Differential positioning method and system based on multi-source satellite data fusion

Abstract

The present application discloses a differential positioning method and system based on multi-source satellite data fusion, which relates to the field of satellite positioning. The method includes: a ground station obtains multiple first satellite data from multiple satellite navigation systems; the ground station determines target fusion data based on the multiple first satellite data and multiple target weights, determines the target error based on the target fusion data and the ground station position information, and sends the target error to the receiver; wherein the multiple target weights correspond one-to-one to the multiple satellite navigation systems, and the multiple target weights are determined based on the signal strength, satellite geometric distribution, and historical target error corresponding to the multiple satellite navigation systems; the receiver determines the receiver target position based on the target error and the receiver position observation value. The present disclosure can solve the problem in the prior art that the accuracy of fusion data is poor in complex environments, resulting in low accuracy of positioning results, improve the accuracy of fusion data, and improve the accuracy of positioning results.

Description

Technical Field

[0001] The present application relates to the field of satellite positioning technology, and in particular to a differential positioning method and system based on multi-source satellite data fusion. Background Art

[0002] With the widespread application of satellite positioning technology, such as in navigation, geographic mapping, intelligent transportation and other fields, higher and higher requirements are placed on positioning accuracy and reliability.

[0003] Currently, differential positioning methods based on the fusion of multi-source satellite data are generally used to improve positioning accuracy. However, while these existing methods can improve positioning accuracy, the fusion of satellite data from multiple satellite navigation systems (i.e., multi-source satellite data) is performed based on preset weights for each satellite navigation system. This results in poor accuracy in complex environments, leading to inaccurate positioning results. Summary of the Invention

[0004] The embodiments of the present application provide a differential positioning method and system based on multi-source satellite data fusion, which solves the problem in the prior art that the accuracy of fused data is poor in complex environments, resulting in low accuracy of positioning results. It can improve the accuracy of fused data and the accuracy of positioning results.

[0005] In a first aspect, an embodiment of the present application provides a differential positioning method based on multi-source satellite data fusion, including:

[0006] The ground station obtains multiple first satellite data from multiple satellite navigation systems; the ground station determines target fusion data based on the multiple first satellite data and multiple target weights, determines target errors based on the target fusion data and ground station position information, and sends the target errors to the receiver; wherein the multiple target weights correspond one-to-one to the multiple satellite navigation systems, and the multiple target weights are determined based on the signal strengths, satellite geometric distributions, and historical target errors corresponding to the multiple satellite navigation systems; the receiver determines the receiver target position based on the target errors and the receiver position observation values.

[0007] Furthermore, the determination of multiple target weights includes:

[0008] Based on the historical average signal strength of each satellite navigation system and the signal strength of the corresponding first satellite data, the first initial weight of each satellite navigation system in the signal quality dimension is determined; based on the satellite geometric distribution of each satellite navigation system, the second initial weight of each satellite navigation system in the geometric distribution dimension is determined; based on the average historical target error of each satellite navigation system, the third initial weight of each satellite navigation system in the historical error dimension is determined; based on the first initial weight, second initial weight and third initial weight corresponding to each satellite navigation system, the target weight corresponding to each satellite navigation system is determined.

[0009] Furthermore, before determining the target weight corresponding to each satellite navigation system based on the first initial weight, the second initial weight, and the third initial weight corresponding to each satellite navigation system, the method further includes:

[0010] Based on the carrier-to-noise ratio data, multipath effect strength and phase stability parameters of each satellite navigation system, the first reference weight of each satellite navigation system in the signal quality dimension is determined; the first initial weight is updated based on the first reference weight; the second initial weight coefficient of each satellite navigation system in the geometric distribution dimension is updated based on the horizontal precision factor, vertical precision factor and time precision factor corresponding to the satellite geometric distribution of each satellite navigation system; based on the historical target error of each satellite navigation system, the contribution rate of multiple error sources to the historical target error is determined; based on the contribution rate of multiple error sources to the historical target error, the third initial weight coefficient of each satellite navigation system in the historical error dimension is updated.

[0011] Furthermore, the target error is determined based on the target fusion data and the ground station position information, including: determining the first error based on the target fusion data and the ground station position information; determining the error change trend based on multiple historical first errors; determining the error compensation based on the error change trend; and determining the target error based on the first error and the error compensation.

[0012] Furthermore, error compensation is determined according to the error variation trend, including: determining the prediction error through a time series prediction model according to the error variation trend; and determining the error compensation according to the prediction error.

[0013] Furthermore, the first error is determined based on the target fusion data and the ground station position information, including: determining the initial positioning error through the observation residual method based on the target fusion data; determining the first error through the Kalman filtering algorithm based on the initial positioning error and the ground station position information.

[0014] Furthermore, the error change trend is determined based on multiple historical first errors, including: obtaining the error change trend based on a pre-trained error change trend prediction model according to the multiple historical first errors, and the satellite navigation system information and environmental information corresponding to the multiple historical first errors; wherein the satellite navigation system information includes satellite signal strength and satellite geometric distribution parameters, and the environmental information includes time, weather conditions, and topography; the pre-trained error change trend prediction model is obtained by training an initial machine learning model with the satellite navigation system information and environmental information corresponding to the multiple historical first errors as input and the multiple historical first errors as output.

[0015] Furthermore, the method also includes: the ground station determines an accuracy assessment result based on the horizontal position error and elevation position error of the receiver, the accuracy assessment result including horizontal accuracy and elevation accuracy; when the accuracy assessment result does not meet the accuracy requirements, updating multiple target weights.

[0016] Furthermore, before determining the target error based on the target fusion data and the ground station position information, the method further includes: performing data integrity verification on the target fusion data.

[0017] In a second aspect, an embodiment of the present application provides a differential positioning system based on multi-source satellite data fusion, including:

[0018] Ground station and receiver, the ground station includes an acquisition module, a fusion module and an error module, and the receiver includes a positioning module.

[0019] The acquisition module is configured to acquire a plurality of first satellite data from a plurality of satellite navigation systems.

[0020] The fusion module is used for the ground station to determine target fusion data according to multiple first satellite data and multiple target weights.

[0021] The error module is used to determine the target error based on the target fusion data and the ground station position information, and send the target error to the receiver; wherein, multiple target weights correspond one-to-one to multiple satellite navigation systems, and the multiple target weights are determined based on the signal strength, satellite geometric distribution, and historical target errors corresponding to the multiple satellite navigation systems.

[0022] The positioning module is used to determine the target position of the receiver based on the target error and the receiver position observation value.

[0023] In a third aspect, an embodiment of the present application provides a device comprising: a processor; a memory for storing processor-executable instructions; and a method for implementing the first aspect or any possible implementation of the first aspect when the processor executes the executable instructions.

[0024] In a fourth aspect, an embodiment of the present application provides a non-volatile computer-readable storage medium, which includes a device for storing a computer program or instruction, and when the computer program or instruction is executed, the method of the first aspect or any possible implementation method of the first aspect is implemented.

[0025] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0026] In an embodiment of the present application, a ground station obtains multiple first satellite data from multiple satellite navigation systems; the ground station accurately determines multiple target weights based on the multiple first satellite data, as well as the signal strengths, satellite geometric distributions, and historical target errors corresponding to the multiple satellite navigation systems, thereby improving the accuracy of the determined target fusion data; a more accurate target error is determined based on the more accurate target fusion data and the ground station position information, and the target error is sent to the receiver; the receiver can accurately determine the receiver target position based on the more accurate target error and the receiver position observation value, thereby improving the accuracy of the positioning result. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments of the present application or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 A schematic diagram of a flow chart of a differential positioning method based on multi-source satellite data fusion provided in an embodiment of the present application;

[0029] Figure 2 A schematic diagram of the composition of a differential positioning system based on multi-source satellite data fusion provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0031] The following description of some of the technologies involved in the embodiments of this application is provided to facilitate understanding and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for the sake of clarity and conciseness, some descriptions of well-known functions and structures are omitted from the following description.

[0032] With the widespread application of satellite positioning technology, such as in navigation, geographic mapping, intelligent transportation and other fields, higher and higher requirements are placed on positioning accuracy and reliability.

[0033] Currently, differential positioning methods based on the fusion of multi-source satellite data are generally used to improve positioning accuracy. However, while these existing methods can improve positioning accuracy, the fusion of satellite data from multiple satellite navigation systems (i.e., multi-source satellite data) is performed based on preset weights for each satellite navigation system. This results in poor accuracy in complex environments, leading to inaccurate positioning results.

[0034] Against this background, the present disclosure provides a differential positioning method based on multi-source satellite data fusion, which can improve the accuracy of fused data and the accuracy of positioning results.

[0035] The differential positioning method based on multi-source satellite data fusion is exemplarily described below with reference to the accompanying drawings.

[0036] Figure 1 This is a flow chart of a differential positioning method based on multi-source satellite data fusion provided in an embodiment of the present application. Figure 1 This is only an execution order shown in the embodiment of the present application, and does not represent the only execution order of the differential positioning method based on multi-source satellite data fusion. If the final result can be achieved, Figure 1 The steps shown can be performed in parallel or in reverse. Figure 1 As shown, the method may include S101 to S103.

[0037] S101: A ground station obtains a plurality of first satellite data from a plurality of satellite navigation systems.

[0038] Exemplarily, the multiple satellite navigation systems may include existing satellite navigation systems such as the Global Positioning System (GPS), the Beidou Satellite Navigation System (BDS), and the Galileo Satellite Navigation System (Galileo), without limitation thereto.

[0039] It can be understood that the ground station can obtain different first satellite data from different satellite navigation systems, and multiple satellite navigation systems correspond one-to-one to multiple first satellite data.

[0040] S102: The ground station determines target fusion data based on the multiple first satellite data and the multiple target weights; determines a target error based on the target fusion data and the ground station position information, and sends the target error to the receiver.

[0041] Among them, multiple target weights correspond one-to-one to multiple satellite navigation systems, and the multiple target weights are determined based on the signal strength, satellite geometric distribution, and historical target errors corresponding to the multiple satellite navigation systems.

[0042] Exemplarily, target fusion data may be obtained through a weighted algorithm based on multiple first satellite data and multiple target weights.

[0043] For example, the signal strength, satellite geometric distribution, and historical target error corresponding to each satellite navigation system can be obtained through the historical satellite data corresponding to each satellite navigation system. This is a conventional technical means in the field and will not be described in detail here.

[0044] Exemplarily, the target error may include error values ​​corresponding to multiple error sources, and the multiple error sources may include satellite clock error, atmospheric delay error, multipath effect error, satellite ephemeris error, etc.

[0045] For example, the target fusion data represents the ground station's observation of its own position, while the ground station's position information represents the actual value of the ground station's own position. The error between the observed and actual values ​​for each error source can be calculated to obtain the target error. The ground station obtains the error corresponding to each error source by fusing the data and the actual position information, which is a conventional method in the art and will not be further described here.

[0046] It can be understood that the receiver is installed on objects such as aircraft, cars, ships, etc. that need satellite navigation to provide them with navigation and positioning data.

[0047] In some possible implementations, determining multiple target weights includes S201 to S204.

[0048] S201: Determine a first initial weight of each satellite navigation system in a signal quality dimension based on the historical average signal strength of each satellite navigation system.

[0049] Exemplarily, the historical average signal strength of each satellite navigation system can be normalized to obtain the normalized average signal strength corresponding to each satellite navigation system, and the normalized average signal strength is determined as the first initial weight of each satellite navigation system in the signal quality dimension.

[0050] For example, the normalized average signal strength corresponding to each satellite navigation system can be calculated by the following formula:

[0051]

[0052] Where, represents the normalized average signal strength of the i-th satellite navigation system, represents the historical average signal strength of the i-th satellite navigation system, Indicates the minimum historical average signal strength of all satellite navigation systems. Indicates the maximum historical average signal strength of all satellite navigation systems.

[0053] S202: Determine a second initial weight of each satellite navigation system in a geometric distribution dimension based on the geometric distribution of satellites of each satellite navigation system.

[0054] For example, the corresponding geometric dilution of precision can be calculated based on the geometric distribution of each satellite, and the inverse of the geometric dilution of precision can be determined as the second initial weight of each satellite navigation system in the geometric distribution dimension. Calculating the geometric dilution of precision based on the satellite geometric distribution is a conventional technique in the art and will not be further described here.

[0055] S203: Determine a third initial weight of each satellite navigation system in a historical error dimension based on the average historical target error of each satellite navigation system.

[0056] Exemplarily, the average historical target error of each satellite navigation system can be normalized to obtain the normalized average historical target error corresponding to each satellite navigation system, and the normalized average historical target error is determined as the third initial weight of each satellite navigation system in the historical error dimension.

[0057] For example, the normalized average historical target error corresponding to each satellite navigation system can be calculated by the following formula:

[0058]

[0059] Where, represents the normalized average historical target error of the i-th satellite navigation system, represents the average historical target error of the i-th satellite navigation system, represents the minimum value of the average historical target error among all satellite navigation systems, It represents the maximum value of the average historical target error among all satellite navigation systems.

[0060] S204: Determine a target weight corresponding to each satellite navigation system according to the first initial weight, the second initial weight, and the third initial weight corresponding to each satellite navigation system.

[0061] Exemplarily, for any satellite navigation system, corresponding coefficients can be preset for the signal quality dimension, geometric distribution dimension, and historical error dimension, respectively, and then the first initial weight, the second initial weight, and the third initial weight are weighted and summed in combination with the corresponding coefficients to obtain the intermediate weight corresponding to the satellite navigation system; the intermediate weights corresponding to each satellite navigation system are normalized to obtain the target weight corresponding to each satellite navigation system.

[0062] For example, taking the first initial weight as 0.8, the second initial weight as 0.5, and the third initial weight as 0.6, and the signal quality dimension accounting for 40%, the geometric distribution dimension accounting for 30%, and the historical error dimension accounting for 30%, the intermediate weight is 0.8×40%+0.5×30%+0.6×30%=0.65.

[0063] For example, the intermediate weights corresponding to each satellite navigation system can be normalized by first calculating the sum of the intermediate weights corresponding to each satellite navigation system, and then calculating the ratio of the intermediate weights corresponding to each satellite navigation system to the sum to obtain the normalized weight, that is, the target weight corresponding to each satellite navigation system.

[0064] For example, taking the intermediate weights corresponding to satellite navigation systems A, B, and C as 0.8, 0.6, and 0.5 respectively, the sum of the intermediate weights corresponding to each satellite navigation system is 0.8+0.6+0.5=1.9, the target weight corresponding to satellite navigation system A is 0.8 / 1.9=0.42, the target weight corresponding to satellite navigation system B is 0.6 / 1.9=0.32, and the target weight corresponding to satellite navigation system C is 0.5 / 1.9=0.26.

[0065] In this way, the target weight corresponding to each satellite navigation system can be accurately determined by combining the first initial weight of the signal quality dimension, the second initial weight of the geometric distribution dimension and the third initial weight of the historical error dimension corresponding to each satellite navigation system.

[0066] In some possible embodiments, before determining the target weight corresponding to each satellite navigation system based on the first initial weight, the second initial weight, and the third initial weight corresponding to each satellite navigation system, the method further includes:

[0067] S301. Determine a first reference weight of each satellite navigation system in a signal quality dimension based on carrier-to-noise ratio data, multipath effect strength, and phase stability parameters of each satellite navigation system; and update the first initial weight based on the first reference weight.

[0068] For example, the phase stability parameter may be the standard deviation of phase changes between adjacent epochs, which is not limited.

[0069] It should be noted that the carrier-to-noise ratio data, multipath effect strength and phase stability parameters of each satellite navigation system can also be obtained through the historical satellite data corresponding to each satellite navigation system. This is a conventional technical means in this field and will not be repeated here.

[0070] For example, the carrier-to-noise ratio of each satellite navigation system can be normalized to obtain the normalized carrier-to-noise ratio corresponding to each satellite navigation system; the multipath effect intensity of each satellite navigation system can be normalized to obtain the normalized multipath effect intensity corresponding to each satellite navigation system; the phase stability parameter of each satellite navigation system can be normalized to obtain the normalized phase stability parameter corresponding to each satellite navigation system.

[0071] It is understandable that the normalized carrier-to-noise ratio, normalized multipath effect strength, and normalized phase stability parameter can be calculated using normalized calculation formulas similar to the aforementioned formulas (formulas in S201 and S203), which will not be described in detail here.

[0072] After obtaining the normalized carrier-to-noise ratio, normalized multipath effect strength, and normalized phase stability parameters, the sum of the products of the normalized carrier-to-noise ratio, normalized multipath effect strength, normalized phase stability parameters and the corresponding preset coefficients can be determined as the first reference weight of each satellite navigation system in the signal quality dimension.

[0073] Among them, the preset coefficients corresponding to the normalized carrier-to-noise ratio, the normalized multipath effect strength, and the normalized phase stability parameter may be the same or different; it can be understood that the sum of the preset coefficients corresponding to the three is 1.

[0074] For example, after obtaining the first reference weight, an average value of the first reference weight and the first initial weight may be calculated, and the average value may be used as the updated first initial weight.

[0075] S302: Update the second initial weight coefficient of each satellite navigation system in the geometric distribution dimension according to the horizontal dilution of precision, vertical dilution of precision, and temporal dilution of precision corresponding to the geometric distribution of satellites of each satellite navigation system.

[0076] For example, corresponding thresholds can be preset for the horizontal DOP, vertical DOP, and temporal DOP. When the horizontal DOP, vertical DOP, or temporal DOP is less than the corresponding threshold, the second initial weight coefficient is increased by a certain multiple. When the horizontal DOP, vertical DOP, or temporal DOP is not less than the corresponding threshold, the second initial weight coefficient is decreased by a certain multiple. The thresholds corresponding to the horizontal DOP, vertical DOP, and temporal DOP can be the same or different, and the corresponding increase and decrease multiples can be the same or different, without limitation.

[0077] For example, for the horizontal dilution of precision: the threshold corresponding to the horizontal dilution of precision can be set to 2, the increase multiple to 0.2, and the decrease multiple to 0.3. When the second initial weight coefficient of a satellite navigation system is 0.3, if the horizontal dilution of precision is 1.5, then the updated second initial weight coefficient of the satellite navigation system is 0.3×(1+0.2)=0.36; if the horizontal dilution of precision is 2.5, then the updated second initial weight coefficient of the satellite navigation system is 0.3×(1-0.3)=0.21.

[0078] For the vertical dilution of precision: the threshold corresponding to the vertical dilution of precision can be set to 3, the increase multiple to 0.15, and the decrease multiple to 0.25. When the second initial weight coefficient of a satellite navigation system is 0.25, if the vertical dilution of precision is 2.5, then the updated second initial weight coefficient of the satellite navigation system is 0.25×(1+0.15)=0.2875; if the vertical dilution of precision is 3.5, then the updated second initial weight coefficient of the satellite navigation system is 0.25×(1-0.25)=0.1875.

[0079] For the temporal dilution of precision: the threshold corresponding to the temporal dilution of precision can be set to 1.8, the increase multiple to 0.1, and the decrease multiple to 0.2. When the second initial weight coefficient of a satellite navigation system is 0.2, if the temporal dilution of precision is 1.5, the updated second initial weight coefficient of the satellite navigation system is 0.2×(1+0.1)=0.22; if the temporal dilution of precision is 2.0, the updated second initial weight coefficient of the satellite navigation system is 0.2×(1-0.2)=0.16.

[0080] S303. Determine, based on the historical target errors of each satellite navigation system, contribution rates of multiple error sources to the historical target errors; and update, based on the contribution rates of the multiple error sources to the historical target errors, a third initial weight coefficient of each satellite navigation system in the historical error dimension.

[0081] For example, the historical target error may include data of various error sources including satellite ephemeris error, receiver clock error, atmospheric delay error, etc.

[0082] For example, an initial multiple linear regression model can be constructed using the historical target error as the dependent variable and each error source as the independent variable. Parameters can be estimated using the least squares method to obtain a target multiple linear regression model. Based on the regression results, the variance contribution rate of each error source to the historical target error is calculated, and the variance contribution rate of each error source to the historical target error is determined as the contribution rate of each error source to the historical target error. It is understood that the construction and parameter estimation of a multiple linear regression model, as well as the calculation of the variance contribution rate of each independent variable (i.e., each error source) to the dependent variable (i.e., the historical target error), are all common knowledge and will not be elaborated upon here.

[0083] After obtaining the contribution rates of multiple error sources to the historical target error, the contribution rates corresponding to each error source can be compared with the baseline contribution rate. For every percentage increase in the contribution rate compared to the baseline contribution rate, the third initial weight coefficient can be reduced by a certain multiple; for every percentage decrease in the contribution rate compared to the baseline contribution rate, the third initial weight coefficient can be increased by a certain multiple.

[0084] For example, the benchmark contribution rate is 30%, and the third initial weight coefficient decreases by 0.1 for every 10% increase in the contribution rate of the error source compared to the benchmark contribution rate. The third initial weight coefficient increases by 0.15 for every 10% decrease in the contribution rate of the error source compared to the benchmark contribution rate. When the contribution rate corresponding to the satellite ephemeris error in a certain satellite navigation system is 40%, and the contribution rate of the satellite ephemeris error increases by 10% compared to the benchmark contribution rate, the third initial weight coefficient decreases by 0.1 times; when the ephemeris error contribution rate is 20%, and the contribution rate of the satellite ephemeris error decreases by 10% compared to the benchmark contribution rate, its third initial weight coefficient increases by 0.15 times.

[0085] In this way, the initial weights of the satellite navigation systems can be updated in the signal quality dimension, geometric distribution dimension, and historical error dimension respectively, further improving the accuracy of the target weights corresponding to each satellite navigation system.

[0086] In some possible implementations, the ground station determines the target error based on the target fusion data and the ground station position information, including S401 to S404.

[0087] S401. Determine a first error based on target fusion data and ground station position information.

[0088] Exemplarily, the errors of the ground station position information and the target fusion data for each error source may be calculated, that is, a first error may be obtained.

[0089] Specifically, determining the first error based on the target fusion data and the ground station position information includes:

[0090] Based on the target fusion data, the initial positioning error is determined by the observation residual method; based on the initial positioning error and the ground station position information, the first error is determined by the Kalman filter algorithm.

[0091] For example, by comparing the observed values ​​(i.e., target fusion data) with the theoretical observed values ​​predicted based on parameters such as satellite ephemeris, satellite clock errors, and receiver clock errors, the observation residual is calculated and converted to obtain an initial estimate of the positioning error (i.e., the initial positioning error). The theoretical observed values ​​are determined based on satellite position, signal propagation time, and other factors. The difference between the actual observed values ​​and the theoretical values ​​reflects the presence of positioning error. The observation residual method is a conventional technique in this field and will not be further described here.

[0092] Next, the initial positioning error is considered part of the system state vector and, combined with the ground station's position information, the Kalman filter algorithm estimates and updates the error in real time. Using the Kalman filter's prediction and update equations, the observed data and the system's dynamic model are combined to obtain the optimal positioning error estimate (i.e., the first error), making the error estimate more accurate. The Kalman filter algorithm is a conventional technique in this field and will not be described in detail here.

[0093] In this way, a first error with higher accuracy can be obtained through the Kalman filter algorithm.

[0094] S402: Determine an error change trend based on multiple historical first errors.

[0095] Exemplarily, multiple historical first errors in two consecutive windows can be obtained through a sliding window of fixed size, and the error change trend can be determined based on the average value of the multiple historical first errors corresponding to the previous sliding window and the average value of the multiple historical first errors corresponding to the next sliding window.

[0096] For example, if the sliding window size is 5 and the current moment is the 7th moment, calculate the mean of the historical first errors from moments 1 to 5, and the mean of the historical first errors from moments 2 to 6, and compare the difference between the two to determine whether the error increases, decreases, or remains stable, as well as the degree of increase or decrease.

[0097] For example, the relationship between the historical first error and time can be considered a mathematical function, and the least squares method can be used to fit the parameters of this function. It can be assumed that the relationship between the historical first error and time is a linear function. By solving the least squares solution, the slope of the error change is obtained. This slope represents the error change trend. A positive slope indicates that the error increases with time, while a negative slope indicates that the error decreases. It can be understood that the larger the absolute value of the slope, the greater the degree of the error change trend.

[0098] In some possible implementations, determining an error change trend based on a plurality of historical first errors includes:

[0099] Obtaining an error change trend based on a pre-trained error change trend prediction model according to a plurality of historical first errors, satellite navigation system information and environmental information corresponding to the plurality of historical first errors;

[0100] Among them, satellite navigation system information includes satellite signal strength and satellite geometric distribution parameters, and environmental information includes time, weather conditions, and topography; the pre-trained error change trend prediction model is obtained by training the initial machine learning model with the satellite navigation system information and environmental information corresponding to multiple historical first errors as input and multiple historical first errors as output.

[0101] For example, the initial machine learning model may be a random forest model, a support vector machine, a long short-term memory network (LSTM), etc., without limitation.

[0102] In practical applications, new error data is continuously collected and used for online learning of the error trend prediction model. This allows the model to update its understanding of error variation patterns in real time to adapt to changes in the environment and system. Furthermore, the error trend prediction model can be regularly retrained and optimized to ensure its accuracy and effectiveness. Simultaneously, the model is monitored and maintained to promptly identify and resolve potential issues.

[0103] In this way, the machine learning model can be used to quickly and accurately predict the error change trend based on satellite navigation system information and environmental information that reflect different conditions.

[0104] S403: Determine error compensation according to the error change trend.

[0105] Specifically, error compensation is determined based on the error change trend, including:

[0106] According to the error change trend, the prediction error is determined through the time series prediction model; according to the prediction error, the error compensation is determined.

[0107] For example, a time series prediction model can be used to predict the change in the first error at a future moment based on the error change trend. Based on the predicted change, the inverse of the change is determined as the error compensation. For example, if the first error at the next moment is predicted to increase by 0.5m, the error compensation is -0.5m to offset the future error growth. The time series prediction model is a commonly used prediction model and will not be described in detail here.

[0108] S404: Determine a target error according to the first error and the error compensation.

[0109] For example, the first error and the error compensation value may be summed to obtain the target error.

[0110] In this way, the first error can be compensated to obtain a more accurate target error.

[0111] S103: The receiver determines the receiver target position according to the target error and the receiver position observation value.

[0112] For example, the receiver uses the received target error as a correction to correct the receiver's position observation (i.e., the receiver's observation of its own position), thereby obtaining the receiver's actual position (i.e., the receiver's target position). The receiver's use of the correction to correct its own position observation to obtain its actual position is common knowledge among those skilled in the art and will not be further elaborated here.

[0113] In an embodiment of the present application, a ground station obtains multiple first satellite data from multiple satellite navigation systems; the ground station accurately determines multiple target weights based on the multiple first satellite data, as well as the signal strengths, satellite geometric distributions, and historical target errors corresponding to the multiple satellite navigation systems, thereby improving the accuracy of the determined target fusion data; a more accurate target error is determined based on the more accurate target fusion data and the ground station position information, and the target error is sent to the receiver; the receiver can accurately determine the receiver target position based on the more accurate target error and the receiver position observation value, thereby improving the accuracy of the positioning result.

[0114] In some possible embodiments, the method may further include:

[0115] The ground station determines the accuracy assessment result based on the horizontal position error and elevation position error of the receiver. The accuracy assessment result includes horizontal accuracy and elevation accuracy. When the accuracy assessment result does not meet the accuracy requirements, multiple target weights are updated.

[0116] For example, the horizontal accuracy can be calculated by the following formula:

[0117]

[0118] Where, Indicates the horizontal accuracy, represents the standard deviation of the receiver's position error in the x direction, Indicates the standard deviation of the receiver's position error in the y direction.

[0119] For example, the standard deviation of the position error of the receiver in the z direction can be , determined as elevation accuracy ,Right now .

[0120] For example, after obtaining the horizontal accuracy and elevation accuracy, when the horizontal accuracy does not meet the accuracy requirements, the target weight of the satellite navigation system that contributes more to horizontal positioning (the satellite navigation system with a wider azimuth distribution can be determined as the satellite navigation system that contributes more to horizontal positioning) can be increased; when the elevation accuracy does not meet the accuracy requirements, the target weight of the satellite navigation system that contributes more to elevation positioning (the satellite navigation system with a higher elevation angle can be determined as the satellite navigation system that contributes more to horizontal positioning) can be increased.

[0121] For example, the product of a preset increase coefficient and a target weight can be calculated to obtain an increase value corresponding to the target weight. The preset increase coefficient can be 5%, 10%, 15%, 20%, etc., and there is no limitation on this.

[0122] It can be understood that while increasing the target weight of one satellite navigation system, the target weights of other satellite navigation systems should be appropriately reduced to ensure that the sum of multiple target weights remains equal to one.

[0123] For example, the ratio of the increase value of the target weight of one satellite navigation system to the number of other satellite navigation systems may be used as the decrease value of the target weights of other satellite navigation systems.

[0124] This embodiment can further improve the accuracy of the multiple target weights and the accuracy of the positioning result by updating the multiple target weights when the accuracy assessment result does not meet the accuracy requirement.

[0125] In some possible embodiments, before determining the target error based on the target fusion data and the ground station position information, the method may further include:

[0126] Perform data integrity verification on the target fusion data.

[0127] For example, we can first define data integrity indicators, clarify the data items that the fused data should include, the time series continuity requirements, and the reasonable value range of each data item; then use the verification algorithm to verify and validate the target fused data, and compare the calculated checksum with the preset value; then perform a consistency check on the target fused data, compare the observation results of different satellite navigation systems at the same observation point, and analyze the logical relationship between the data; then use statistical analysis and model-based detection methods to identify abnormal data points; finally, generate an integrity assessment report to record the results of the verification process, abnormal data, and integrity assessment conclusions.

[0128] It can be understood that after determining that the target fusion data can pass the data integrity verification, the method of the present application can continue to be executed (i.e., continue to execute S103); when the target fusion data cannot pass the data integrity verification, the method of the present application can be stopped and the first satellite data acquisition step and the target fusion data determination step (i.e., S101 and S102) can be re-executed.

[0129] This embodiment can better ensure the accuracy of the target error by performing data integrity verification on the target fusion data before determining the target error.

[0130] Although this application provides method operation steps such as embodiments or flowcharts, more or fewer operation steps may be included based on routine or non-creative work. The order of steps listed in this embodiment is only one way of executing the steps among many, and does not represent the only execution order. When an actual device or client product executes, the method can be executed sequentially according to the embodiment or the accompanying drawings, or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0131] like Figure 2 As shown, the embodiment of the present application also provides a differential positioning system based on multi-source satellite data fusion. The system includes:

[0132] The ground station 510 and the receiver 520 , the ground station 510 includes an acquisition module 511 , a fusion module 512 and an error module 513 , and the receiver 520 includes a positioning module 521 .

[0133] The acquisition module 511 is configured to acquire a plurality of first satellite data from a plurality of satellite navigation systems.

[0134] The fusion module 512 is used for the ground station to determine target fusion data according to multiple first satellite data and multiple target weights.

[0135] The error module 513 is used to determine the target error based on the target fusion data and the ground station position information, and send the target error to the receiver.

[0136] Among them, multiple target weights correspond one-to-one to multiple satellite navigation systems, and the multiple target weights are determined based on the signal strength, satellite geometric distribution, and historical target errors corresponding to the multiple satellite navigation systems.

[0137] The positioning module 521 is used to determine the target position of the receiver based on the target error and the receiver position observation value.

[0138] The beneficial effects and specific implementation methods of the present device embodiment can be referred to the aforementioned method embodiment, and will not be described in detail here.

[0139] Some modules in the apparatus described herein may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0140] The devices or modules described in the above application embodiments can be implemented by computer chips or physical devices, or by products with certain functions. For ease of description, the above devices are described separately by function in various modules. When implementing the embodiments of this application, the functions of each module can be implemented in the same or multiple software and / or hardware. Of course, a module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0141] The methods, devices, or modules described herein can be implemented in the form of computer-readable program code. The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the memory control logic. Those skilled in the art will also appreciate that, in addition to implementing the controller in pure computer-readable program code, the controller can also be implemented in the form of logic gates, switches, an application-specific integrated circuit, a programmable logic controller, an embedded microcontroller, etc. by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the means for implementing various functions may be considered to be both a software module for implementing the method and a structure within a hardware component.

[0142] An embodiment of the present application further provides a device comprising: a processor; a memory for storing processor-executable instructions; and when the processor executes the executable instructions, the method described in the embodiment of the present application is implemented.

[0143] The embodiments of the present application also provide a non-volatile computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed, the method described in the embodiments of the present application is implemented.

[0144] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist independently, or two or more modules may be integrated into one module.

[0145] The above-mentioned storage media include, but are not limited to, random access memory (RAM), read-only memory (ROM), cache, hard disk drive (HDD), or memory card. Such memory can be used to store computer program instructions.

[0146] Through the description of the above implementation methods, it can be seen that those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, or can be embodied through the implementation process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application or certain parts of the embodiments.

[0147] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments. All or part of this application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, mobile communication terminals, multi-processor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0148] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some or all of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present application.

Claims

1. A differential positioning method based on multi-source satellite data fusion, characterized in that: include: The ground station acquires a plurality of first satellite data from a plurality of satellite navigation systems; The ground station determines target fusion data based on the multiple first satellite data and multiple target weights, determines a target error based on the target fusion data and ground station position information, and sends the target error to a receiver; wherein the multiple target weights correspond one-to-one to multiple satellite navigation systems and are determined based on signal strength, satellite geometric distribution, and historical target errors corresponding to the multiple satellite navigation systems; The receiver determines a receiver target position based on the target error and the receiver position observation value; Determining the multiple target weights includes: determining a first initial weight of each satellite navigation system in a signal quality dimension based on a historical average signal strength of each satellite navigation system and a signal strength of corresponding first satellite data; determining a second initial weight of each satellite navigation system in a geometric distribution dimension based on a satellite geometric distribution of each satellite navigation system; determining a third initial weight of each satellite navigation system in a historical error dimension based on an average historical target error of each satellite navigation system; and determining a target weight corresponding to each satellite navigation system based on the first initial weight, the second initial weight, and the third initial weight corresponding to each satellite navigation system. Before determining the target weight corresponding to each satellite navigation system based on the first initial weight, the second initial weight, and the third initial weight corresponding to each satellite navigation system, the method further includes: Based on the carrier-to-noise ratio data, multipath effect strength and phase stability parameters of each satellite navigation system, the first reference weight of each satellite navigation system in the signal quality dimension is determined; the first initial weight is updated based on the first reference weight; the second initial weight coefficient of each satellite navigation system in the geometric distribution dimension is updated based on the horizontal precision factor, vertical precision factor and time precision factor corresponding to the satellite geometric distribution of each satellite navigation system; based on the historical target error of each satellite navigation system, the contribution rate of multiple error sources to the historical target error is determined; based on the contribution rate of multiple error sources to the historical target error, the third initial weight coefficient of each satellite navigation system in the historical error dimension is updated.

2. The method according to claim 1, characterized in that Determining the target error according to the target fusion data and the ground station position information includes: determining a first error based on the target fusion data and the ground station position information; Determine the error change trend based on multiple historical first errors; Determine error compensation based on the error change trend; A target error is determined based on the first error and the error compensation.

3. The method according to claim 2, characterized in that Determining error compensation according to the error variation trend includes: According to the error change trend, the prediction error is determined through the time series prediction model; The error compensation is determined based on the prediction error.

4. The method according to claim 2, characterized in that The determining a first error according to the target fusion data and the ground station position information includes: According to the target fusion data, an initial positioning error is determined by an observation residual method; according to the initial positioning error and ground station position information, the first error is determined by a Kalman filter algorithm.

5. The method according to claim 2, characterized in that Determining the error change trend based on the plurality of historical first errors includes: Obtaining an error change trend based on a pre-trained error change trend prediction model according to a plurality of historical first errors, satellite navigation system information and environmental information corresponding to the plurality of historical first errors; Among them, the satellite navigation system information includes satellite signal strength and satellite geometric distribution parameters, and the environmental information includes time, weather conditions, and topography; the pre-trained error change trend prediction model is obtained by training the initial machine learning model with the satellite navigation system information and environmental information corresponding to multiple historical first errors as input and multiple historical first errors as output.

6. The method according to claim 1, characterized in that The method further comprises: The ground station determines an accuracy assessment result based on the horizontal position error and the elevation position error of the receiver, where the accuracy assessment result includes horizontal accuracy and elevation accuracy; when the accuracy assessment result does not meet the accuracy requirement, the multiple target weights are updated.

7. The method according to claim 1, characterized in that Before determining the target error based on the target fusion data and the ground station position information, the method further includes: Performing data integrity verification on the target fusion data.

8. A differential positioning system based on multi-source satellite data fusion, characterized in that: include: A ground station and a receiver, wherein the ground station includes an acquisition module, a fusion module and an error module, and the receiver includes a positioning module; The acquisition module is used to acquire a plurality of first satellite data from a plurality of satellite navigation systems; The fusion module is used for the ground station to determine target fusion data according to the multiple first satellite data and multiple target weights; The error module is used to determine a target error based on the target fusion data and the ground station position information, and send the target error to a receiver; The multiple target weights correspond one-to-one to multiple satellite navigation systems, and the multiple target weights are determined based on signal strength, satellite geometric distribution, and historical target errors corresponding to the multiple satellite navigation systems; The positioning module is used to determine the target position of the receiver based on the target error and the receiver position observation value; Determining the multiple target weights includes: determining a first initial weight of each satellite navigation system in a signal quality dimension based on a historical average signal strength of each satellite navigation system and a signal strength of corresponding first satellite data; determining a second initial weight of each satellite navigation system in a geometric distribution dimension based on a satellite geometric distribution of each satellite navigation system; determining a third initial weight of each satellite navigation system in a historical error dimension based on an average historical target error of each satellite navigation system; and determining a target weight corresponding to each satellite navigation system based on the first initial weight, the second initial weight, and the third initial weight corresponding to each satellite navigation system. Before determining the target weight corresponding to each satellite navigation system based on the first initial weight, the second initial weight, and the third initial weight corresponding to each satellite navigation system, the method further includes: Based on the carrier-to-noise ratio data, multipath effect strength and phase stability parameters of each satellite navigation system, the first reference weight of each satellite navigation system in the signal quality dimension is determined; the first initial weight is updated based on the first reference weight; the second initial weight coefficient of each satellite navigation system in the geometric distribution dimension is updated based on the horizontal precision factor, vertical precision factor and time precision factor corresponding to the satellite geometric distribution of each satellite navigation system; based on the historical target error of each satellite navigation system, the contribution rate of multiple error sources to the historical target error is determined; based on the contribution rate of multiple error sources to the historical target error, the third initial weight coefficient of each satellite navigation system in the historical error dimension is updated.

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