PPP-B2b Orbit Clock Error Correction Method and Device

Abstract

The present invention relates to the cross - technical field of the integration of BDS - 3 precise point positioning service and machine learning, and particularly relates to a PPP - B2b orbit clock error correction method and device. The method includes: restoring the PPP - B2b precise orbit and clock difference based on the Beidou navigation message and the PPP - B2b precise message of the Beidou - 3 precise point positioning service; adjusting the neural network weights and configuration parameters; performing memory decay factor weighted processing on the comprehensive error prediction value by using a preset memory decay mechanism; and correcting the PPP - B2b orbit clock error according to the final prediction value. By adjusting the neural network weights and bias parameters, the present invention combines the neural network method in machine learning with the PPP - B2b technology, establishes an error compensation model for PPP - B2b correction information, weakens the influence of space signal error on the positioning accuracy, and finally improves the real - time precise point positioning accuracy.

Description

Technical Field

[0001] The present invention relates to the cross - technical field of the integration of BDS - 3 precise point positioning service and machine learning, and particularly relates to a PPP - B2b orbit clock error correction method and device. Background Technique

[0002] With the rapid development of emerging technologies such as autonomous driving, mobile robots, and artificial intelligence, people's demand for spatio - temporal information is undergoing profound changes, gradually moving from low precision to high precision, and from post - processing to real - time response. In the era of the booming development of the Internet of Everything and artificial intelligence, the acquisition, processing, and information extraction of spatio - temporal data need to be intelligentized. Deeply integrating navigation engineering and artificial intelligence to promote the development of spatio - temporal intelligence can endow the system with stronger perception, reasoning, generation, and interaction capabilities. High - precision positioning services have become an urgent need in industrial applications, leading technological innovation and business model innovation.

[0003] In related technologies, the Beidou Navigation Satellite System (BDS) provides precise correction information of GPS and Beidou - 3 satellites to help users achieve real - time precise point positioning, that is, the PPP - B2b service. By receiving these enhanced information broadcast by GEO satellites, users can achieve dynamic decimeter - level and static centimeter - level positioning accuracy.

[0004] However, for real - time navigation and positioning users in related technologies, compared with QZSS CLAS and Galileo high - precision service HAS, the orbit accuracy of PPP - B2b is relatively low, and there are still predictable error components in the orbit and clock error, and there is still room for further improvement. Summary of the Invention

[0005] The present invention provides a PPP - B2b orbit clock error correction method and device to solve the problem that traditional error correction relies on physical models and empirical formulas, ignores the non - linear components in the error sequence, and has limitations in dealing with abnormal signals or dynamic environmental changes.

[0006] In the first aspect of the present invention, an embodiment provides a PPP-B2b orbit clock error correction method, including the following steps: Based on the Beidou navigation message and the PPP-B2b precise message of the precise point positioning service of Beidou-3, restore the PPP-B2b precise orbit and clock difference to generate PPP-B2b restoration data; Based on the PPP-B2b restoration data, use a preset reference product to evaluate the PPP-B2b orbit clock error that meets the preset time condition to generate PPP-B2b orbit clock error data; Calculate the comprehensive error of the PPP-B2b precise orbit and the clock difference according to the PPP-B2b orbit clock error data, and adjust the neural network weights and configuration parameters according to the comprehensive error to generate adjustment data, and calculate the loss function value of the neural network training according to the adjustment data until the loss function value meets the preset convergence condition, generate the training result of the preset error model; Based on the training result of the preset error model, use a preset memory decay mechanism to perform memory decay factor weighting processing on the comprehensive error prediction value to generate a final prediction value; Correct the PPP-B2b orbit clock error according to the final prediction value to generate the predicted orbit clock result of PPP-B2b.

[0007] Optionally, in an embodiment of the present invention, before generating the training result of the preset error model, it further includes: Based on the PPP-B2b orbit clock error, establish the preset error model based on the preset backpropagation neural network algorithm.

[0008] Optionally, in an embodiment of the present invention, the restoration formula of the PPP-B2b precise orbit is:

[0009]

[0010]

[0011]

[0012]

[0013]

[0014] Among them, the superscript s represents the observed GNSS satellite, are the unit vectors of the satellite's radial, tangential, and normal directions in the earth-fixed coordinate system respectively, is the orbit correction number broadcast by PPP-B2b, is the orbit correction number vector in the ECEF coordinate system, and calculate the position and velocity vectors calculated from the broadcast ephemeris respectively, is the satellite position vector calculated for broadcast ephemeris in the ECEF coordinate system, is the position vector of the restored PPP-B2b precise orbit in the ECEF coordinate system;

[0015] The restoration formula for the PPP-B2b precise clock offset is:

[0016]

[0017] where, is the precise satellite clock offset restored from PPP-B2b correction information, is the satellite clock offset obtained from the broadcast ephemeris table, is the PPP-B2b clock offset correction value, is the speed of light.

[0018] Optionally, in an embodiment of the present invention, the error formula of the PPP-B2b precise orbit relative to the reference orbit is:

[0019]

[0020] where, is the PPP-B2b orbit error vector, is the reference orbit vector, is the vector of the PPP-B2b precise orbit, is the transformation matrix from the satellite body-fixed coordinate system to the Earth-fixed coordinate system, is the satellite PCO correction vector;

[0021] The calculation formula for the PPP-B2b orbit clock offset error is:

[0022]

[0023]

[0024] where, is the satellite clock offset of PPP-B2b after correcting DCB in the B1I / B3I IF combination, is the precise reference clock offset, and are the frequencies of the B1I and B3I signals respectively, is the PPP-B2b satellite clock offset error.

[0025] Optionally, in an embodiment of the present invention, the calculation formula for the comprehensive error is:

[0026]

[0027]

[0028] Among them, is the comprehensive error of the PPP - B2b orbit and the clock error, is the direction vector of the ECEF coordinate system, , , are respectively the satellite positions calculated from the broadcast ephemeris, represents the geometric distance between the satellite and the earth's center;

[0029] The expression of the loss function value is:

[0030]

[0031] Among them, is the predicted value, is the true value, is the quantity;

[0032] The expression for the loss function value to reach the preset convergence condition is:

[0033]

[0034] Among them, is a positive number, and are respectively the loss function values after this round and the previous round of iteration.

[0035] Optionally, in an embodiment of the present invention, the weighted formula of the memory decay factor is:

[0036]

[0037] Among them, is the final predicted value after weighting, is the model predicted value at time , is the predicted value after weighting at the previous time, is that the weighted predicted value of the memory decay factor at the initial time adopts the actual comprehensive error at this time.

[0038] Optionally, in an embodiment of the present invention, the expression for applying the final predicted orbit clock error result to precise positioning is:

[0039]

[0040]

[0041] Among them, is the user receiver receives GNSS satellites The pseudorange observation value on the channel at frequency is the pseudorange observation value of the GNSS satellite received by the receiver at the frequency . The subscript represents the observed user receiver. The carrier phase observation value of the GNSS satellite received by the user receiver at the frequency is the pseudorange observation value on the channel at frequency . The carrier phase observation value of the GNSS satellite received by the user receiver at the frequency is . The speed of light in vacuum is , and are the satellite and receiver clock biases respectively. The tropospheric delay projection function is , the receiver zenith tropospheric delay is , the ionospheric delay in the line-of-sight direction is , and are the observation noises corresponding to the pseudorange and carrier observations.

[0042] In the second aspect of the embodiments of the present invention, a PPP-B2b orbit clock error correction device is provided, including: a recovery module, configured to recover the PPP-B2b precise orbit and clock difference based on the Beidou navigation message and the Beidou-3 precise point positioning PPP-B2b precise message, so as to generate PPP-B2b recovery data; an evaluation module, configured to evaluate the PPP-B2b orbit clock error that meets the preset time condition by using a preset reference product based on the PPP-B2b recovery data, so as to generate PPP-B2b orbit clock error data; a calculation module, configured to calculate the comprehensive error of the PPP-B2b precise orbit and the clock difference according to the PPP-B2b orbit clock error data, and adjust the neural network weights and configuration parameters according to the comprehensive error, so as to generate adjustment data, and calculate the loss function value of the neural network training according to the adjustment data until the loss function value meets the preset convergence condition, and then generate the training result of the preset error model; a weighting module, configured to perform memory decay factor weighting processing on the comprehensive error prediction value by using a preset memory decay mechanism based on the training result of the preset error model, so as to generate a final prediction value; a correction module, configured to correct the PPP-B2b orbit clock error according to the final prediction value, so as to generate a predicted orbit clock result of PPP-B2b.

[0043] Optionally, in an embodiment of the present invention, it further includes: a building module, configured to build the preset error model based on the preset backpropagation neural network algorithm before generating the training result of the preset error model based on the PPP-B2b orbit clock error.

[0044] Optionally, in an embodiment of the present invention, the recovery formula of the PPP-B2b precise orbit is:

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] Wherein, the superscript s represents the observed GNSS satellite, are respectively the unit vectors of the satellite in the radial, tangential and normal directions in the earth-fixed coordinate system, is the orbit correction number broadcast by PPP-B2b, is the orbit correction number vector in the ECEF coordinate system, and Calculate the position and velocity vectors calculated from the broadcast ephemeris respectively, is the satellite position vector calculated from the broadcast ephemeris in the ECEF coordinate system, is the position vector of the PPP-B2b precise orbit restored in the ECEF coordinate system;

[0051] The restoration formula of the PPP-B2b precise clock offset is:

[0052]

[0053] where, is the precise satellite clock offset restored from the PPP-B2b correction information, is the satellite clock offset obtained from the broadcast ephemeris, is the PPP-B2b clock offset correction value, is the speed of light.

[0054] Optionally, in an embodiment of the present invention, the error formula of the PPP-B2b precise orbit relative to the reference orbit is:

[0055]

[0056] where, is the PPP-B2b orbit error vector, is the reference orbit vector, is the vector of the PPP-B2b precise orbit, is the transformation matrix from the satellite body-fixed coordinate system to the Earth-fixed coordinate system, is the satellite PCO correction vector;

[0057] The calculation formula of the PPP-B2b orbit clock offset error is:

[0058]

[0059]

[0060] where, is the satellite clock offset of PPP-B2b after correcting the DCB after the B1I / B3I IF combination, is the precise reference clock offset, and are the frequencies of the B1I and B3I signals respectively, is the PPP-B2b satellite clock offset error.

[0061] Optionally, in an embodiment of the present invention, the calculation formula of the comprehensive error is:

[0062]

[0063]

[0064] Among them, is the comprehensive error of the PPP-B2b orbit and the clock error, is the direction vector of the ECEF coordinate system, , , are the satellite positions calculated from the broadcast ephemeris respectively, represents the geometric distance between the satellite and the earth's center;

[0065] The expression of the loss function value is:

[0066]

[0067] Among them, is the predicted value, is the true value, is the quantity;

[0068] The expression for the loss function value to reach the preset convergence condition is:

[0069]

[0070] Among them, is a positive number, and are the loss function values after this round and the previous round of iteration respectively.

[0071] Optionally, in an embodiment of the present invention, the weighting formula of the memory decay factor is:

[0072]

[0073] Among them, is the final predicted value after weighting, is the model predicted value at time , is the predicted value after weighting at the previous time, is that the weighted predicted value of the memory decay factor at the initial time adopts the actual comprehensive error at this time.

[0074] Optionally, in an embodiment of the present invention, the expression for applying the final predicted orbit clock error result to precise positioning is:

[0075]

[0076]

[0077] Among them, For the user receiver Received GNSS satellites At the frequency Pseudorange observations on the channel, For the receiver The received GNSS satellites At the said frequency Carrier phase observations, subscript Indicates the observed user receiver, For the user receiver The received GNSS satellites At the frequency Pseudorange observations on the channel, For the user receiver The received GNSS satellites At the frequency Carrier phase observations, Is the speed of light in vacuum, And Are the satellite and receiver clock biases respectively, Is the tropospheric delay projection function, Is the receiver zenith tropospheric delay, Is the line-of-sight ionospheric delay, Is the wavelength corresponding to the observation frequency, Is the integer ambiguity of the carrier phase observation in cycles, And Are the observation noises corresponding to the pseudorange and carrier observations.

[0078] An embodiment of the third aspect of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the PPP-B2b orbit clock bias error correction method as described in the above embodiment.

[0079] An embodiment of the fourth aspect of the present invention provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the program is executed by a processor, it implements the PPP-B2b orbit clock bias error correction method as above.

[0080] In the embodiments of the present invention, an error compensation model for PPP-B2b correction information can be established by adjusting the weights and bias parameters of the neural network, ultimately improving the PPP-B2b positioning accuracy. The neural network method in machine learning is creatively combined with the PPP-B2b technology to weaken the influence of spatial signal errors on the positioning accuracy. By utilizing the error characteristics of satellite orbits and clock offset corrections broadcast by PPP-B2b, the neural network is trained to establish an estimation model for the comprehensive errors of satellite orbits and clock offsets, improving the real-time precise point positioning accuracy. Thus, the problem that traditional error corrections rely on physical models and empirical formulas, ignoring the non-linear components in the error sequence and having limitations in dealing with abnormal signals or dynamic environmental changes is solved.

[0081] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the following description of the embodiments in conjunction with the accompanying drawings, where:

[0083] Figure 1 FIG. is a flowchart of a method for correcting PPP-B2b orbit and clock offset errors according to an embodiment of the present invention;

[0084] Figure 2 FIG. is a schematic flowchart of a method for correcting PPP-B2b orbit and clock offset errors according to an embodiment of the present invention;

[0085] Figure 3 FIG. is a schematic structural diagram of a device for correcting PPP-B2b orbit and clock offset errors according to an embodiment of the present invention;

[0086] Figure 4 FIG. is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0087] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0088] The PPP-B2b orbit clock error correction method and device according to the embodiments of the present invention will be described below with reference to the accompanying drawings. Aiming at the problem in the above-mentioned background art that traditional error correction relies on physical models and empirical formulas, ignores the non-linear components in the error sequence, and has limitations in dealing with abnormal signals or dynamic environmental changes, the present invention provides a PPP-B2b orbit clock error correction method. In this method, an error compensation model for PPP-B2b correction information can be established by adjusting the weights and bias parameters of the neural network, and finally the PPP-B2b positioning accuracy can be improved. The neural network method in machine learning is creatively combined with the PPP-B2b technology to weaken the influence of space signal errors on the positioning accuracy. By utilizing the error characteristics of satellite orbits and clock correction numbers broadcast by PPP-B2b, the neural network is trained to establish an estimation model for the comprehensive error of satellite orbits and clock, improve the real-time precise point positioning accuracy, and the neural network can continuously update the parameters to adapt to environmental changes in real time, improving the robustness of error correction. Thus, the problem that traditional error correction relies on physical models and empirical formulas, ignores the non-linear components in the error sequence, and has limitations in dealing with abnormal signals or dynamic environmental changes is solved.

[0089] Specifically, Figure 1 FIG. is a schematic flowchart of a PPP-B2b orbit clock error correction method provided by an embodiment of the present invention.

[0090] As Figure 1 shown, the PPP-B2b orbit clock error correction method includes the following steps:

[0091] In step S101, based on the Beidou navigation message and the PPP-B2b precise message of the Beidou-3 precise point positioning service, the PPP-B2b precise orbit and clock are restored to generate PPP-B2b restored data.

[0092] It can be understood that in the embodiments of the present invention, the broadcast ephemeris data, precise ephemeris data, and PPP-B2b messages are first used to preprocess the data, identify and remove the outliers in the observed data, and obtain the PPP-B2b precise ephemeris.

[0093] In the actual execution process, the embodiments of the present invention can perform PPP-B2b precise orbit restoration. By obtaining the Beidou navigation message and PPP-B2b precise correction information, the PPP-B2b precise orbit and clock are restored to generate PPP-B2b restored data for error modeling of the Beidou PPP-B2b orbit clock.

[0094] Among them, in one embodiment of the present invention, the restoration formula for the PPP-B2b precise orbit is:

[0095]

[0096]

[0097]

[0098]

[0099]

[0100] Among them, the superscript s represents the observed GNSS satellite, which are the unit vectors of the satellite in the radial, tangential, and normal directions in the Earth-centered Earth-fixed coordinate system, respectively, is the orbit correction broadcast by PPP-B2b, is the orbit correction vector in the ECEF coordinate system, and calculate the position and velocity vectors calculated from the broadcast ephemeris respectively, is the satellite position vector calculated from the broadcast ephemeris in the ECEF coordinate system, is the position vector of the PPP-B2b precise orbit recovered in the ECEF coordinate system;

[0101] The recovery formula for the PPP-B2b precise clock offset is:

[0102]

[0103] Among them, is the precise satellite clock offset recovered from the PPP-B2b correction information, is the satellite clock offset obtained from the broadcast ephemeris, is the PPP-B2b clock offset correction value, is the speed of light.

[0104] In step S102, based on the PPP-B2b recovery data, the PPP-B2b orbit clock offset error that satisfies the preset time condition is evaluated using a preset reference product to generate PPP-B2b orbit clock offset error data.

[0105] It can be understood that the preset reference product in the embodiments of the present invention can be the hourly updated orbit clock offset product provided by IGS; the preset time condition can be m hours (m>3) before the current moment.

[0106] In the actual execution process, the embodiments of the present invention can calculate the PPP-B2b space signal error. Based on the PPP-B2b recovery data, the hourly updated orbit clock offset product provided by IGS is obtained in real time, and it is used as the reference product to evaluate the PPP-B2b orbit clock offset error m hours (m>3) before the current moment.

[0107] Since the reference point of the PPP-B2b precise orbit is based on the antenna phase center (APC) and the reference orbit is based on the satellite centroid, it is necessary to correct the phase center offset (PCO).

[0108] Among them, in an embodiment of the present invention, the error formula of the PPP-B2b precise orbit relative to the reference orbit is:

[0109]

[0110] Among them, is the PPP-B2b orbit error vector, is the reference orbit vector, is the vector of the PPP-B2b precise orbit, is the transformation matrix from the satellite body-fixed coordinate system to the earth-fixed coordinate system, is the satellite PCO correction vector;

[0111] Considering that the time scale and frequency reference of the reference clock error product are different from the frequency points of the PPP-B2b satellite clock error, therefore, it is necessary to use the PPP-B2b satellite hardware delay to correct the clock error. The calculation formula of the PPP-B2b orbit clock error is:

[0112]

[0113]

[0114] Among them, is the satellite clock error of the PPP-B2b after correcting the DCB after the B1I / B3I IF combination, is the precise reference clock error, and are the frequencies of the B1I and B3I signals respectively, is the PPP-B2b satellite clock error.

[0115] In step S103, calculate the comprehensive error of the PPP-B2b precise orbit and clock error according to the PPP-B2b orbit clock error data, and adjust the neural network weights and configuration parameters according to the comprehensive error to generate adjustment data, and calculate the loss function value of the neural network training according to the adjustment data until the loss function value meets the preset convergence condition, and generate the training result of the preset error model.

[0116] It can be understood that the embodiment of the present invention can be based on the BP neural network algorithm, use the comprehensive error of the PPP-B2b orbit and clock error as the input value, initialize the weights, calculate the output value, compare it with the true value to calculate the error, construct a loss function, adjust the weights, and continuously train until the best result is obtained.

[0117] Among them, the embodiments of the present invention can establish an error model based on a BP neural network. Based on the BP (backpropagation) neural network method in machine learning technology, the comprehensive error is calculated using the PPP-B2b orbital clock error data in the previous step, trained and learned, and the network weights and configuration parameters are optimized through multiple iterations until the loss function value meets the preset convergence condition, generating the training result of the preset error model to improve the fitting accuracy of the model for errors. The following are the specific implementation steps:

[0118] Initial data acquisition and model training. Considering that the reference orbit and clock difference are updated hourly, the PPP-B2b orbit and clock difference are trained at the same frequency. At the same time, considering the calculation time consumption of the reference product, the rail clock comprehensive error is calculated using the PPP-B2b orbit and clock difference errors in the m - u hours before the current moment (where u is the delay time of the reference product, such as the hourly product is generally delayed by 1 hour).

[0119] Among them, in an embodiment of the present invention, the calculation formula for the comprehensive error is:

[0120]

[0121]

[0122] Among them, is the comprehensive error of the PPP-B2b orbit and clock difference, is the direction vector of the ECEF coordinate system, 、 、 are the satellite positions calculated by the broadcast ephemeris respectively, represents the geometric distance between the satellite and the earth's center;

[0123] For perform jump detection and repair to obtain a smooth time series as the input for neural network training. The expression of the loss function value is:

[0124]

[0125] Among them, is the predicted value, is the true value, is the quantity;

[0126] Then, the Adam optimizer is used to adjust the network weights. The initial learning rate is set to 0.001, and the learning rate is adaptively adjusted to accelerate convergence. BP backpropagation optimization is performed to determine whether the gradient in the Adam optimizer tends to zero. After each iteration, it is checked whether the value of the loss function decreases until the value of the loss function reaches the convergence condition. Among them, the expression for the value of the loss function reaching the preset convergence condition is:

[0127]

[0128] Among them, is a positive number, and are the values of the loss function after this round and the previous round of iteration, respectively.

[0129] In the embodiment of the present invention, the neural network method in machine learning is combined with the PPP-B2b technology to weaken the influence of spatial signal error on the positioning accuracy. By utilizing the error characteristics of satellite orbits and clock offset corrections broadcast by PPP-B2b, a neural network is trained to establish an estimation model for the comprehensive error of satellite orbits and clock offsets, thereby improving the real-time precise point positioning accuracy. Moreover, the neural network can adapt to environmental changes in real time by continuously updating parameters, improving the robustness of error correction.

[0130] Optionally, in an embodiment of the present invention, before generating the training result of the preset error model, it further includes: establishing a preset error model based on the preset backpropagation neural network algorithm based on the PPP-B2b orbit clock error.

[0131] In the actual execution process, the embodiment of the present invention can establish a preset error model based on the PPP-B2b orbit clock error, ultimately improving the PPP-B2b positioning accuracy. By applying artificial intelligence to the field of satellite navigation and combining with a neural network to achieve high-precision satellite orbit and clock error prediction, the neural network error modeling method can effectively capture and predict this complex relationship, improving the error correction accuracy and significantly enhancing the PPP-B2b positioning accuracy.

[0132] In step S104, based on the training result of the preset error model, the comprehensive error prediction value is weighted by the preset memory decay mechanism to generate the final prediction value.

[0133] It can be understood that the embodiment of the present invention innovatively introduces a memory decay mechanism to weight the comprehensive error prediction value, ultimately realizing the modeling of the comprehensive error of orbits and clock offsets. Then, using the trained error model, the real-time PPP-B2b orbit and the comprehensive error of the error are predicted to obtain the prediction value.

[0134] As a possible implementation, the embodiments of the present invention can perform PPP-B2b orbital clock error comprehensive error prediction. Based on the training results of the preset error model, a memory decay mechanism is innovatively introduced on the basis of the traditional BP algorithm. The memory decay mechanism is used to perform memory decay factor weighting on the comprehensive error prediction value to generate the final prediction value, so as to adapt to the environment of rapid error change. The present invention uses the trained model in the above steps to predict the comprehensive error of the satellite orbital clock after the current moment, and then performs memory decay factor weighting to obtain the final prediction value.

[0135] Among them, in an embodiment of the present invention, the weighting formula of the memory decay factor is:

[0136]

[0137] Among them, is the final prediction value after weighting, is the model prediction value at time , is the prediction value after weighting at the previous moment, is that the weighted prediction value at the initial moment of the memory decay factor adopts the actual comprehensive error at this moment.

[0138] In step S105, the PPP-B2b orbital clock error is corrected according to the final prediction value to generate the predicted orbital clock difference result of PPP-B2b.

[0139] In the actual execution process, the embodiments of the present invention can perform PPP-B2b precise point positioning solution. The PPP-B2b orbital clock error is corrected according to the final prediction value to generate the predicted orbital clock difference result of PPP-B2b. The embodiments of the present invention introduce the comprehensive error prediction value into the precise positioning model for positioning correction, so as to improve the real-time precise positioning performance at the current moment.

[0140] Among them, in an embodiment of the present invention, on the basis of the positioning model, the comprehensive error prediction value is introduced for correction, and the expression of the predicted orbital clock difference result is:

[0141]

[0142]

[0143] Among them, is the user receiver receives the GNSS satellite at the frequency on the channel of the pseudorange observation value, is the receiver receives the GNSS satellite at the frequency The carrier phase observation value, with the subscript represents the observed user receiver, is the user receiver receives the GNSS satellite at the frequency on the channel of the pseudorange observation value, is the user receiver receives the GNSS satellite at the frequency of the carrier phase observation value, is the speed of light in vacuum, and are the satellite and receiver clock biases respectively, is the tropospheric delay projection function, is the receiver zenith tropospheric delay, is the ionospheric delay in the line-of-sight direction, is the wavelength corresponding to the observation frequency, is the integer ambiguity of the carrier phase observation value in weeks, and are the observation noises corresponding to the pseudorange and carrier observations.

[0144] The embodiment of the present invention uses the Kalman filtering method for parameter estimation, and finally obtains the user position after real-time orbit error correction.

[0145] Specifically, it can be combined with Figure 2 as shown, to elaborate in detail on the working principle of the PPP-B2b orbit clock bias error correction method in the embodiment of the present invention with a specific embodiment.

[0146] As Figure 2 shown, the embodiment of the present invention may include the following steps:

[0147] Step S201: Obtain observation data, precise ephemeris, and PPP-B2b message.

[0148] Step S202: PPP-B2b precise orbit restoration.

[0149] Step S203: PPP-B2b comprehensive error calculation.

[0150] Step S204: Input the comprehensive error sample.

[0151] Step S205: Train the error model with the BP algorithm.

[0152] Step S206: Calculate the loss function.

[0153] Step S207: Determine whether it converges. If so, execute Step S208; if not, execute Step S209.

[0154] Step S208: PPP-B2b comprehensive error prediction.

[0155] Step S209: Update the weights.

[0156] Step S210: Memory decay factor weighting.

[0157] Step S211: PPP-B2b precise positioning solution.

[0158] Step S212: Output the predicted orbital clock error result.

[0159] According to the PPP-B2b orbital clock error correction method proposed by the embodiments of the present invention, an error compensation model for PPP-B2b correction information can be established by adjusting the weights and bias parameters of the neural network, and finally the PPP-B2b positioning accuracy can be improved. The neural network method in machine learning is creatively combined with the PPP-B2b technology to weaken the influence of space signal errors on the positioning accuracy. By utilizing the error characteristics of satellite orbits and clock correction numbers broadcast by PPP-B2b, the neural network is trained to establish an estimation model for the comprehensive error of satellite orbits and clock differences, improving the real-time precise point positioning accuracy. Thus, the problem that traditional error correction relies on physical models and empirical formulas, ignores the non-linear components in the error sequence, and has limitations in dealing with abnormal signals or dynamic environmental changes is solved.

[0160] Next, describe the PPP-B2b orbital clock error correction device proposed by the embodiments of the present invention with reference to the accompanying drawings.

[0161] Figure 3 It is a schematic structural diagram of the PPP-B2b orbital clock error correction device according to the embodiments of the present invention.

[0162] As Figure 3 shown, the PPP-B2b orbital clock error correction device 10 includes: a recovery module 100, an evaluation module 200, a calculation module 300, a weighting module 400, and a correction module 500.

[0163] Specifically, the recovery module 100 is used to recover the PPP-B2b precise orbit and clock based on the Beidou navigation message and the Beidou-3 precise point positioning PPP-B2b precise message to generate PPP-B2b recovery data.

[0164] The evaluation module 200 is used to evaluate the PPP-B2b orbital clock error that meets the preset time condition based on the PPP-B2b recovery data by using a preset reference product to generate PPP-B2b orbital clock error data.

[0165] A calculation module 300 is configured to calculate the comprehensive error of the PPP-B2b precise orbit and clock offset based on the PPP-B2b orbit clock offset error data, adjust the neural network weights and configuration parameters according to the comprehensive error to generate adjustment data, and calculate the loss function value of the neural network training based on the adjustment data until the loss function value meets the preset convergence condition, and then generate the training result of the preset error model.

[0166] A weighting module 400 is configured to perform memory decay factor weighting on the comprehensive error prediction value based on the training result of the preset error model by using a preset memory decay mechanism to generate a final prediction value.

[0167] A correction module 500 is configured to correct the PPP-B2b orbit clock offset according to the final prediction value to generate a predicted orbit clock offset result of PPP-B2b.

[0168] Optionally, in an embodiment of the present invention, the PPP-B2b orbit clock offset correction device 10 further includes: a building module.

[0169] Wherein, the building module is configured to build a preset error model based on the PPP-B2b orbit clock offset error before generating the training result of the preset error model, and the preset error model is based on a preset backpropagation neural network algorithm.

[0170] Optionally, in an embodiment of the present invention, the restoration formula of the PPP-B2b precise orbit is:

[0171]

[0172]

[0173]

[0174]

[0175]

[0176] Wherein, the superscript s represents the observed GNSS satellite, are the unit vectors of the satellite in the radial, tangential, and normal directions in the earth-fixed coordinate system respectively, is the orbit correction number broadcast by PPP-B2b, is the orbit correction number vector in the ECEF coordinate system, and calculate the position and velocity vectors calculated from the broadcast ephemeris respectively, is the satellite position vector calculated from the broadcast ephemeris in the ECEF coordinate system, is the position vector of the PPP-B2b precise orbit recovered in the ECEF coordinate system;

[0177] The recovery formula for the PPP-B2b precise clock offset is:

[0178]

[0179] where, is the precise satellite clock offset recovered from the PPP-B2b correction information, is the satellite clock offset obtained from the broadcast ephemeris, is the PPP-B2b clock offset correction value, is the speed of light.

[0180] Optionally, in an embodiment of the present invention, the error formula of the PPP-B2b precise orbit relative to the reference orbit is:

[0181]

[0182] where, is the PPP-B2b orbit error vector, is the reference orbit vector, is the vector of the PPP-B2b precise orbit, is the transformation matrix from the satellite body-fixed coordinate system to the Earth-fixed coordinate system, is the satellite PCO correction vector;

[0183] The calculation formula for the PPP-B2b orbit clock offset error is:

[0184]

[0185]

[0186] where, is the satellite clock offset of the PPP-B2b after correcting the DCB in the B1I / B3I IF combination, is the precise reference clock offset, and are the frequencies of the B1I and B3I signals respectively, is the PPP-B2b satellite clock offset error.

[0187] Optionally, in an embodiment of the present invention, the calculation formula for the comprehensive error is:

[0188]

[0189]

[0190] where, is the comprehensive error of PPP-B2b orbit and clock error, is the direction vector in the ECEF coordinate system, 、 、 are the satellite positions calculated from the broadcast ephemeris respectively, represents the geometric distance between the satellite and the earth's center;

[0191] The expression of the loss function value is:

[0192]

[0193] where, is the predicted value, is the true value, is the quantity;

[0194] The expression for the loss function value to reach the preset convergence condition is:

[0195]

[0196] where, is a positive number, and are the loss function values after this round and the previous round of iteration respectively.

[0197] Optionally, in an embodiment of the present invention, the weighted formula of the memory decay factor is:

[0198]

[0199] where, is the final predicted value after weighting, is the model predicted value at time , is the predicted value after weighting at the previous time, is the actual comprehensive error at the initial time of the memory decay factor weighted predicted value.

[0200] Optionally, in an embodiment of the present invention, the precise positioning correction expression of the final predicted orbit clock error result is:

[0201]

[0202]

[0203] where, is the user receiver receives the GNSS satellite on the channel of frequency pseudorange observation value, is the receiver Received GNSS satellites At frequency The carrier phase observation value, the subscript Indicates the observed user receiver, Is the user receiver Received GNSS satellites At frequency The pseudorange observation value on the channel, Is the user receiver Received GNSS satellites At frequency The carrier phase observation value, Is the speed of light in vacuum, And Are the satellite and receiver clock biases respectively, Is the tropospheric delay projection function, Is the receiver zenith tropospheric delay, Is the ionospheric delay in the line-of-sight direction, Is the wavelength corresponding to the observation frequency, Is the integer ambiguity of the carrier phase observation value in cycles, And Are the observation noises corresponding to the pseudorange and carrier observations.

[0204] It should be noted that the foregoing explanation of the PPP-B2b orbit clock bias error correction method embodiment also applies to the PPP-B2b orbit clock bias error correction device of this embodiment, and will not be repeated here.

[0205] According to the PPP-B2b orbit clock bias error correction device proposed in the embodiments of the present invention, using the satellite orbit correction number, satellite clock bias correction number and DCB correction number provided by PPP-B2b, as well as the satellite precise ephemeris reference value, the PPP-B2b data is restored, the comprehensive orbit and clock bias error is calculated, and it is trained, and finally the PPP-B2b positioning accuracy is improved and the robustness of error correction is improved. Thus, the problem that traditional error correction depends on physical models and empirical formulas, ignores the non-linear components in the error sequence, and has limitations in dealing with abnormal signals or dynamic environment changes is solved.

[0206] Figure 4 Is the structural schematic diagram of the electronic device provided by the embodiments of the present invention. The electronic device may include:

[0207] A memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402.

[0208] When the processor 402 executes the program, it implements the PPP-B2b orbit clock bias error correction method provided in the above embodiments.

[0209] Furthermore, the electronic device further includes:

[0210] A communication interface 403, for communication between the memory 401 and the processor 402.

[0211] A memory 401, for storing a computer program that can run on the processor 402.

[0212] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0213] If the memory 401, the processor 402, and the communication interface 403 are implemented independently, the communication interface 403, the memory 401, and the processor 402 can be interconnected through a bus and complete communication with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 4 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0214] Optionally, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a chip, the memory 401, the processor 402, and the communication interface 403 can complete communication with each other through an internal interface.

[0215] The processor 402 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0216] This embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the PPP-B2b orbit clock error correction method as described above is implemented.

[0217] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0218] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0219] Any process or method description shown in a flowchart or described in other ways herein can be understood as representing a module, segment, or portion of code including one or N executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0220] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0221] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0222] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0223] In addition, each functional unit in various embodiments of the present invention may be integrated into one processing module, may exist separately as individual physical units, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0224] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A PPP-B2b orbital clock error correction method, characterized in that, Including the following steps: Based on the Beidou navigation message and the PPP-B2b precise message of the precise point positioning service of Beidou-3, restore the PPP-B2b precise orbit and clock offset to generate PPP-B2b restoration data; Based on the PPP-B2b restoration data, use a preset reference product to evaluate the PPP-B2b orbit clock offset error that meets the preset time condition to generate PPP-B2b orbit clock offset error data; Calculate the comprehensive error of the PPP-B2b precise orbit and the clock offset according to the PPP-B2b orbit clock offset error data, and adjust the neural network weights and configuration parameters according to the comprehensive error to generate adjustment data, and calculate the loss function value of the neural network training according to the adjustment data until the loss function value meets the preset convergence condition, generate the training result of the preset error model; Based on the training result of the preset error model, use a preset memory decay mechanism to perform memory decay factor weighting processing on the comprehensive error prediction value to generate a final prediction value; Correct the PPP-B2b orbit clock offset error according to the final prediction value to generate the predicted orbit clock offset result of PPP-B2b.

2. The PPP-B2b orbital clock error correction method according to claim 1, wherein Before generating the training result of the preset error model, it further includes: Based on the PPP-B2b orbit clock offset error, establish the preset error model based on the preset backpropagation neural network algorithm.

3. The PPP-B2b orbital clock error correction method according to claim 1, wherein The restoration formula of the PPP-B2b precise orbit is: Among them, the superscript s represents the observed GNSS satellite, which are the unit vectors of the satellite's radial, tangential, and normal directions in the Earth-fixed coordinate system, respectively, is the orbit correction broadcast by PPP-B2b, is the orbit correction vector in the ECEF coordinate system, and calculate the position and velocity vectors calculated from the broadcast ephemeris, respectively, is the satellite position vector calculated from the broadcast ephemeris in the ECEF coordinate system, is the position vector of the PPP-B2b precise orbit recovered in the ECEF coordinate system; The restoration formula of the PPP-B2b precise clock offset is: wherein, is the precise satellite clock error restored from the PPP-B2b correction information, is the satellite clock error obtained from the broadcast ephemeris, is the PPP-B2b clock error correction value, is the speed of light.

4. The PPP-B2b orbital clock error correction method according to claim 3, wherein, The error formula of the PPP-B2b precise orbit relative to the reference orbit is: Among them, is the PPP-B2b orbit error vector, is the reference orbit vector, is the vector of the PPP-B2b precise orbit, is the transformation matrix from the satellite body-fixed coordinate system to the Earth-fixed coordinate system, is the satellite PCO correction vector; The calculation formula of the PPP-B2b orbit clock offset error is: Among them, is the satellite clock error of the PPP-B2b to correct the DCB after the B1I / B3I IF combination, is the precise reference clock error, and are the frequencies of the B1I and B3I signals respectively, is the PPP-B2b satellite clock error.

5. The PPP-B2b orbital clock error correction method according to claim 4, wherein The calculation formula of the comprehensive error is: wherein, is the combined error of the PPP-B2b orbit and the clock error, is the direction vector of the ECEF coordinate system, , , are respectively the satellite positions calculated from the broadcast ephemeris, represents the geometric distance between the satellite and the earth's center; The expression of the loss function value is: Among them, is the predicted value, is the true value, is the quantity; The expression that the loss function value reaches the preset convergence condition is: wherein, is a positive number, and are the loss function values after this round and the previous round of iteration, respectively.

6. The PPP-B2b orbital clock error correction method according to claim 1, characterized in that, The weighting formula of the memory decay factor is: Among them, is the final predicted value after weighting, is the model predicted value at time , is the predicted value after weighting at the previous time, is the weighted predicted value at the initial moment of the memory decay factor, and the actual comprehensive error at this moment is adopted.

7. The PPP-B2b orbital clock error correction method according to claim 1, characterized in that The expression of applying the predicted orbit clock offset result to precise positioning is: Wherein, is the user receiver pseudorange observations on the channel of the received GNSS satellite at frequency , is the carrier phase observation of the GNSS satellite received by the receiver at the frequency , the subscript represents the observed user receiver is the pseudorange observation on the channel of the GNSS satellite received by the user receiver at frequency , is the carrier phase observation of the GNSS satellite received by the user receiver at frequency , is the speed of light in vacuum and are the satellite and receiver clock biases respectively is the tropospheric delay projection function is the receiver zenith tropospheric delay is the line-of-sight ionospheric delay is the wavelength corresponding to the observation frequency is the integer ambiguity of the carrier phase observation in cycles and are the observation noises corresponding to the pseudorange and carrier observations 8. A PPP-B2b orbital clock error correction device, characterized in that Adopting the PPP-B2b orbit clock offset error correction method according to any one of claims 1-7, including: A restoration module, configured to restore the PPP-B2b precise orbit and clock offset based on the Beidou navigation message and the PPP-B2b precise message of the precise point positioning service of Beidou-3 to generate PPP-B2b restoration data; An evaluation module, configured to evaluate the PPP-B2b orbit clock offset error that meets the preset time condition by using a preset reference product based on the PPP-B2b restoration data to generate PPP-B2b orbit clock offset error data; A calculation module, configured to calculate the comprehensive error of the PPP-B2b precise orbit and the clock offset according to the PPP-B2b orbit clock offset error data, and adjust the neural network weights and configuration parameters according to the comprehensive error to generate adjustment data, and calculate the loss function value of the neural network training according to the adjustment data until the loss function value meets the preset convergence condition, generate the training result of the preset error model; A weighting module, configured to perform memory decay factor weighting processing on a comprehensive error prediction value by using a preset memory decay mechanism based on a training result of the preset error model, so as to generate a final prediction value; A correction module, configured to correct the PPP-B2b orbit clock error according to the final prediction value, so as to generate a predicted orbit clock result of PPP-B2b.

9. An electronic device, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the PPP-B2b orbit clock error correction method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to be used for implementing the PPP-B2b orbit clock error correction method according to any one of claims 1-7.

Images