PPP-B2b orbital clock error correction method and device

Abstract

The invention relates to the cross technical field of BDS-3 precision single point positioning service and machine learning fusion, in particular to a PPP-B2b track clock error correction method and device, and the method comprises the steps: recovering a PPP-B2b precision track and clock error based on a Beidou navigation message and a Beidou No.3 precision single point positioning service PPP-B2b precision message; adjusting the weight and configuration parameters of the neural network; performing memory attenuation factor weighting processing on the comprehensive error predicted value by using a preset memory attenuation mechanism; and correcting the PPP-B2b orbital clock error according to the final predicted value. According to the method, the weight and the bias parameter of the neural network are adjusted, a neural network method in machine learning is combined with a PPP-B2b technology, an error compensation model of PPP-B2b correction information is established, the influence of space signal errors on the positioning precision is weakened, and finally the real-time precision single-point positioning precision is improved.

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 Art

[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 positioning accuracies of dynamic decimeter - level and static centimeter - level.

[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 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 environmental changes.

[0006] An embodiment of the first aspect of the present invention 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 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: Establishing the preset error model based on the preset backpropagation neural network algorithm based on the PPP-B2b orbit clock error.

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

[0009]

[0010]

[0011]

[0012]

[0013] Among them, 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, The position vector of the PPP-B2b precise orbit restored in the ECEF coordinate system; The restoration formula of the PPP-B2b precise clock offset is:

[0014] Wherein, 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.

[0015] Optionally, in an embodiment of the present invention, the error formula of the PPP-B2b precise orbit relative to the reference orbit is:

[0016] Wherein, 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:

[0017]

[0018] Wherein, 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.

[0019] Optionally, in an embodiment of the present invention, the calculation formula of the comprehensive error is:

[0020]

[0021] Wherein, is the comprehensive error of the PPP-B2b orbit and the clock offset, is the direction vector of the ECEF coordinate system, 、 , are the satellite positions calculated from the broadcast ephemeris, representing the geometric distance between the satellite and the earth's center; The expression for the loss function value is:

[0022] where, is the predicted value, is the true value, is the quantity; The expression for the loss function value reaching the preset convergence condition is:

[0023] where, is a positive number, and are the loss function values after this round and the previous round of iteration, respectively.

[0024] Optionally, in an embodiment of the present invention, the weighting formula for the memory decay factor is:

[0025] 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 this time for the weighted predicted value of the memory decay factor at the initial time.

[0026] Optionally, in an embodiment of the present invention, the expression for applying the final predicted orbital clock error result to precise positioning is:

[0027]

[0028] where, is the user receiver receiving the GNSS satellite on the channel at frequency of the pseudorange observation value, is the receiver receiving the GNSS satellite at the frequency of the carrier phase observation value, and the subscript represents the observed user receiver, is the user receiver receiving the GNSS satellite The pseudorange observation value on the channel at frequency , for the user receiver receiving the GNSS satellite at frequency The carrier phase observation value . is the speed of light in vacuum, and are respectively the satellite and receiver clock biases, 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.

[0029] In the second aspect of the embodiments of the present invention, a PPP-B2b orbit clock bias error correction device is provided, including: a recovery module, configured to recover the PPP-B2b precise orbit and clock bias based on the Beidou navigation message and the Beidou-3 precise point positioning (PPP-B2b) precise message to generate PPP-B2b recovery data; an evaluation module, configured to evaluate the PPP-B2b orbit clock bias error that meets the preset time condition by using a preset reference product based on the PPP-B2b recovery data to generate PPP-B2b orbit clock bias error data; a calculation module, configured to calculate the comprehensive error of the PPP-B2b precise orbit and the clock bias according to the PPP-B2b orbit clock bias 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 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 to generate a final prediction value; a correction module, configured to correct the PPP-B2b orbit clock bias error according to the final prediction value to generate the predicted orbit clock bias result of PPP-B2b.

[0030] 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 based on the PPP-B2b orbit clock bias error before generating the training result of the preset error model.

[0031] Optionally, in an embodiment of the present invention, the recovery formula of the PPP-B2b precise orbit is:

[0032]

[0033]

[0034]

[0035]

[0036] where 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 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 recovered PPP-B2b precise orbit in the ECEF coordinate system; The recovery formula of the PPP-B2b precise clock offset is:

[0037] 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.

[0038] Optionally, in an embodiment of the present invention, the error formula of the PPP-B2b precise orbit relative to the reference orbit is:

[0039] 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; The calculation formula of the PPP-B2b orbit clock offset error is:

[0040]

[0041] Among them, is the satellite clock error that corrects the DCB after the PPP-B2b in 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.

[0042] Optionally, in an embodiment of the present invention, the calculation formula of the comprehensive error is:

[0043]

[0044] 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 by the broadcast ephemeris respectively, represents the geometric distance between the satellite and the earth's center; The expression of the loss function value is:

[0045] Among them, is the predicted value, is the true value, is the quantity; The expression for the loss function value to reach the preset convergence condition is:

[0046] Among them, is a positive number, and are the loss function values after this round and the previous round of iteration respectively.

[0047] Optionally, in an embodiment of the present invention, the weighted formula of the memory decay factor is:

[0048] Among them, is the final predicted value after weighting, is the model predicted value at time , is the weighted predicted value at the previous moment, is the weighted predicted value of the memory decay factor at the initial moment, and the actual comprehensive error at this moment is adopted.

[0049] Optionally, in an embodiment of the present invention, the expression for applying the final predicted orbital clock error result to precise positioning is:

[0050]

[0051] wherein, 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 of the carrier phase observation value, and 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 end clock errors 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 value in units of weeks, and are the observation noises corresponding to the pseudorange and carrier observations.

[0052] 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 orbital clock error correction method as described in the above embodiment.

[0053] In a fourth aspect embodiment of the present invention, a computer-readable storage medium is provided. 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 error correction method as described above.

[0054] Embodiments of the present invention can establish an error compensation model for PPP-B2b correction information by adjusting the weights and bias parameters of a neural network, ultimately improving the PPP-B2b positioning accuracy. It creatively combines the neural network method in machine learning with PPP-B2b technology to weaken the influence of space signal errors on positioning accuracy. By utilizing the error characteristics of satellite orbits and clock correction numbers broadcast by PPP-B2b, a 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, it solves 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.

[0055] 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. Description of the Drawings

[0056] The above and / or additional aspects and advantages of the present invention will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, where: Figure 1 FIG. is a flowchart of a PPP-B2b orbit clock error correction method provided according to an embodiment of the present invention; Figure 2 FIG. is a schematic flowchart of a PPP-B2b orbit clock error correction method according to an embodiment of the present invention; Figure 3 FIG. is a schematic structural diagram of a PPP-B2b orbit clock error correction device provided according to an embodiment of the present invention; Figure 4 FIG. is a schematic structural diagram of an electronic device provided according to an embodiment of the present invention. Detailed Embodiments

[0057] The embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0058] 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. In view of 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 using 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 clocks, 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.

[0059] Specifically, Figure 1 FIG. is a schematic flow chart of a PPP-B2b orbit clock error correction method provided by an embodiment of the present invention.

[0060] As Figure 1 shown, the PPP-B2b orbit clock error correction method includes the following steps: 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 restoration data.

[0061] 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 eliminate the outliers in the observed data, and obtain the PPP-B2b precise ephemeris.

[0062] 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 restoration data, so as to perform error modeling on the Beidou PPP-B2b orbit clock.

[0063] Among them, in an embodiment of the present invention, the restoration formula of the PPP-B2b precise orbit is:

[0064]

[0065]

[0066]

[0067]

[0068] 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-centered inertial 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; The recovery formula for the PPP-B2b precise clock offset is:

[0069] 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.

[0070] In step S102, based on the PPP-B2b recovery data, the PPP-B2b orbit clock offset error that meets the preset time condition is evaluated by using a preset reference product to generate PPP-B2b orbit clock offset error data.

[0071] 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.

[0072] 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 a reference product to evaluate the PPP-B2b orbit clock offset error m hours (m > 3) before the current moment.

[0073] 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, the phase center offset (PCO) correction needs to be carried out.

[0074] Among them, in one embodiment of the present invention, the error formula of the PPP-B2b precise orbit relative to the reference orbit is:

[0075] 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; 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:

[0076]

[0077] Among them, is the satellite clock error of 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.

[0078] 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.

[0079] 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 the loss function, adjust the weights, and continuously train until the best result is obtained.

[0080] Among them, the embodiments of the present invention can establish an error model based on the 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: 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).

[0081] Among them, in one embodiment of the present invention, the calculation formula for the comprehensive error is:

[0082]

[0083] 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; Perform jump detection and repair on to obtain the smoothed time series as the input for neural network training. The expression of the loss function value is:

[0084] Among them, is the predicted value, is the true value, is the quantity; 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, check whether the value of the loss function decreases until the loss function value reaches the convergence condition. Among them, the expression for the loss function value to reach the preset convergence condition is:

[0085] Among them, is a positive number, and are the loss function values after this round and the previous round of iteration respectively.

[0086] 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, enhancing the robustness of error correction.

[0087] 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.

[0088] 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, applying artificial intelligence to the field of satellite navigation, combining neural networks to achieve high-precision satellite orbit and clock 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.

[0089] In step S104, based on the training result of the preset error model, the comprehensive error prediction value is weighted by the memory decay factor using the preset memory decay mechanism to generate the final prediction value.

[0090] It can be understood that the embodiment of the present invention innovatively introduces the memory decay mechanism, weights the comprehensive error prediction value, and finally realizes 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.

[0091] As a possible implementation manner, the embodiment of the present invention can perform the prediction of the PPP-B2b orbit clock comprehensive error. Based on the training result of the preset error model, on the basis of the traditional BP algorithm, the memory decay mechanism is innovatively introduced, and the memory decay mechanism is used to weight the comprehensive error prediction value by the memory decay factor to generate the final prediction value, adapting to the environment where the error changes rapidly. The present invention uses the model trained in the above steps to predict the comprehensive error of the satellite orbit clock after the current moment, and then performs the weighting of the memory decay factor to obtain the final prediction value.

[0092] Among them, in an embodiment of the present invention, the weighting formula of the memory decay factor is:

[0093] 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 of the memory decay factor at the initial time, and the actual comprehensive error at this time is adopted.

[0094] In step S105, the PPP-B2b orbit clock error is corrected according to the final predicted value to generate the predicted orbit clock result of PPP-B2b.

[0095] In the actual execution process, the embodiment of the present invention can perform PPP-B2b precise point positioning solution. The PPP-B2b orbit clock error is corrected according to the final predicted value to generate the predicted orbit clock result of PPP-B2b. The embodiment of the present invention introduces the comprehensive error predicted value into the precise positioning model for positioning correction, and improves the real-time precise positioning performance at the current time.

[0096] 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 orbit clock result is:

[0097]

[0098] Among them, is the pseudorange observation value of the GNSS satellite received by the user receiver on the channel at frequency , is the carrier phase observation value of the GNSS satellite received by the receiver on the channel at frequency , and the subscript represents the observed user receiver, is the pseudorange observation value of the GNSS satellite received by the user receiver on the channel at frequency , is the carrier phase observation value of the GNSS satellite received by the user receiver on the channel 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 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 in cycles, and are the observation noises corresponding to the pseudorange and carrier observations.

[0099] In the embodiments of the present invention, the Kalman filtering method is used for parameter estimation, and finally the user position after real-time orbit error correction is obtained.

[0100] Specifically, as shown in Figure 2 , the working principle of the PPP-B2b orbit clock bias error correction method in the embodiments of the present invention will be elaborated in detail with a specific embodiment.

[0101] As Figure 2 shown, the embodiments of the present invention may include the following steps: Step S201: Obtain observation data, precise ephemeris, and PPP-B2b message.

[0102] Step S202: PPP-B2b precise orbit recovery.

[0103] Step S203: PPP-B2b comprehensive error calculation.

[0104] Step S204: Input comprehensive error samples.

[0105] Step S205: Train the error model with the BP algorithm.

[0106] Step S206: Calculate the loss function.

[0107] Step S207: Determine whether to converge. If so, execute Step S208; if not, execute Step S209.

[0108] Step S208: PPP-B2b comprehensive error prediction.

[0109] Step S209: Update the weights.

[0110] Step S210: Weighted by the memory decay factor.

[0111] Step S211: PPP-B2b precise positioning solution.

[0112] Step S212: Output the predicted orbit clock bias result.

[0113] According to the PPP-B2b orbital clock error correction method proposed by the embodiments of the present invention, by adjusting the neural network weights and bias parameters, an error compensation model for PPP-B2b correction information can be established, ultimately improving the PPP-B2b positioning accuracy. It creatively combines the neural network method in machine learning with PPP-B2b technology to weaken the influence of space signal errors on 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 errors of satellite orbits and clock differences, thereby 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.

[0114] Next, a PPP-B2b orbital clock error correction device proposed according to the embodiments of the present invention is described with reference to the accompanying drawings.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The calculation module 300 is used to calculate the comprehensive error of the PPP-B2b precise orbit and clock according to the PPP-B2b orbital clock 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 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.

[0120] The weighting module 400 is used to perform memory decay factor weighting processing 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.

[0121] Correction module 500, configured to correct the PPP-B2b orbit clock error according to the final prediction value to generate a predicted orbit clock result of PPP-B2b.

[0122] Optionally, in an embodiment of the present invention, the PPP-B2b orbit clock error correction device 10 further includes: a building module.

[0123] Wherein, the building module is configured to build a preset error model based on the preset backpropagation neural network algorithm based on the PPP-B2b orbit clock error before generating the training result of the preset error model.

[0124] Optionally, in an embodiment of the present invention, the restoration formula of the PPP-B2b precise orbit is:

[0125]

[0126]

[0127]

[0128]

[0129] 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 restored in the ECEF coordinate system; The restoration formula of the PPP-B2b precise clock difference is:

[0130] Wherein, is the precise satellite clock difference restored from the PPP-B2b correction information, is the satellite clock difference obtained from the broadcast ephemeris, is the PPP-B2b clock difference correction value, is the speed of light.

[0131] Optionally, in an embodiment of the present invention, the error formula of the PPP-B2b precise orbit relative to the reference orbit is:

[0132] Among them, is the PPP-B2b orbital error vector, is the reference orbital 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 for the PPP-B2b orbital clock error is:

[0133]

[0134] Among them, is the satellite clock error that corrects the DCB after the PPP-B2b in 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.

[0135] Optionally, in an embodiment of the present invention, the calculation formula for the comprehensive error is:

[0136]

[0137] Among them, is the comprehensive error of the PPP-B2b orbit and clock, 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; The expression of the loss function value is:

[0138] Among them, is the predicted value, is the true value, is the quantity; The expression for the loss function value to reach the preset convergence condition is:

[0139] Among them, is a positive number, and They are the loss function values after this round and the previous round of iteration respectively.

[0140] Optionally, in an embodiment of the present invention, the weighting formula of the memory decay factor is:

[0141] Wherein, 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 prediction value, which is adopted at this time.

[0142] Optionally, in an embodiment of the present invention, the precise positioning correction expression of the final predicted orbit clock error result is:

[0143]

[0144] Wherein, is the pseudo-range observation value of the GNSS satellite received by the user receiver on the channel at frequency , is the carrier phase observation value of the GNSS satellite received by the receiver on the channel at frequency . The subscript represents the observed user receiver, is the pseudo-range observation value of the GNSS satellite received by the user receiver on the channel at frequency , is the carrier phase observation value of the GNSS satellite received by the user receiver on the channel at frequency , is the speed of light in vacuum, and are the satellite and receiver end clock errors 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 units of weeks, and are the observation noises corresponding to the pseudo-range and carrier observations.

[0145] It should be noted that the foregoing explanatory description of the PPP-B2b orbit clock error correction method embodiments is also applicable to the PPP-B2b orbit clock error correction device of this embodiment, and will not be elaborated here.

[0146] The PPP-B2b orbit clock error correction device proposed according to the embodiments of the present invention uses the satellite orbit correction number, satellite clock error correction number, and DCB correction number provided by PPP-B2b, as well as the satellite precise ephemeris reference value to restore PPP-B2b data, calculate the comprehensive orbit and clock error, train it, and ultimately improve the PPP-B2b positioning accuracy and 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.

[0147] Figure 4 The structural schematic diagram of the electronic device provided by the embodiments of the present invention. The electronic device may include: A memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402.

[0148] When the processor 402 executes the program, it implements the PPP-B2b orbit clock error correction method provided in the foregoing embodiments.

[0149] Further, the electronic device further includes: A communication interface 403 for communication between the memory 401 and the processor 402.

[0150] The memory 401 is used to store a computer program executable on the processor 402.

[0151] 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.

[0152] 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 via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 only a thick line is used in Figure 4 , but it does not mean that there is only one bus or one type of bus.

[0153] Optionally, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a single chip, the memory 401, the processor 402, and the communication interface 403 can communicate with each other via an internal interface.

[0154] 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.

[0155] 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.

[0156] In the description of this specification, the descriptions with reference to the terms "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 representations 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 conflict, 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.

[0157] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, 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.

[0158] Any process or method description represented in a flowchart or described otherwise herein may be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logic function or process. The scope of the preferred embodiments of the present invention includes additional implementations, where functions may 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.

[0159] Logic and / or steps represented in a flowchart or described otherwise herein, for example, may be considered as a sequenced list of executable instructions for implementing a logical function and may be specifically implemented in any computer-readable medium for use by or in connection with 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. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion having one or N wirings (electronic device), a portable computer diskette (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 may even be paper or other suitable medium on which the program can be printed, as the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0160] 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 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 by 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 appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0161] Those of ordinary skill in the art can understand that all or part of the steps carried by the method 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.

[0162] In addition, each functional unit in various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0163] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disk, or the like. 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 orbit clock error correction method, characterized in that: The following steps are involved: Based on the BeiDou navigation message and the BeiDou-3 precise point positioning service PPP-B2b precise message, restore the PPP-B2b precise orbit and clock error to generate PPP-B2b recovery data; Based on the PPP-B2b restored data, a PPP-B2b orbit clock error satisfying a preset time condition is evaluated using a preset reference product to generate PPP-B2b orbit clock error data; Calculate the comprehensive error of the PPP-B2b precise orbit and the clock error according to the PPP-B2b orbit clock error data, adjust the weights and configuration parameters of the neural network 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; Based on the training result of the preset error model, a preset memory decay mechanism is used to perform a memory decay factor weighted processing on the comprehensive error prediction value to generate a final prediction value; The PPP-B2b orbit clock error is corrected according to the final predicted value to generate a predicted orbit clock error result of PPP-B2b.

2. The PPP-B2b orbit clock error correction method according to claim 1, characterized in that: Before generating the training results of the preset error model, it also includes: Based on the PPP-B2b orbit clock error, the preset error model based on the preset back propagation neural network algorithm is established.

3. The PPP-B2b orbit clock error correction method according to claim 1, characterized in that: The recovery formula of the PPP-B2b precise orbit is: 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, is the orbit correction number 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 restored in the ECEF coordinate system; The recovery formula of the PPP-B2b precise clock error is: in, is the precise satellite clock error recovered from the PPP-B2b correction information, is the satellite clock error obtained from the broadcast ephemeris, is the PPP-B2b clock correction value, The speed of light.

4. The PPP-B2b orbit clock error correction method according to claim 3, characterized in that: The error formula of the PPP-B2b precision orbit relative to the reference orbit is: in, is the PPP-B2b orbit error vector, is the reference orbit vector, is the vector of the PPP-B2b precision orbit, is the transformation matrix from the satellite fixed system to the ground fixed system, is the satellite PCO correction vector; The calculation formula of the PPP-B2b orbit clock error is: in, Correct the DCB satellite clock error after B1I / B3I IF combination for PPP-B2b, is the precise reference clock error, and are the frequencies of the B1I and B3I signals, is the PPP-B2b satellite clock error.

5. The PPP-B2b orbit clock error correction method according to claim 4, characterized in that: The calculation formula of the comprehensive error is: in, is the combined error of the PPP-B2b orbit and the clock error, is the direction vector of the ECEF coordinate system, , , are the satellite positions calculated for the broadcast ephemeris, Represents the geometric distance between the satellite and the center of the earth; The expression of the loss function value is: in, is the predicted value, is the true value, for quantity; The expression for the loss function value to reach the preset convergence condition is: in, is a positive number, and are the loss function values ​​after this and previous iterations, respectively.

6. The PPP-B2b orbit clock error correction method according to claim 1, characterized in that: The weighted formula of the memory decay factor is: in, is the final weighted prediction value, For the moment The model prediction value of is the weighted predicted value of the previous moment, The weighted prediction value of the memory decay factor at the initial moment adopts the actual comprehensive error at that moment.

7. The PPP-B2b orbit clock error correction method according to claim 1, characterized in that: The final predicted orbit clock error result is applied to the precise positioning expression: in, For user receiver Received GNSS satellites In frequency The pseudorange observation value on the channel, For the receiver The GNSS satellites received At the frequency The carrier phase observation value of represents the observed user receiver, For the user receiver The GNSS satellites received In frequency The pseudorange observation value on the channel, For the user receiver The GNSS satellites received In frequency The carrier phase observation value, is the speed of light in vacuum, and are the satellite and receiver clock errors 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 in cycles, and is the observation noise corresponding to the pseudorange and carrier observations.

8. A PPP-B2b orbit clock error correction device, characterized in that: The PPP-B2b orbit clock error correction method according to any one of claims 1 to 7 comprises: The recovery module is used to recover the PPP-B2b precise orbit and clock error based on the BeiDou navigation message and the BeiDou-3 precise point positioning service PPP-B2b precise message to generate PPP-B2b recovery data; An evaluation module, configured to evaluate the PPP-B2b orbit clock error that meets a preset time condition based on the PPP-B2b restored data using a preset reference product to generate PPP-B2b orbit clock error data; A calculation module, used to calculate the comprehensive error of the PPP-B2b precise orbit and the 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; A weighting module, used for performing a memory decay factor weighting process 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; A correction module is used to correct the PPP-B2b orbit clock error according to the final prediction value to generate a predicted orbit clock error result of PPP-B2b.

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

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

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