Location service optimization method based on Beidou multi-source data fusion

Abstract

The present invention relates to the field of positioning optimization technology, and more particularly to a positioning service optimization method based on Beidou multi-source data fusion. The method comprises obtaining carrier phase observation data of an antenna carried by a positioning target relative to a Beidou satellite, and determining the baseline direction of the antenna based on the carrier phase observation data; obtaining the current heading angle of the positioning target, and determining the installation position of an inertial sensor on the positioning target based on the baseline direction of the antenna, the current heading angle of the positioning target, and the optical flow offset of the positioning target in the baseline direction; and optimizing the position of the positioning target based on the installation position of the inertial sensor to improve positioning accuracy.

Description

Technical Field

[0001] The present invention relates to the field of positioning optimization technology, and in particular to a location service optimization method based on Beidou multi-source data fusion. Background Art

[0002] Currently, satellite navigation systems such as GPS and Beidou are widely used in various application scenarios. However, positioning technologies that rely on a single satellite signal have significant limitations in complex environments, such as urban canyons, indoor areas, and tunnels. For one thing, between tall buildings or in indoor environments, satellite signals are easily blocked or reflected by buildings, resulting in signal weakening or even loss. Furthermore, in signal-restricted areas, the positioning accuracy of a single satellite navigation system cannot meet the requirements of high-precision applications such as autonomous driving and drone navigation.

[0003] Therefore, faced with the problem of inaccurate positioning caused by the positioning technology relying on a single satellite signal in the existing positioning service system, there is an urgent need for a positioning service optimization method based on Beidou multi-source data fusion to solve the above problem. Summary of the Invention

[0004] The purpose of the present invention is to provide a location service optimization method based on Beidou multi-source data fusion: to solve the technical problem of inaccurate positioning caused by the positioning technology relying on a single satellite signal in the existing positioning service system.

[0005] A location service optimization method based on Beidou multi-source data fusion, including:

[0006] Obtain carrier phase observation data of the antenna carried by the positioning target relative to the Beidou satellite, and determine the baseline direction of the antenna based on the carrier phase observation data;

[0007] Obtain the current heading angle of the positioning target, and determine the installation position of the inertial sensor on the positioning target based on the baseline direction of the antenna, the current heading angle of the positioning target, and the optical flow offset of the positioning target in the baseline direction;

[0008] The position of the positioning target is optimized based on the installation position of the inertial sensor: a three-dimensional coordinate system is established with the center position of the positioning target as the coordinate origin, the position coordinates of the inertial sensor are determined, the Beidou satellite signal of the positioning target is obtained, and the quality index of the Beidou satellite signal is calculated. If the quality index is greater than the preset threshold, the Beidou satellite signal and the high-frequency attitude information output by the inertial sensor are combined to provide positioning services for the positioning target. If the quality index is less than the preset threshold, the time delay evaluation value between each inertial sensor is calculated, and the pitch angle and azimuth angle of the positioning target in the three-dimensional coordinate system are determined based on the time delay evaluation value, and the pitch angle and azimuth angle are adjusted based on the corrected angle error value.

[0009] Furthermore, determining the baseline direction of the antenna based on the carrier phase observation data specifically includes the following process:

[0010] Step 1: Set the baseline direction candidate value set. The carrier phase observation data includes the phase center error, and the phase center error is corrected first.

[0011] Step 2: construct a mathematical model of antenna orientation based on the corrected carrier phase observation; solve the relative positioning result based on the mathematical model, and calculate the evaluation value of each baseline direction candidate value based on the relative positioning result;

[0012] Step three: Arrange the evaluation values ​​in descending order and record the baseline direction candidate value with the smallest evaluation value as the baseline direction of the antenna.

[0013] Furthermore, constructing a mathematical model of antenna orientation based on the corrected carrier phase observations specifically includes the following steps:

[0014] The mathematical model of antenna orientation is: ;in, is the difference between the BeiDou satellite observation distance and the BeiDou satellite-antenna geometric distance, for expectations, is the carrier phase ambiguity vector, is the three-dimensional baseline vector, 、 is a coefficient matrix, which contains the carrier wavelength and BeiDou satellite unit observation vector information respectively.

[0015] Furthermore, solving the relative positioning result based on the mathematical model and calculating the evaluation value of each baseline direction candidate value based on the relative positioning result specifically includes the following process:

[0016] Based on mathematical model The fixed solution of the baseline direction corresponding to the minimum , and calculate the evaluation value of each baseline direction candidate value based on the fixed solution of the baseline direction :

[0017] ;

[0018] Among them, the a posteriori residual of the double-difference carrier phase observation is , carrier phase measurement variance , the posterior residual of the double-difference pseudorange observation , pseudorange measurement variance , is the fixed solution of baseline length derived from the fixed solution of relative positioning, The baseline length corresponding to each baseline direction candidate value, the measurement variance of the prior baseline length , is the candidate value of the i-th baseline direction, is the variance of the baseline direction estimate.

[0019] Furthermore, the optical flow offset of the positioning target in the baseline direction specifically includes the following process:

[0020] A pyramid is constructed by sampling the two frames of images before and after the motion trajectory of the positioning target in the baseline direction. Starting from the top layer, each feature point in the previous frame image is tracked, and the optical flow and transformation matrix are calculated. The iteration is repeated to minimize the grayscale difference of the feature points between the two frames after transformation by the optical flow and transformation matrix. The result of this layer is then passed to the next layer, and the optical flow and affine transformation matrix are recalculated and passed to the next layer until it is passed to the last layer to obtain the optical flow offset of the positioning target in the baseline direction.

[0021] Furthermore, determining the installation position of the inertial sensor on the positioning target based on the baseline direction of the antenna, the current heading angle of the positioning target, and the optical flow offset of the positioning target in the baseline direction specifically includes the following process:

[0022] Based on the angle corresponding to the antenna baseline direction, calculate the angle difference between the positioning target heading angle and the angle ;

[0023] The angle difference Substitute the optical flow offset WY into the associated formula to calculate the angle between the installation reference position of the inertial sensor on the positioning target and the heading direction of the positioning target , the association formula is: ,in, is the signal transmission speed of the cable, The time when the inertial sensor data is received for positioning the target;

[0024] The installation reference position and the installation positions of other inertial sensors are recorded as base arrays. First, the three base arrays are arranged around the positioning target. One base array is approximately regarded as a source sensor. The installation positions of other inertial sensors are located through the intersection of the three base arrays.

[0025] Furthermore, calculating the quality index of the BeiDou satellite signal specifically includes the following process:

[0026] Acquire signal attenuation information of a first time period, signal attenuation information of a second time period, and signal attenuation information of a Gth time period based on the Beidou satellite signal, wherein the signal attenuation information of the first time period includes a first three-dimensional position dilution of precision factor, a first horizontal position dilution of precision factor, and a first elevation dilution of precision factor; the signal attenuation information of the Gth time period includes a Gth three-dimensional position dilution of precision factor, a Gth horizontal position dilution of precision factor, and a Gth elevation dilution of precision factor; the three-dimensional position dilution of precision factor is a degree to which the geometric structure between the positioning target and the Beidou satellite amplifies the observation error; the horizontal position dilution of precision factor is a degree to which the geometric structure between the positioning target and the Beidou satellite amplifies the horizontal observation error; and the elevation dilution of precision factor is a degree to which the geometric structure between the positioning target and the Beidou satellite amplifies the vertical observation error;

[0027] The first three-dimensional position dilution of precision factor, the first horizontal position dilution of precision factor, and the first elevation dilution of precision factor are added together to obtain the signal quality coefficient of the first time period, until the signal quality coefficient of the Gth time period is calculated. The average value of the signal quality coefficients from the first time period to the Gth time period is recorded as the quality index of the Beidou satellite signal.

[0028] Furthermore, combining BeiDou satellite signals with high-frequency attitude information output by inertial sensors to provide positioning services for the target specifically includes the following processes:

[0029] Based on Beidou satellite signals, the original observation data of satellite pseudorange, Doppler frequency shift, and carrier phase are obtained. The high-frequency attitude information includes high-frequency angular velocity and acceleration. The high-frequency attitude information timestamps of the Beidou signal and the inertial sensor output are aligned based on the time synchronization module. The initial attitude is estimated by jointly using the Beidou velocity / position information and the high-frequency attitude information, and the position of the positioning target is updated based on the extended Kalman filter algorithm.

[0030] Furthermore, calculating the delay evaluation value between each inertial sensor specifically includes the following process:

[0031] Calculate the inertial sensor using the calculation formula and Delay evaluation value :

[0032] ;

[0033] in, for Weighting function, It is an inertial sensor and The cross power spectrum of the signals between is the frequency domain analysis parameter, For variables 's points.

[0034] The calculation of the corrected angle error value specifically includes the following process:

[0035] Furthermore, the current position of the positioning target is obtained , locate the previous position of the target , according to the calculation formula , and obtain the correction coefficient curve ,in, The sampling time interval value The corresponding sampling time sequence number, ,and Is a positive integer; based on the correction curve Calculate the corrected angle error value : ;in, is a constant, Time sequence number of the sampling period The corresponding correction factor is, Time sequence number of the sampling period The corresponding correction factor.

[0036] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0037] The present invention deeply integrates Beidou carrier phase observation data with the high-frequency attitude information of the inertial sensor. When the satellite signal quality index is lower than the threshold, it automatically switches to the inertial navigation dominant mode, uses the optical flow offset and time delay evaluation value to dynamically correct the attitude error, realizes continuous positioning, and improves positioning accuracy.

[0038] By jointly calculating the baseline direction and optical flow offset, the installation position of the inertial sensor is accurately calibrated, and the weight is dynamically adjusted in combination with the Beidou satellite signal quality index to reduce the vertical positioning error. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0040] Figure 1 This is a workflow diagram of a first location service optimization method based on Beidou multi-source data fusion according to an embodiment of the present invention;

[0041] Figure 2 This is a workflow diagram of a second location service optimization method based on Beidou multi-source data fusion according to an embodiment of the present invention;

[0042] Figure 3This is a workflow diagram of a third location service optimization method based on Beidou multi-source data fusion in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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.

[0044] In addition, the described features, structures or characteristics can be combined in any suitable manner in one or more example embodiments. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure can be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. can be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0045] This embodiment provides a location service optimization method based on Beidou multi-source data fusion. Figure 1 This is a workflow diagram of the first location service optimization method based on Beidou multi-source data fusion according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0046] Step S101: Acquire carrier phase observation data of the antenna carried by the positioning target relative to the Beidou satellite, and determine the baseline direction of the antenna based on the carrier phase observation data;

[0047] Step S102: Obtain the current heading angle of the positioning target, and determine the installation position of the inertial sensor on the positioning target based on the baseline direction of the antenna, the current heading angle of the positioning target, and the optical flow offset of the positioning target in the baseline direction;

[0048] Step S103: Optimize the position of the positioning target based on the installation position of the inertial sensor: establish a three-dimensional coordinate system with the center position of the positioning target as the coordinate origin, determine the position coordinates of the inertial sensor, obtain the Beidou satellite signal of the positioning target, calculate the quality index of the Beidou satellite signal, if the quality index is greater than a preset threshold, combine the Beidou satellite signal and the high-frequency attitude information output by the inertial sensor to provide positioning services for the positioning target, if the quality index is less than the preset threshold, calculate the delay evaluation value between each inertial sensor, determine the pitch angle and azimuth angle of the positioning target in the three-dimensional coordinate system based on the delay evaluation value, and adjust the pitch angle and azimuth angle based on the corrected angle error value.

[0049] In summary, the present invention deeply integrates Beidou carrier phase observation data with the high-frequency attitude information of the inertial sensor. When the satellite signal quality index is lower than the threshold, it automatically switches to the inertial navigation dominant mode, and uses the optical flow offset and the time delay evaluation value to dynamically correct the attitude error to achieve continuous positioning. By jointly solving the baseline direction and the optical flow offset, the installation position of the inertial sensor is accurately calibrated. The weight is dynamically adjusted in combination with the Beidou satellite signal quality index to reduce the vertical positioning error and thus improve the positioning accuracy.

[0050] In some embodiments, Figure 2 This is a workflow diagram of a second location service optimization method based on Beidou multi-source data fusion according to an embodiment of the present invention. Figure 2 As shown in FIG, determining the baseline direction of the antenna based on the carrier phase observation data specifically includes the following process:

[0051] Step 1: Set the baseline direction candidate value set. The carrier phase observation data includes the phase center error, and the phase center error is corrected first.

[0052] Step 2: construct a mathematical model of antenna orientation based on the corrected carrier phase observation; solve the relative positioning result based on the mathematical model, and calculate the evaluation value of each baseline direction candidate value based on the relative positioning result;

[0053] Step three: Arrange the evaluation values ​​in descending order and record the baseline direction candidate value with the smallest evaluation value as the baseline direction of the antenna.

[0054] In some embodiments, constructing a mathematical model of antenna orientation based on the use of corrected carrier phase observations specifically includes the following process:

[0055] The mathematical model of antenna orientation is: ;in, is the difference between the BeiDou satellite observation distance and the BeiDou satellite-antenna geometric distance, for expectations, is the carrier phase ambiguity vector, is the three-dimensional baseline vector, 、 is a coefficient matrix, which contains the carrier wavelength and BeiDou satellite unit observation vector information respectively.

[0056] Furthermore, solving the relative positioning result based on the mathematical model and calculating the evaluation value of each baseline direction candidate value based on the relative positioning result specifically includes the following process:

[0057] Based on mathematical model The fixed solution of the baseline direction corresponding to the minimum , and calculate the evaluation value of each baseline direction candidate value based on the fixed solution of the baseline direction :

[0058] ;

[0059] Among them, the a posteriori residual of the double-difference carrier phase observation is , carrier phase measurement variance , the posterior residual of the double-difference pseudorange observation , pseudorange measurement variance , is the fixed solution of baseline length derived from the fixed solution of relative positioning, The baseline length corresponding to each baseline direction candidate value, the measurement variance of the prior baseline length , is the candidate value of the i-th baseline direction, is the variance of the baseline direction estimate.

[0060] In some embodiments, obtaining the optical flow offset of the positioning target in the baseline direction specifically includes the following process:

[0061] A pyramid is constructed by sampling the two frames of images before and after the target's motion trajectory in the baseline direction. Starting from the top layer, each feature point in the previous frame is tracked, and the optical flow and transformation matrix are calculated. The iteration is repeated to minimize the grayscale difference of the feature points between the two frames after transformation by the optical flow and transformation matrix. The result of this layer is then passed to the next layer, and the optical flow and affine transformation matrix are recalculated and passed to the next layer until it is passed to the last layer, and the Lucas-Kanade algorithm is used to calculate the incremental offset of each feature point.

[0062] In some embodiments, determining the installation position of the inertial sensor on the positioning target based on the baseline direction of the antenna, the current heading angle of the positioning target, and the optical flow offset of the positioning target in the baseline direction specifically includes the following process:

[0063] Based on the angle corresponding to the antenna baseline direction, calculate the angle difference between the positioning target heading angle and the angle ;

[0064] After all units are unified in dimension, the angle difference Substitute the optical flow offset WY into the associated formula to calculate the angle between the installation reference position of the inertial sensor on the positioning target and the heading direction of the positioning target , the association formula is: ,in, is the signal transmission speed of the cable, The time when the inertial sensor data is received for positioning the target;

[0065] The installation reference position and the installation positions of other inertial sensors are recorded as base arrays. First, the three base arrays are arranged around the positioning target. One base array is approximately regarded as a source sensor. The installation positions of other inertial sensors are located through the intersection of the three base arrays.

[0066] In some embodiments, Figure 3 This is a workflow diagram of a third location service optimization method based on Beidou multi-source data fusion according to an embodiment of the present invention. Figure 3 As shown in FIG, calculating the quality index of the BeiDou satellite signal specifically includes the following process:

[0067] Step S301: Acquire signal attenuation information of a first time period, signal attenuation information of a second time period, and signal attenuation information of a G-th time period based on Beidou satellite signals;

[0068] It is worth noting that the first time period, the second time period, and the Gth time period are preset time periods, wherein the signal attenuation information of the first time period includes the first three-dimensional position dilution of precision factor, the first horizontal position dilution of precision factor, and the first elevation dilution of precision factor; the signal attenuation information of the Gth time period includes the Gth three-dimensional position dilution of precision factor, the Gth horizontal position dilution of precision factor, and the Gth elevation dilution of precision factor; the three-dimensional position dilution of precision factor is the degree to which the geometric structure between the positioning target and the Beidou satellite amplifies the observation error; the horizontal position dilution of precision factor is the degree to which the geometric structure between the positioning target and the Beidou satellite amplifies the horizontal observation error; and the elevation dilution of precision factor is the degree to which the geometric structure between the positioning target and the Beidou satellite amplifies the vertical observation error;

[0069] Step S302: Add the first three-dimensional position dilution of precision factor, the first horizontal position dilution of precision factor, and the first elevation dilution of precision factor to obtain the signal quality coefficient of the first time period, until the signal quality coefficient of the Gth time period is calculated, and record the average value of the signal quality coefficient from the first time period to the signal quality coefficient of the Gth time period as the quality index of the Beidou satellite signal.

[0070] Furthermore, combining BeiDou satellite signals with high-frequency attitude information output by inertial sensors to provide positioning services for the target specifically includes the following processes:

[0071] Based on Beidou satellite signals, the original observation data of satellite pseudorange, Doppler frequency shift, and carrier phase are obtained. The high-frequency attitude information includes high-frequency angular velocity and acceleration. The high-frequency attitude information timestamps of the Beidou signal and the inertial sensor output are aligned based on the time synchronization module. The initial attitude is estimated by jointly using the Beidou velocity / position information and the high-frequency attitude information, and the position of the positioning target is updated based on the extended Kalman filter algorithm.

[0072] In some embodiments, calculating the delay evaluation value between each inertial sensor specifically includes the following process:

[0073] Calculate the inertial sensor using the calculation formula and Delay evaluation value :

[0074] ;

[0075] in, for Weighting function, It is an inertial sensor and The cross power spectrum of the signals between is the frequency domain analysis parameter, For variables 's points.

[0076] In some embodiments, determining the pitch angle and azimuth angle of the positioning target in the three-dimensional coordinate system based on the delay evaluation value specifically includes the following process:

[0077] Assume that the position coordinates of the positioning target are , calculate the distance between the positioning target and the installation reference position of the inertial sensor on the positioning target ;

[0078] based on and Get the pitch angle and azimuth :

[0079] ;

[0080] .

[0081] in, Delay evaluation value The average value of n is the number of inertial sensors.

[0082] Further, get the current position of the positioning target , locate the previous position of the target , according to the calculation formula , and obtain the correction coefficient curve ,in, The sampling time interval value The corresponding sampling time sequence number, ,and Is a positive integer; based on the correction curve Calculate the corrected angle error value : ;in, is a constant, Time sequence number of the sampling period The corresponding correction factor is, Time sequence number of the sampling period The corresponding correction coefficient is used to determine the position of the positioning target in the three-dimensional coordinate system based on the corrected pitch angle and azimuth angle.

[0083] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0084] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0085] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only for some logical functions. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0087] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0088] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A location service optimization method based on Beidou multi-source data fusion, characterized in that: Methods include: Obtain carrier phase observation data of the antenna carried by the positioning target relative to the Beidou satellite, and determine the baseline direction of the antenna based on the carrier phase observation data; Obtain the current heading angle of the positioning target, and determine the installation position of the inertial sensor on the positioning target based on the baseline direction of the antenna, the current heading angle of the positioning target, and the optical flow offset of the positioning target in the baseline direction; The position of the positioning target is optimized based on the installation position of the inertial sensor: a three-dimensional coordinate system is established with the center position of the positioning target as the coordinate origin, the position coordinates of the inertial sensor are determined, the Beidou satellite signal of the positioning target is obtained, and the quality index of the Beidou satellite signal is calculated. If the quality index is greater than the preset threshold, the Beidou satellite signal and the high-frequency attitude information output by the inertial sensor are combined to provide positioning services for the positioning target. If the quality index is less than the preset threshold, the time delay evaluation value between each inertial sensor is calculated, and the pitch angle and azimuth angle of the positioning target in the three-dimensional coordinate system are determined based on the time delay evaluation value, and the pitch angle and azimuth angle are adjusted based on the corrected angle error value.

2. The location service optimization method based on Beidou multi-source data fusion according to claim 1 is characterized in that: Determining the antenna baseline direction based on carrier phase observation data specifically includes the following process: Step 1: Set the baseline direction candidate value set. The carrier phase observation data includes the phase center error, and the phase center error is corrected first. Step 2: construct a mathematical model of antenna orientation based on the corrected carrier phase observation; solve the relative positioning result based on the mathematical model, and calculate the evaluation value of each baseline direction candidate value based on the relative positioning result; Step three: Arrange the evaluation values ​​in descending order and record the baseline direction candidate value with the smallest evaluation value as the baseline direction of the antenna.

3. The location service optimization method based on Beidou multi-source data fusion according to claim 2 is characterized in that: The mathematical model of antenna orientation based on the corrected carrier phase observations includes the following steps: The mathematical model of antenna orientation is: ;in, is the difference between the BeiDou satellite observation distance and the BeiDou satellite-antenna geometric distance, for expectations, is the carrier phase ambiguity vector, is the three-dimensional baseline vector, 、 is a coefficient matrix, which contains the carrier wavelength and BeiDou satellite unit observation vector information respectively.

4. The location service optimization method based on Beidou multi-source data fusion according to claim 3 is characterized in that: The relative positioning result is solved based on the mathematical model, and the evaluation value of each baseline direction candidate value is calculated based on the relative positioning result. The following processes are included: Based on mathematical model The fixed solution of the baseline direction corresponding to the minimum , and calculate the evaluation value of each baseline direction candidate value based on the fixed solution of the baseline direction : ； Among them, the a posteriori residual of the double-difference carrier phase observation is , carrier phase measurement variance , the posterior residual of the double-difference pseudorange observation , pseudorange measurement variance , is the fixed solution of baseline length derived from the fixed solution of relative positioning, The baseline length corresponding to each baseline direction candidate value, the measurement variance of the prior baseline length , is the candidate value of the i-th baseline direction, is the variance of the baseline direction estimate.

5. The location service optimization method based on Beidou multi-source data fusion according to claim 1 is characterized in that: The optical flow offset of the positioning target in the baseline direction specifically includes the following processes: A pyramid is constructed by sampling the two frames of images before and after the motion trajectory of the positioning target in the baseline direction. Starting from the top layer, each feature point in the previous frame image is tracked, and the optical flow and transformation matrix are calculated. The iteration is repeated to minimize the grayscale difference of the feature points between the two frames after transformation by the optical flow and transformation matrix. The result of this layer is then passed to the next layer, and the optical flow and affine transformation matrix are recalculated and passed to the next layer until it is passed to the last layer to obtain the optical flow offset of the positioning target in the baseline direction.

6. The location service optimization method based on Beidou multi-source data fusion according to claim 1 is characterized in that: The specific installation position of the inertial sensor on the positioning target is determined based on the baseline direction of the antenna, the current heading angle of the positioning target and the optical flow offset of the positioning target in the baseline direction. The following processes are included: Based on the angle corresponding to the antenna baseline direction, calculate the angle difference between the positioning target heading angle and the angle ; The angle difference Substitute the optical flow offset WY into the associated formula to calculate the angle between the installation reference position of the inertial sensor on the positioning target and the heading direction of the positioning target , the association formula is: ,in, is the signal transmission speed of the cable, The time when the inertial sensor data is received for positioning the target; The installation reference position and the installation positions of other inertial sensors are recorded as base arrays. First, the three base arrays are arranged around the positioning target. One base array is approximately regarded as a source sensor. The installation positions of other inertial sensors are located through the intersection of the three base arrays.

7. The location service optimization method based on Beidou multi-source data fusion according to claim 1 is characterized in that: Calculating the quality index of Beidou satellite signals specifically includes the following processes: Acquire signal attenuation information of a first time period, signal attenuation information of a second time period, and signal attenuation information of a Gth time period based on the Beidou satellite signal, wherein the signal attenuation information of the first time period includes a first three-dimensional position dilution of precision factor, a first horizontal position dilution of precision factor, and a first elevation dilution of precision factor; the signal attenuation information of the Gth time period includes a Gth three-dimensional position dilution of precision factor, a Gth horizontal position dilution of precision factor, and a Gth elevation dilution of precision factor; the three-dimensional position dilution of precision factor is a degree to which the geometric structure between the positioning target and the Beidou satellite amplifies the observation error; the horizontal position dilution of precision factor is a degree to which the geometric structure between the positioning target and the Beidou satellite amplifies the horizontal observation error; and the elevation dilution of precision factor is a degree to which the geometric structure between the positioning target and the Beidou satellite amplifies the vertical observation error; The first three-dimensional position dilution of precision factor, the first horizontal position dilution of precision factor, and the first elevation dilution of precision factor are added together to obtain the signal quality coefficient of the first time period, until the signal quality coefficient of the Gth time period is calculated. The average value of the signal quality coefficients from the first time period to the Gth time period is recorded as the quality index of the Beidou satellite signal.

8. The location service optimization method based on Beidou multi-source data fusion according to claim 1 is characterized in that: The positioning service for the positioning target by combining Beidou satellite signals and high-frequency attitude information output by inertial sensors specifically includes the following processes: Based on Beidou satellite signals, the original observation data of satellite pseudorange, Doppler frequency shift, and carrier phase are obtained. The high-frequency attitude information includes high-frequency angular velocity and acceleration. The high-frequency attitude information timestamps of the Beidou signal and the inertial sensor output are aligned based on the time synchronization module. The initial attitude is estimated by jointly using the Beidou velocity / position information and the high-frequency attitude information, and the position of the positioning target is updated based on the extended Kalman filter algorithm.

9. The location service optimization method based on Beidou multi-source data fusion according to claim 1 is characterized in that: Calculating the delay evaluation value between each inertial sensor specifically includes the following process: Calculate the inertial sensor using the calculation formula and Delay evaluation value : ； in, for Weighting function, It is an inertial sensor and The cross power spectrum of the signals between is the frequency domain analysis parameter, For variables 's points.

10. The location service optimization method based on Beidou multi-source data fusion according to claim 1, characterized in that: Calculate the corrected angle error value The following processes are included: Get the current location of the positioning target , locate the previous position of the target , according to the calculation formula , and obtain the correction coefficient curve ,in, The sampling time interval value The corresponding sampling time sequence number, ,and Is a positive integer; based on the correction curve Calculate the corrected angle error value : ;in, is a constant, Time sequence number of the sampling period The corresponding correction factor is, Time sequence number of the sampling period The corresponding correction factor.

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