW is the MW wide lane ambiguity, N1 and N2 are the ambiguities of the two frequencies, J1 and J2 are the corresponding frequencies, L1 and L2 are the carrier observation values ​​of the two frequencies, λ1 and λ2 are the wavelengths of the corresponding frequencies, and P1 and P2 represent the pseudoranges of the two frequencies respectively;

[0175]

[0176] Among them, L GF,d =L GF,k -L GF,k-1 , N MW,d =N MW,k -N MW,k-1 , k represents the kth epoch, L GF,k is the GF combined observation at the kth epoch, L GF,k-1 is the GF combined observation at the k-1th epoch, L GF,d is the difference between the GF combination observations of two adjacent epochs, th GF is the GF combination threshold; N MW,k is the MW widelane ambiguity at the kth epoch, N MW,k-1 is the MW widelane ambiguity at the k-1th epoch, N MW,d is the difference in MW wide lane ambiguity between two adjacent epochs, th MW is the MW combined ambiguity threshold;

[0177] Establishment unit: used to establish a dual-frequency ionosphere-free combination model using satellite positions, clock errors and preliminary processing results:

[0178]

[0179] in, P IF is the pseudo-range observation value after P1 and P2 are combined, L IF is the phase observation value after L1 and L2 are combined, dt r is the deviation of the receiver clock relative to the satellite system time, that is, the receiver clock error, t is the satellite clock error, δ trop is the tropospheric delay, c is the speed of light, λ IF represents the ionosphere-free combined wavelength, N IF is the ionospheric-free combined ambiguity, ε P,IF and ε L,IF They represent the noise of the dual-frequency ionosphere-free combined pseudorange and phase observation values, is the geometric distance between the receiver and the satellite;

[0180] Calculation unit: used to calculate the coordinates of the receiver [x r y r z r ] and satellite position X orbit =[x s y s z s The calculation formula of ] is as follows:

[0181]

[0182] In the formula, is the geometric distance between the receiver and the satellite, [x r y r z r ] is the approximate coordinate of the receiver, [x s y s z s ] is the satellite position.

[0183] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0184] Embodiment 3:

[0185] Corresponding to the above method embodiment, a positioning and timing device based on PPP-B2B is also provided in this embodiment. The positioning and timing device based on PPP-B2B described below and the positioning and timing method based on PPP-B2B described above can refer to each other.

[0186] Figure 3 FIG. 8 is a block diagram of a PPP-B2B-based positioning and timing device 800 according to an exemplary embodiment. Figure 3 As shown, the PPP-B2B-based positioning and timing device 800 includes: a processor 801 and a memory 802. The PPP-B2B-based positioning and timing device 800 also includes a multimedia component 803 and an I / O interface 804.

[0187] The processor 801 is used to control the overall operation of the PPP-B2B-based positioning and timing device 800 to complete all or part of the steps in the above-mentioned PPP-B2B-based positioning and timing method. The memory 802 is used to store various types of data to support the operation of the PPP-B2B-based positioning and timing device 800, which may include instructions for any application or method used to operate on the PPP-B2B-based positioning and timing device 800, as well as application-related data, such as PPP-B2B message data, original observation data, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, which is used to receive external audio signals. The received audio signal may be further stored in the memory 802 for transmission. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, which may be a keyboard, a mouse, or buttons, etc. These buttons may be virtual buttons or physical buttons.

[0188] In an exemplary embodiment, the PPP-B2B-based positioning and timing device 800 can be implemented by one or more application-specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processor, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned PPP-B2B-based positioning and timing method.

[0189] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned positioning and timing method based on PPP-B2B are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by the processor 801 of the positioning and timing device 800 based on PPP-B2B to complete the above-mentioned positioning and timing method based on PPP-B2B.

[0190] Embodiment 4:

[0191] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the positioning and timing method based on PPP-B2B described above can refer to each other.

[0192] A computer program is stored on the readable storage medium, and when the computer program is executed by the processor, the steps of the positioning and timing method based on PPP-B2B in the above method embodiment are implemented.

[0193] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0194] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0195] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A positioning and timing method based on PPP-B2B, characterized in that: include: Receive PPP-B2b messages and GNSS raw observation data, self-match each information type of the PPP-B2b message through a data version number, extract key features from the self-matched PPP-B2b message, learn and match the key features using a support vector machine, analyze the matching results, and associate the matching results with the broadcast ephemeris, wherein the data version number includes SSR, IODP, IODN, and IOD Corr, and the key features include data version numbers of different information types; Based on the matching results, the satellite position and satellite clock error are calculated using the PPP-B2b orbit and clock correction data, and an error model including error terms is constructed. The error model is optimized using the nonlinear least squares method, where the error terms include ionospheric delay, tropospheric delay, and satellite and receiver hardware delay. The data quality control is performed on the original GNSS observation data obtained from the receiver at fixed time intervals over a period of time, and a dual-frequency ionosphere-free combination model is established using the calculated satellite positions and satellite clock errors; Based on the dual-frequency ionosphere-free combination model, Kalman filtering is used for parameter estimation to complete real-time positioning and timing.

2. The positioning and timing method based on PPP-B2B according to claim 1, characterized in that: Each information type of the PPP-B2b message is self-matched through the data version number, including: Traverse the saved satellite mask messages and clock correction messages to determine whether the SSR and IODP of the satellite mask message are the same as the SSR and IODP of the clock correction message. If they are the same, save the corresponding satellite and proceed to the next step. If they are not the same, continue to determine. Traverse the saved orbit correction messages and inter-code deviation correction messages, select the orbit correction message and inter-code deviation correction message that are consistent with the SSR successfully matched in the previous step, and form the final pairing group; Based on the final pairing group, for the satellites with PPP-B2b correction messages, determine whether the IOD Corr in the orbit correction message is the same as the IOD Corr in the clock correction message. If they are the same, save the corresponding satellite orbit, clock correction and inter-code deviation data and proceed to the next step; For all satellites saved in the previous step, determine whether the IODN in each satellite orbit correction message is the same as the known broadcast ephemeris IODC. If they are the same, continue to determine whether the satellite orbit and clock correction data exceed the available validity period. If not, use the orbit correction and clock correction data to calculate the satellite position and satellite clock error.

3. The positioning and timing method based on PPP-B2B according to claim 1, characterized in that: The key features are extracted from the self-matched PPP-B2b message, and the key features are learned and matched by a support vector machine, and the matching results are analyzed, including: Extracting key features from the self-matched PPP-B2b message to obtain a first key feature; Using the first key feature, a support vector machine model is trained, wherein the SVM model is used to analyze and learn to distinguish different data types or categories according to the feature; The result obtained by analyzing the first key feature in the message according to the SVM model is compared with the pattern learned in the training phase to determine the best matching result; Analyze the matching results provided by the SVM model, verify the accuracy of the matching, and then evaluate the performance of the SVM model.

4. The positioning and timing method based on PPP-B2B according to claim 1, characterized in that: The satellite position and satellite clock error are calculated by using the PPP-B2b orbit and clock error correction data. The calculation formula is as follows: The satellite position is calculated as: X orbit =X broadcast -δX Among them, X orbit is the position of the satellite in the Earth-centered fixed system after orbit correction, X broadcast is the satellite position in the ECEF coordinate system calculated by broadcast ephemeris, δX is the orbit correction number of the satellite in the ECEF coordinate system; The calculation formula of satellite clock error is: Among them, t b is the satellite clock error calculated using the broadcast ephemeris, t s is the satellite clock error after PPP-B2b clock error correction, c is the speed of light, and C0 is the PPP-B2b clock error correction data.

5. The positioning and timing method based on PPP-B2B according to claim 1, characterized in that: The data quality control of the GNSS raw observation data obtained at fixed time intervals by the receiver within a period of time includes: Preprocess the original GNSS observation data of the receiver at fixed time intervals, including removing outliers, filling missing values ​​and data smoothing, to obtain the original error data for building the error model; The pseudorange / carrier phase observations are subjected to gross error elimination and cycle slip detection, and qualified observation data are screened out according to the preset data quality standards to provide input items for model building.

6. The positioning and timing method based on PPP-B2B according to claim 1, characterized in that: The error model including the error term is constructed and the error model is optimized by using the nonlinear least square method, which includes: Based on the original error data, after the error source identification and parameterization processing, the error terms of ionospheric delay, tropospheric delay, satellite and receiver hardware delay are defined, and the error model is constructed to solve the parameter θ so that S(θ) is minimized. The calculation formula is as follows: Where S(θ) is the objective function, y i is the observed data, x i is the corresponding independent variable, f is the nonlinear model function, θ is the model parameter vector, and n is the number of observations; The error model is iteratively solved using the Gauss-Newton method, and the parameter estimates are updated until convergence to minimize the objective function.

7. The positioning and timing method based on PPP-B2B according to claim 1, characterized in that: The data quality control is performed on the GNSS original observation data of the receiver fixed time interval information obtained within a period of time, and the calculated satellite position and satellite clock error are used to establish a dual-frequency ionosphere-free combination model, which includes: The GNSS original observation data is processed to obtain preliminary processing results, where the GNSS original observation data includes pseudo-range observations and carrier phase observations. The pseudo-range observations are subjected to gross error elimination, and the calculation formula is as follows: |P1-P2|>50 Among them, P1 and P2 represent the pseudoranges of two frequency points respectively. If the absolute value of the difference between the pseudoranges of two frequency points of a satellite is greater than 50m, the observation of the satellite in this epoch is considered invalid. The calculation formula of the carrier phase observation using the TurboEdit algorithm is as follows: L GF =λ1L1-λ2L2 Among them, L GF is the GF combined observation, L1 and L2 are the carrier observation values ​​of two frequency points, λ1 and λ2 are the wavelengths of the corresponding frequencies; Among them, N MW is the MW wide lane ambiguity, N1 and N2 are the ambiguities of the two frequencies, f1 and f2 are the corresponding frequencies, L1 and L2 are the carrier observation values ​​of the two frequencies, λ1 and λ2 are the wavelengths of the corresponding frequencies, and P1 and P2 represent the pseudoranges of the two frequencies respectively; Among them, L GF,d =L GF,k -L GF,k-1 , N MW,d =N MW,k -N MW,k-1 , k represents the kth epoch, L GF,k is the GF combined observation at the kth epoch, L GF,k-1 is the GF combined observation at the k-1th epoch, L GF,d is the difference between the GF combination observations of two adjacent epochs, th GF is the GF combination threshold; N MW,k is the MW widelane ambiguity at the kth epoch, N MW,k-1 is the MW widelane ambiguity at the j-1 epoch, N MW,d is the difference in MW wide lane ambiguity between two adjacent epochs, th MW is the MW combined ambiguity threshold; Using satellite positions, clock errors and preliminary processing results, a dual-frequency ionosphere-free combination model is established: in, f1 and f2 are the frequencies of two frequency points, P IF is the pseudo-range observation value after P1 and P2 are combined, L IF is the phase observation value after L1 and L2 are combined, dt r is the deviation of the receiver clock relative to the satellite system time, that is, the receiver clock error, t s is the satellite clock error, δ trop is the tropospheric delay, c is the speed of light, λ IF represents the ionosphere-free combined wavelength, N IF is the ionospheric-free combined ambiguity, ε P,IF and ε L,IF They represent the noise of the dual-frequency ionosphere-free combined pseudorange and phase observation values, is the geometric distance between the receiver and the satellite; According to the receiver coordinates [x r y r z r ] and satellite position X orbit =[x s y s z s The calculation formula of ] is as follows: In the formula, is the geometric distance between the receiver and the satellite, [x r y r z r ] is the approximate coordinate of the receiver, [x s y s z s ] is the satellite position.

8. A positioning and timing system based on PPP-B2B, based on the positioning and timing method based on PPP-B2B according to claim 1, characterized in that: include: Matching module: used for receiving PPP-B2b messages and GNSS original observation data, each information type of the PPP-B2b message is self-matched through the data version number, key features are extracted from the self-matched PPP-B2b message, and the key features are learned and matched by a support vector machine, the matching results are analyzed, and the matching results are associated with the broadcast ephemeris, wherein the data version number includes SSR, IODP, IODN and IOD Corr, wherein the key features include data version numbers of different information types; Calculation module: used to calculate satellite position and satellite clock error based on the matching results and PPP-B2b orbit and clock correction data, build an error model including error terms, and optimize the error model using nonlinear least squares method. The error terms include ionospheric delay, tropospheric delay, satellite and receiver hardware delay. Establishment module: used to perform data quality control on the original GNSS observation data obtained from the receiver at fixed time intervals over a period of time, and to establish a dual-frequency ionosphere-free combination model using the calculated satellite positions and satellite clock errors; Timing module: It is used to perform parameter estimation based on the dual-frequency ionosphere-free combination model using Kalman filtering to complete real-time positioning and timing.

9. A positioning and timing device based on PPP-B2B, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the PPP-B2B-based positioning and timing method as described in any one of claims 1 to 7 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the positioning and timing method based on PPP-B2B as claimed in any one of claims 1 to 7 is implemented.

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