A Multi-Mode Cooperative BeiDou High-Precision Positioning Method and System for Complex Electromagnetic Environments

Abstract

This invention relates to the technical field of BeiDou positioning, and discloses a method and system for multi-mode collaborative high-precision BeiDou positioning in complex electromagnetic environments. The method includes: collecting multi-source positioning data using multi-source positioning devices and performing spatiotemporal alignment processing; extracting positioning quality features from the multi-source positioning devices and converting these features into weight information for the multi-source positioning devices; constructing observation information and state information of the target to be positioned; and combining the observation information with the state information using a weighted extended Kalman filter for reinforcement learning to obtain high-precision fused positioning coordinates of the target to be positioned. This invention dynamically senses the positioning quality of different positioning devices, adjusts the weight information, automatically adapts to different positioning environments, and constructs a unified observation framework to filter and calculate BeiDou positioning results, fusing various heterogeneous information to achieve high-precision positioning through multi-mode collaboration, thus compensating for the positioning stability of the BeiDou navigation satellite system in complex electromagnetic environments.

Description

Technical Field

[0001] This invention relates to the field of BeiDou positioning, and more particularly to a BeiDou high-precision positioning method and system with multi-mode coordination in complex electromagnetic environments. Background Technology

[0002] In applications such as intelligent transportation, unmanned systems, and industrial automation, where positioning accuracy and stability are extremely critical, achieving centimeter-level or even sub-meter-level high-precision positioning remains a key challenge. As China's independently constructed and operated global satellite navigation system, the BeiDou Navigation Satellite System (BDS) is increasingly becoming a core supporting technology for key national industries and major projects due to its advantages of wide coverage, high time synchronization accuracy, strong signal redundancy, and excellent anti-interference capabilities. However, in typical complex electromagnetic environments such as urban canyons, densely populated high-rise building areas, underground parking garages, tunnels, and under bridges, BeiDou signal propagation is often severely affected by severe obstruction, strong multipath interference, and complex electromagnetic noise sources. In these environments, not only is direct signal acquisition difficult, but reflected signals can also introduce false ranging information, leading to systematic deviations in observations, a significant decrease in positioning accuracy, and even signal loss and navigation calculation failure, seriously affecting the availability and reliability of the BeiDou system. Furthermore, high-frequency equipment interference in industrial parks and the high-dynamic motion states in traffic scenarios further amplify the vulnerability of traditional positioning algorithms.

[0003] To alleviate the above problems, researchers have extensively explored multimodal cooperative localization strategies, introducing technologies such as IMU (Inertial Measurement Unit), UWB (Ultra-Wideband), visual SLAM, and 5G positioning into the localization system, and improving the overall system stability and accuracy by leveraging the complementary characteristics of sensors.

[0004] For example, existing patent CN118859274B proposes a multi-scenario positioning enhancement method for the fusion of BeiDou and 5G. This method first acquires 5G and BeiDou positioning data, filters them separately, and then sends them to a multi-scenario positioning pattern recognition system. Next, by identifying scene patterns, a fusion strategy is selected to jointly process the two types of data, and a neural network is further used to train and enhance the fusion result, thereby improving positioning accuracy in specific scenarios. However, this scheme mainly focuses on the fusion of BeiDou and 5G. In dynamic and complex electromagnetic environments with rapid fluctuations in signal reliability, modal failures, and significant differences in device positioning quality, it still lacks refined modeling and collaborative fusion mechanisms for spatiotemporal consistency processing and quality perception of short-term precise positioning methods such as IMU and UWB. Furthermore, existing schemes mostly employ static weights or offline optimization strategies, making it difficult to achieve real-time perception of positioning quality and dynamic weight adjustment, thus affecting the stability and robustness of the fused positioning results.

[0005] To address this problem, this invention proposes a multi-mode collaborative BeiDou high-precision positioning method and system under complex electromagnetic environments, which improves the positioning accuracy, reliability, and robustness. Summary of the Invention

[0006] This invention provides a multi-mode collaborative BeiDou high-precision positioning method and system in complex electromagnetic environments. By deploying multi-source positioning devices to collect data, a spatiotemporal alignment processing method is introduced to eliminate time drift and spatial offset of multi-source data, compensating for the shortcomings of the BeiDou system under obstruction and interference conditions. Furthermore, the accuracy and availability of different positioning devices fluctuate dynamically in different scenarios. Existing methods mostly employ static weighting or simple averaging strategies, which cannot accurately reflect the impact of device quality on the fused positioning results. This invention extracts positioning quality features and converts them into dynamic weights, assigning credibility levels to different positioning devices in specific environments, achieving weighted adaptive positioning fusion. This leads to the construction of a unified observation-state fusion framework, modeling the BeiDou positioning coordinates and multi-source weight information together as a constraint relationship between state variables and observation variables, achieving multi-source collaborative modeling while preserving the inherent coupling characteristics between various types of data, thus strengthening the foundation for fusion.

[0007] To achieve the above objectives, this invention provides a multi-mode collaborative BeiDou high-precision positioning method under complex electromagnetic environments, comprising the following steps:

[0008] S1: Deploy multi-source positioning equipment based on the BeiDou navigation satellite system, collect multi-source positioning data using the multi-source positioning equipment, and perform spatiotemporal alignment processing on the multi-source positioning data to obtain spatiotemporally aligned multi-source positioning data;

[0009] S2: Based on the spatiotemporally aligned multi-source positioning data, extract positioning quality features from the multi-source positioning devices and convert the positioning quality features into weight information of the multi-source positioning devices;

[0010] S3: Based on the positioning coordinates of the BeiDou Navigation Satellite System, the weight information of multi-source positioning devices and spatiotemporally aligned multi-source positioning data are integrated to construct state information and observation information that characterize the positioning results of the target to be positioned in the multi-source positioning devices.

[0011] S4: Combine observation information with weighted extended Kalman filtering for reinforcement learning to obtain the filtered results of the positioning coordinates in the state information at different times, which are used as the high-precision fused positioning coordinates of the target to be located.

[0012] As a further improvement of the present invention:

[0013] Optionally, deploy multi-source positioning equipment primarily based on the BeiDou Navigation Satellite System, and utilize this equipment to collect multi-source positioning data, including:

[0014] The multi-source positioning equipment includes the BeiDou navigation satellite system, ultra-wideband equipment, and IMU equipment, wherein the ultra-wideband equipment is divided into UWB anchor point equipment and UWB target equipment.

[0015] The collected multi-source positioning data includes BeiDou positioning data, UWB positioning data, and IMU positioning data. The BeiDou positioning data includes the positioning coordinates, pseudorange observations, carrier phase observations, carrier-to-noise ratio, multipath suppression ratio, and observation timestamps of the target acquired by the BeiDou navigation satellite system. The UWB positioning data includes the communication signal strength, ranging results, and communication timestamps between the target and the UWB target device acquired by the ultra-wideband equipment. The IMU positioning data includes the acceleration, velocity, and angular velocity of the target in the three axes of the inertial coordinate system, the attitude information of the target, the packet loss rate, and the measurement timestamps. The observation timestamps are the timestamps of communication between the satellite communication equipment and the BeiDou navigation satellite system.

[0016] Optionally, the multi-source positioning data is subjected to spatiotemporal alignment processing to obtain spatiotemporally aligned multi-source positioning data, including:

[0017] Obtain the external calibration parameters of each coordinate system in the multi-source positioning device relative to the reference coordinate system, including translation vectors and rotation matrices, where the reference coordinate system is the geodetic coordinate system, including latitude, longitude and altitude;

[0018] Using external calibration parameters, the coordinate system-related data in the multi-source positioning data are transformed and mapped to the reference coordinate system to obtain multi-source positioning data with coordinate system spatial alignment.

[0019] The clock of the satellite communication equipment is used as the master clock, and the observation timestamps during the communication process are synchronized to the ultra-wideband equipment and the IMU equipment. The communication corresponding to the observation timestamp is used as the BeiDou time synchronization time, and the communication timestamps and measurement timestamps of the ultra-wideband equipment and the IMU equipment are synchronized.

[0020] Optionally, based on the spatiotemporally aligned multi-source positioning data, positioning quality features are extracted from the multi-source positioning device, including:

[0021] Positioning parameter data of multi-source positioning devices is extracted from spatiotemporally aligned multi-source positioning data. The positioning parameters of the BeiDou Navigation Satellite System include carrier-to-noise ratio and multipath suppression ratio. The positioning parameters of the ultra-wideband device include communication signal strength and ranging results. The positioning parameters of the IMU device include packet loss rate. The positioning parameter-related data are normalized to form the positioning parameter data.

[0022] Calculate the positioning quality characteristics of a multi-source positioning device at different aligned timestamps:

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] in, This represents the positioning quality characteristics of the BeiDou Navigation Satellite System after the m-th alignment timestamp. This represents the normalized carrier-to-noise ratio of the timestamp after the m-th alignment of the BeiDou navigation satellite system. This represents the normalized multipath suppression ratio of the m-th aligned timestamp of the BeiDou navigation satellite system.

[0028] This represents the positioning quality characteristics of the ultra-wideband device after the m-th alignment timestamp. This represents the mean normalized communication signal strength of the ultra-wideband device at the timestamp after the m-th alignment. The standard deviation of the normalized ranging result of the ultra-wideband device after the m-th alignment timestamp;

[0029] This represents the positioning quality characteristic of the IMU device at the timestamp after the m-th alignment. This represents the normalized packet loss rate of the IMU device after the m-th alignment timestamp. This indicates packet loss control parameters, settings. It is 5. Represents an exponential function with the natural constant as its base;

[0030] The positioning quality feature ranges from 0 to 1. The higher the positioning quality feature, the more accurate the positioning data of the timestamp after alignment.

[0031] Optionally, the positioning quality features are converted into weight information for multi-source positioning devices, including:

[0032] The quantification formula for the weight information is:

[0033] ;

[0034] ;

[0035] in, This represents the weight information of the multi-source positioning device under the m-th aligned timestamp. These represent the weights of the BeiDou Navigation Satellite System, the ultra-wideband equipment, and the IMU equipment at the m-th aligned timestamp, respectively. This represents the weight of the j-th positioning device under the m-th aligned timestamp, where the BeiDou Navigation Satellite System, the ultra-wideband device, and the IMU device are the 1st to 3rd positioning devices, respectively.

[0036] This represents the positioning quality characteristic of the j-th positioning device at the timestamp after the m-th alignment. This indicates the preset confidence lower limit;

[0037] The positioning importance of the j-th positioning device is represented by the enhancement of the high-confidence positioning quality characteristics, which is used to describe the usage frequency of positioning devices under normal conditions. The positioning importance of the Beidou navigation satellite system, ultra-wideband equipment, and IMU equipment is set to 4, 2, and 1, respectively. Indicates positioning quality characteristics Difference amplification factor;

[0038] This represents the positioning quality characteristics of the k-th positioning device at the timestamp after the m-th alignment. This indicates the positioning importance of the k-th positioning device.

[0039] Optionally, by fusing the weight information of multi-source positioning devices and spatiotemporally aligned multi-source positioning data, state information and observation information characterizing the positioning results of the target to be located in the multi-source positioning devices are constructed, including:

[0040] The status information representing the positioning result of the target to be located in the multi-source positioning device is:

[0041] ;

[0042] in, This represents the status information of the target to be located after the m-th alignment timestamp. This represents the positioning coordinates of the target obtained by the BeiDou Navigation Satellite System after the m-th aligned timestamp, where the positioning coordinates are in a geodetic coordinate system and include latitude, longitude, and altitude. This represents the velocity vector of the target to be located, acquired by the IMU device after the m-th aligned timestamp. The velocity vector includes the velocity of the target in latitude, longitude and altitude. T represents transpose.

[0043] The observation information characterizing the positioning result of the target to be located in the multi-source positioning device is as follows:

[0044] ;

[0045] in, This represents the observation information of the target to be located after the m-th alignment timestamp. These represent the ranging vector, acceleration vector, and angular velocity vector of the target to be located at the m-th alignment timestamp, respectively. The ranging vector consists of the ranging results between the UWB target device on the target and the three nearest UWB anchor point devices. The acceleration vector includes the acceleration of the target in latitude, longitude, and altitude, and the angular velocity vector includes the angular velocity of the target in latitude, longitude, and altitude. This represents the weight information of the multi-source positioning device under the m-th aligned timestamp.

[0046] Optionally, the state information is processed by reinforcement learning-based weighted extended Kalman filtering in conjunction with observation information to obtain the filtered results of the BeiDou navigation satellite system's positioning coordinates at different times, including:

[0047] The weighted extended Kalman filter processing procedure is as follows:

[0048] Obtain the status information of the target to be located after the first alignment timestamp. ;

[0049] The acquired state information is subjected to an M-1 step weighted extended Kalman filter to obtain the M-1 step filtered state information. ,in Status information The corresponding filtering results, Status information Obtain the corresponding filtering results. The weighted extended Kalman filter processing flow is as follows:

[0050] Will and Perform splicing, using the Transformer model to... Make a prediction and obtain Prediction results and covariance Make a prediction and obtain the prediction result. Correlation covariance ,in For the filtering result The corresponding covariance;

[0051] Using a fully connected network to predict results Mapped to predictive observations Calculate observation information With predictive observations The difference between the values ​​is used as the observation residual. The observation residual covariance matrix is ​​calculated, and Kalman gain calculation, state update, and covariance update are performed sequentially to obtain the state information. Corresponding filtering results and covariance ;

[0052] Calculate the filtering results Status information The differences between them are used to dynamically adjust the observation information using reinforcement learning. Weight information in ;

[0053] From the filtering results The positioning coordinates are extracted and used as the filtered result of the m-th aligned timestamp of the positioning coordinates of the Beidou navigation satellite system.

[0054] To address the aforementioned problems, the present invention also provides a BeiDou high-precision positioning system, which includes a data acquisition device, a positioning observation module, and a high-precision positioning module.

[0055] The data acquisition device is used to deploy multi-source positioning equipment based on the BeiDou navigation satellite system, collect multi-source positioning data using the multi-source positioning equipment, and perform spatiotemporal alignment processing on the multi-source positioning data to obtain spatiotemporally aligned multi-source positioning data;

[0056] The positioning observation module is used to extract positioning quality features from multi-source positioning devices based on spatiotemporally aligned multi-source positioning data, and convert the positioning quality features into weight information of multi-source positioning devices; using the positioning coordinates of the BeiDou navigation satellite system as a reference, it integrates the weight information of multi-source positioning devices and spatiotemporally aligned multi-source positioning data to construct state information and observation information that characterize the positioning result of the target to be positioned in the multi-source positioning devices.

[0057] The high-precision positioning module is used to combine observation information with weighted extended Kalman filtering of state information to obtain the filtered results of the positioning coordinates of the Beidou navigation satellite system at different times, which are used as the high-precision fused positioning coordinates of the target to be positioned.

[0058] To achieve the BeiDou high-precision positioning method with multi-mode coordination in complex electromagnetic environments as described above.

[0059] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0060] Memory, storing at least one instruction;

[0061] Communication interfaces enable communication between electronic devices; and

[0062] The processor executes the instructions stored in the memory to implement the aforementioned method for multi-mode collaborative BeiDou high-precision positioning in complex electromagnetic environments.

[0063] To address the aforementioned issues, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned multi-mode collaborative BeiDou high-precision positioning method under complex electromagnetic environments.

[0064] Compared with existing technologies, this invention proposes a multi-mode collaborative BeiDou high-precision positioning method and system in complex electromagnetic environments, which has the following beneficial effects:

[0065] First, the BeiDou Navigation Satellite System provides global satellite positioning services. However, satellite signals may be affected by tall buildings, underground spaces, or environments with severe electromagnetic interference, leading to a decrease in positioning accuracy. In such situations, ultra-wideband (UWB) devices and integrated measurement units (IMUs) can play an important auxiliary role. UWB devices utilize wideband signals for short-range, high-precision positioning, possessing strong anti-multipath interference capabilities and extremely low power consumption. They can provide centimeter-level positioning accuracy in indoor environments, effectively compensating for the shortcomings of satellite positioning in complex environments. By fusing with BeiDou positioning results, UWB devices can provide reliable location information when satellite signals are weak or unavailable, ensuring positioning continuity. IMUs measure the acceleration and angular velocity of objects using accelerometers and gyroscopes, and combined with Kalman filtering, can achieve short-term positioning without satellite signals.

[0066] Meanwhile, for multimodal fusion positioning scenarios, to fully utilize the positioning information from different positioning devices and improve robustness and accuracy in complex electromagnetic environments, this application introduces a confidence lower bound mechanism for positioning quality features to quantify their positioning reliability boundaries. The confidence lower bound represents the minimum level of confidence a positioning device possesses even in the worst-case scenario, reflecting the tolerance boundary for its worst performance. By constructing confidence intervals, the stability and risk level of positioning devices in specific scenarios can be dynamically characterized. The improved positioning weight calculation formula is based on positioning quality features and incorporates a difference amplification factor based on the confidence lower bound, achieving significant enhancement for high-confidence, critical positioning devices. This method significantly improves the robustness of fusion positioning in complex environments such as non-line-of-sight, interference, and occlusion, reduces positioning offsets caused by the performance degradation of a single positioning device, and solves the problem that traditional fixed-weighting strategies cannot dynamically adapt to environmental changes, resulting in higher accuracy and adaptability. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating a multi-mode collaborative BeiDou high-precision positioning method under complex electromagnetic environments, provided by an embodiment of the present invention.

[0068] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0069] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0070] This application provides a method and system for multi-mode collaborative BeiDou high-precision positioning in complex electromagnetic environments. The executing entity of this method includes, but is not limited to, at least one electronic device that can be configured to execute the method provided in this application, such as a server or a terminal. In other words, the method can be executed by software or hardware installed on a terminal device or a server device, and the software may be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0071] Reference Figure 1 Embodiment 1 of the present invention is as follows:

[0072] A multi-mode collaborative BeiDou high-precision positioning method under complex electromagnetic environments includes the following steps:

[0073] S1: Deploy multi-source positioning equipment based on the BeiDou Navigation Satellite System, collect multi-source positioning data using the multi-source positioning equipment, and perform spatiotemporal alignment processing on the multi-source positioning data to obtain spatiotemporally aligned multi-source positioning data.

[0074] Deploy multi-source positioning equipment primarily based on the BeiDou Navigation Satellite System, and utilize this equipment to collect multi-source positioning data, including:

[0075] The multi-source positioning equipment includes the BeiDou navigation satellite system, ultra-wideband equipment, and IMU equipment, wherein the ultra-wideband equipment is divided into UWB anchor point equipment and UWB target equipment.

[0076] UWB anchor devices are deployed on equipment with known precise latitude and longitude coordinates as positioning reference points to provide distance measurement benchmarks for the target. UWB target devices are deployed on the target to be positioned to communicate with and measure distances with the UWB anchor devices. The relative position of the target with respect to the positioning reference point is calculated through distance measurement. IMU devices are deployed on the target to be positioned to obtain the acceleration, angular velocity and attitude information of the target in the latitude and longitude directions. The attitude information is represented in Euler angles. Satellite communication equipment is deployed on the target to be positioned for communication with the BeiDou Navigation Satellite System.

[0077] The collected multi-source positioning data includes BeiDou positioning data, UWB positioning data, and IMU positioning data. The BeiDou positioning data includes the positioning coordinates, pseudorange observations, carrier phase observations, carrier-to-noise ratio, multipath suppression ratio, and observation timestamps of the target to be positioned, all collected by the BeiDou navigation satellite system. The UWB positioning data includes the communication signal strength, ranging results, and communication timestamps between the target to be positioned and the UWB target device, all collected by the ultra-wideband equipment. The IMU positioning data includes the acceleration, velocity, angular velocity, attitude information, packet loss rate, and measurement timestamps of the target to be positioned in the inertial coordinate system, with the observation timestamps being the timestamps of communication between the satellite communication equipment and the BeiDou navigation satellite system.

[0078] Specifically, the observation timestamp is used as the BeiDou reference timestamp, and the satellite communication equipment and the BeiDou navigation satellite system perform real-time timestamp alignment.

[0079] It should be noted that the communication process of the satellite communication equipment is as follows: the satellite in the Beidou navigation satellite system transmits satellite signals to the satellite communication equipment, the satellite communication equipment records the time of receiving the satellite signals, calculates the pseudorange observation value of the satellite for the target to be located, and performs positioning processing of the target to be located.

[0080] Specifically, the carrier phase observation value is the change in the carrier wave of the satellite signal emitted by the BeiDou navigation satellite system; the carrier-to-noise ratio is the ratio of the satellite signal strength to the noise power density; the multipath suppression ratio is the ability of the BeiDou navigation satellite system to suppress multipath effects, quantized as the ratio of the signal power of the straight path to the signal power of the multipath path; the pseudorange observation value is the distance between the target to be positioned and the satellite; the target to be positioned is located by using pseudorange observation values ​​between at least four satellites and the target to obtain the positioning coordinates of the target in the BeiDou navigation satellite system, wherein the positioning coordinates are initially in the geocentric geofixed coordinate system; and the positioning formula of the target in the BeiDou navigation satellite system is:

[0081] ;

[0082] Where c represents the speed of light. Indicates the first The pseudorange observations of each satellite for the target to be located. This indicates that the target to be located received the first... The time when each satellite transmits its signal. Indicates the first The time it takes for a satellite to transmit satellite signals to the target to be located;

[0083] Indicates the first The coordinates of each satellite in the geocentric coordinate system This represents the positioning coordinates of the target in the BeiDou Navigation Satellite System. The positioning coordinates are determined by simultaneously solving four equations. Solve the problem;

[0084] In this embodiment, a two-way ranging method is used to measure the distance between the UWB anchor point device and the UWB target device.

[0085] The multi-source localization data is spatiotemporally aligned to obtain spatiotemporally aligned multi-source localization data, including:

[0086] Obtain the external calibration parameters of each coordinate system in the multi-source positioning device relative to the reference coordinate system, including translation vectors and rotation matrices, where the reference coordinate system is the geodetic coordinate system, including latitude, longitude and altitude;

[0087] Using external calibration parameters, the coordinate system-related data in the multi-source positioning data are transformed and mapped to the reference coordinate system to obtain multi-source positioning data with coordinate system spatial alignment.

[0088] Specifically, the coordinate system data in the multi-source positioning data includes the positioning coordinates of the target to be positioned collected by the Beidou navigation satellite system, as well as the acceleration and angular velocity of the target to be positioned along the three axes in the inertial coordinate system;

[0089] Using the clock of the satellite communication equipment as the master clock, the observation timestamps during the communication process are synchronized to the ultra-wideband equipment and the IMU equipment. The communication timestamps corresponding to the observation timestamps are used as the BeiDou time synchronization time. The communication timestamps and measurement timestamps of the ultra-wideband equipment and the IMU equipment are synchronized. The synchronization process is as follows:

[0090] Obtain the observation timestamp during the communication process ,in This represents the observation timestamp of the satellite communication equipment during its nth communication. N represents the number of communications by the satellite communication equipment;

[0091] Calculate the difference between the observation timestamp and the communication and measurement timestamps recorded by the ultra-wideband device and the IMU device during each communication process, where the observation timestamp... The difference between the communication timestamp recorded by the ultra-wideband device at the same time and the timestamp recorded by the device at the same time is , This represents the communication timestamp and observation timestamp recorded by the ultra-wideband device at the same time. The difference between the measurement timestamp recorded by the IMU device at the same time is , This represents the measurement timestamps recorded by the IMU devices at the same time, and the difference is used as the time observation model;

[0092] Initial construction of the first observation timestamp The time drift information is obtained by using Kalman filtering and combining it with a time observation model to update the time drift information for each observation timestamp. The updated time drift information includes the time offset and local clock drift rate at the observation timestamp. Based on this time drift information, the local clocks of the ultra-wideband (UWB) device and the IMU device are corrected. The UWB device at the nth observation timestamp... The time offset and local clock drift rate are respectively as follows: The IMU device at the nth observation timestamp The time offset and local clock drift rate are respectively as follows: The local clock correction formula for ultra-wideband devices and IMU devices is:

[0093] ;

[0094] ;

[0095] in, Represents the communication timestamp in ultra-wideband devices The corrected formula, Indicates the measurement timestamp in the IMU device The corrected formula;

[0096] ;

[0097] The local clocks of the ultra-wideband (UWB) device and the IMU device are corrected using a local clock correction formula, so that the multi-source positioning devices have a unified time reference. Interpolation is used to complete the BeiDou positioning data, UWB positioning data, and IMU positioning data to obtain multi-source positioning data with consistent length and spatiotemporal alignment. The number of timestamps after alignment of the spatiotemporal aligned multi-source positioning data is M. The spatiotemporal aligned multi-source positioning data consists of the positioning data of the multi-source positioning devices at M timestamps.

[0098] It should be noted that because ultra-wideband (UWB) devices and IMU devices have independent local clocks, they often deviate from high-precision time references such as BeiDou. Furthermore, local clocks are affected by factors such as temperature and aging, and their errors typically manifest as fixed time offsets and local clock drift rates. Without correction, this will lead to data alignment errors and inaccurate position calculations, severely impacting overall positioning accuracy. By analyzing the error sequence between the local clock timestamp and the BeiDou reference timestamp, and using Kalman filtering to estimate the offset and drift rate online, dynamic correction of the local clock can be achieved. The corrected timestamp has a unified time reference, effectively eliminating asynchronous errors between positioning devices, improving the time consistency and accuracy stability of IMU and UWB fusion positioning, and enabling UWB devices and IMU devices to stably provide reliable data and maintain high-precision continuous positioning capabilities even when BeiDou satellite signals are blocked or lost.

[0099] In this embodiment, for the nth observation timestamp, the time drift information transfer model of the Kalman filter is as follows:

[0100] ;

[0101] ;

[0102] in, This represents the time drift information transfer model for ultra-wideband devices, where Q represents process noise and T represents transpose. This indicates the nth observation timestamp of the ultra-wideband device. Time drift information, This indicates the (n-1)th observation timestamp of the ultra-wideband device. Time drift information;

[0103] The time observation model is as follows:

[0104] ;

[0105] in, This indicates the nth observation timestamp of the ultra-wideband device. The time observation model.

[0106] S2: Based on the spatiotemporally aligned multi-source positioning data, extract positioning quality features from the multi-source positioning devices and convert the positioning quality features into weight information of the multi-source positioning devices.

[0107] Based on spatiotemporally aligned multi-source positioning data, positioning quality features are extracted from multi-source positioning devices, including:

[0108] Positioning parameter data of multi-source positioning devices is extracted from spatiotemporally aligned multi-source positioning data. The positioning parameters of the BeiDou Navigation Satellite System include carrier-to-noise ratio and multipath suppression ratio. The positioning parameters of the ultra-wideband device include communication signal strength and ranging results. The positioning parameters of the IMU device include packet loss rate. The positioning parameter-related data are normalized to form the positioning parameter data.

[0109] Calculate the positioning quality characteristics of a multi-source positioning device at different aligned timestamps:

[0110] ;

[0111] ;

[0112] ;

[0113] ;

[0114] in, This represents the positioning quality characteristics of the BeiDou Navigation Satellite System after the m-th alignment timestamp. This represents the normalized carrier-to-noise ratio of the timestamp after the m-th alignment of the BeiDou navigation satellite system. This represents the normalized multipath suppression ratio of the m-th aligned timestamp of the BeiDou navigation satellite system.

[0115] This represents the positioning quality characteristics of the ultra-wideband device after the m-th alignment timestamp. This represents the mean normalized communication signal strength of the ultra-wideband device at the timestamp after the m-th alignment. The standard deviation of the normalized ranging result of the ultra-wideband device after the m-th alignment timestamp;

[0116] This represents the positioning quality characteristic of the IMU device at the timestamp after the m-th alignment. This represents the normalized packet loss rate of the IMU device after the m-th alignment timestamp. This indicates packet loss control parameters, settings. It is 5. Represents an exponential function with the natural constant as its base;

[0117] The positioning quality feature ranges from 0 to 1. The higher the positioning quality feature, the more accurate the positioning data of the timestamp after alignment.

[0118] The positioning quality features are converted into weight information for multi-source positioning devices, including:

[0119] The quantification formula for the weight information is:

[0120] ;

[0121] ;

[0122] in, This represents the weight information of the multi-source positioning device under the m-th aligned timestamp. These represent the weights of the BeiDou Navigation Satellite System, the ultra-wideband equipment, and the IMU equipment at the m-th aligned timestamp, respectively. This represents the weight of the j-th positioning device under the m-th aligned timestamp, where the BeiDou Navigation Satellite System, the ultra-wideband device, and the IMU device are the 1st to 3rd positioning devices, respectively.

[0123] This represents the positioning quality characteristic of the j-th positioning device at the timestamp after the m-th alignment. This indicates the preset confidence lower limit; specifically, setting... It is 0.2;

[0124] The positioning importance of the j-th positioning device is represented by the enhancement of the high-confidence positioning quality characteristics, which is used to describe the usage frequency of positioning devices under normal conditions. The positioning importance of the Beidou navigation satellite system, ultra-wideband equipment, and IMU equipment is set to 4, 2, and 1, respectively. Indicates positioning quality characteristics Difference amplification factor;

[0125] This represents the positioning quality characteristics of the k-th positioning device at the timestamp after the m-th alignment. This indicates the positioning importance of the k-th positioning device.

[0126] S3: Based on the positioning coordinates of the BeiDou Navigation Satellite System, the weight information of multi-source positioning devices and spatiotemporally aligned multi-source positioning data are integrated to construct state information and observation information that characterize the positioning results of the target to be positioned in the multi-source positioning devices.

[0127] By fusing weight information from multi-source positioning devices and spatiotemporally aligned multi-source positioning data, state information and observation information characterizing the positioning results of the target in the multi-source positioning devices are constructed, including:

[0128] The status information representing the positioning result of the target to be located in the multi-source positioning device is:

[0129] ;

[0130] in, This represents the status information of the target to be located after the m-th alignment timestamp. This represents the positioning coordinates of the target obtained by the BeiDou Navigation Satellite System after the m-th aligned timestamp, where the positioning coordinates are in a geodetic coordinate system and include latitude, longitude, and altitude. This represents the velocity vector of the target to be located, acquired by the IMU device after the m-th aligned timestamp. The velocity vector includes the velocity of the target in latitude, longitude and altitude. T represents transpose.

[0131] The observation information characterizing the positioning result of the target to be located in the multi-source positioning device is as follows:

[0132] ;

[0133] in, This represents the observation information of the target to be located after the m-th alignment timestamp. These represent the ranging vector, acceleration vector, and angular velocity vector of the target to be located at the m-th alignment timestamp, respectively. The ranging vector consists of the ranging results between the UWB target device on the target and the three nearest UWB anchor point devices. The acceleration vector includes the acceleration of the target in latitude, longitude, and altitude, and the angular velocity vector includes the angular velocity of the target in latitude, longitude, and altitude. This represents the weight information of the multi-source positioning device under the m-th aligned timestamp.

[0134] S4: Combine observation information with weighted extended Kalman filtering for reinforcement learning to obtain the filtered results of the positioning coordinates in the state information at different times, which are used as the high-precision fused positioning coordinates of the target to be located.

[0135] By combining observational information with weighted extended Kalman filtering of state information through reinforcement learning, the filtered results of the BeiDou navigation satellite system's positioning coordinates at different times are obtained, including:

[0136] The weighted extended Kalman filter processing procedure is as follows:

[0137] Obtain the status information of the target to be located after the first alignment timestamp. ;

[0138] The acquired state information is subjected to an M-1 step weighted extended Kalman filter to obtain the M-1 step filtered state information. ,in Status information The corresponding filtering results, Status information Obtain the corresponding filtering results. The weighted extended Kalman filter processing flow is as follows:

[0139] Will and Perform splicing, using the Transformer model to... Make a prediction and obtain Prediction results and covariance Make a prediction and obtain the prediction result. Correlation covariance ,in For the filtering result The corresponding covariance;

[0140] Using a fully connected network to predict results Mapped to predictive observations Calculate observation information With predictive observations The difference between the values ​​is used as the observation residual. The observation residual covariance matrix is ​​calculated, and Kalman gain calculation, state update, and covariance update are performed sequentially to obtain the state information. Corresponding filtering results and covariance ;

[0141] Calculate the filtering results Status information The differences between them are used to dynamically adjust the observation information using reinforcement learning. Weight information in The filtering result Status information The difference between them is ,in It is an L2 norm;

[0142] From the filtering results The positioning coordinates are extracted and used as the filtered result of the m-th aligned timestamp of the positioning coordinates of the Beidou navigation satellite system.

[0143] Specifically, the filtering results Status information The differences between observations serve as immediate rewards for reinforcement learning and, combined with historical difference trajectories, form the state input, driving the policy network to generate weight information for the next time step. This weight information is then updated to the observation information. By introducing a reinforcement learning-based adaptive adjustment mechanism for observation weights, the weight distribution of multi-source information from the BeiDou Navigation Satellite System, ultra-wideband equipment, and IMU equipment can be dynamically optimized during the filtering process based on actual state difference feedback. This adaptively strengthens the state update process and effectively avoids the impact of specific equipment anomalies or environmental degradation on the final result. Compared to traditional fixed weights or heuristic adjustment methods, this method exhibits stronger positioning stability and error suppression capabilities in scenarios such as complex electromagnetic environments, obstructed environments, or drastic fluctuations in the positioning quality of ultra-wideband equipment, significantly improving the robustness and versatility of the BeiDou high-precision positioning system. Furthermore, the weighted extended Kalman filter essentially performs a linearization approximation of a nonlinear system, integrating predicted and observed values ​​in the state update. Reinforcement learning further guides the estimation to iterate towards lower errors, making the state estimation closer to the true trajectory and improving the stability and accuracy of the filtering.

[0144] Example 2:

[0145] A BeiDou high-precision positioning system includes a data acquisition device, a positioning observation module, and a high-precision positioning module.

[0146] The data acquisition device is used to deploy multi-source positioning equipment based on the BeiDou navigation satellite system, collect multi-source positioning data using the multi-source positioning equipment, and perform spatiotemporal alignment processing on the multi-source positioning data to obtain spatiotemporally aligned multi-source positioning data;

[0147] The positioning observation module is used to extract positioning quality features from multi-source positioning devices based on spatiotemporally aligned multi-source positioning data, and convert the positioning quality features into weight information of multi-source positioning devices; using the positioning coordinates of the BeiDou navigation satellite system as a reference, it integrates the weight information of multi-source positioning devices and spatiotemporally aligned multi-source positioning data to construct state information and observation information that characterize the positioning result of the target to be positioned in the multi-source positioning devices.

[0148] The high-precision positioning module is used to combine observation information with weighted extended Kalman filtering of state information to obtain the filtered results of the positioning coordinates of the Beidou navigation satellite system at different times, which are used as the high-precision fused positioning coordinates of the target to be positioned.

[0149] To achieve the BeiDou high-precision positioning method with multi-mode collaboration in complex electromagnetic environments as described in Example 1.

[0150] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0151] It should be noted that the sequence numbers of the above embodiments of the present invention are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0152] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0153] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A Beidou high-precision positioning method in a complex electromagnetic environment and in a multi-mode coordination, characterized in that, The method comprises: S1: deploying a multi-source positioning device dominated by a Beidou navigation satellite system, collecting multi-source positioning data by using the multi-source positioning device, and performing space-time alignment processing on the multi-source positioning data to obtain multi-source positioning data after space-time alignment; The data related to the coordinate system in the multi-source positioning data are converted and mapped to a reference coordinate system by using external calibration parameters to obtain multi-source positioning data after space-time alignment of the coordinate system; The data related to the coordinate system in the multi-source positioning data include positioning coordinates of a target to be positioned collected by the Beidou navigation satellite system, and acceleration and angular velocity of three axes of the target to be positioned in an inertial coordinate system; The clock of the satellite communication device is used as a master clock, and the observation time stamp in the communication process is synchronized to the ultra-wideband device and the IMU device, and the time corresponding to the observation time stamp is used as a Beidou time synchronization time, and the communication time stamp and the measurement time stamp of the ultra-wideband device and the IMU device are synchronized, and the synchronization process is as follows: Obtaining observation timestamps in a communication process wherein represents an observation timestamp of the satellite communication device in the n-th communication, N represents the number of communications of the satellite communication device; the difference between the observation timestamp and the communication timestamp recorded by the ultra-wideband device and the measurement timestamp recorded by the IMU device during each communication process, wherein the observation timestamp the difference between the observation timestamp and the communication timestamp recorded by the ultra-wideband device is , the communication timestamp recorded by the ultra-wideband device at the same time, the observation timestamp the difference between the observation timestamp and the measurement timestamp recorded by the IMU device is , the measurement timestamp recorded by the IMU device at the same time, and the difference is taken as a time observation model; initially construct the first observation timestamp The time drift information of each observation timestamp is updated by using Kalman filtering and combining with a time observation model to obtain updated time drift information of each observation timestamp, wherein the time drift information includes time offset and local clock drift rate at the observation timestamp. The local clocks of the UWB device and the IMU device are corrected according to the time drift information. The time offset and the local clock drift rate of the UWB device at the nth observation timestamp are respectively. The time offset and the local clock drift rate of the IMU device at the nth observation timestamp are respectively. The local clock correction formula of the UWB device and the IMU device is: ； ； wherein, represents a correction formula for a communication timestamp in an ultra-wideband device, represents a correction formula for a communication timestamp in an ultra-wideband device, represents a correction formula for a measurement timestamp in an IMU device; and represents a correction formula for a measurement timestamp in an IMU device. ； S2: based on the multi-source positioning data after space-time alignment, extracting positioning quality features of the multi-source positioning device, and converting the positioning quality features into weight information of the multi-source positioning device; Based on the multi-source positioning data after space-time alignment, the positioning quality features of the multi-source positioning device are extracted, including: The positioning parameter data of the multi-source positioning device is extracted from the multi-source positioning data after space-time alignment, wherein the positioning parameters of the Beidou navigation satellite system include carrier-to-noise ratio and multipath suppression ratio, the positioning parameters of the ultra-wideband device include communication signal strength and ranging result, and the positioning parameters of the IMU device include packet loss rate, and the data related to the positioning parameters are normalized to form the positioning parameter data; The positioning quality features of the multi-source positioning device at different aligned time stamps are calculated: ； ； ； ； wherein, denotes a positioning quality feature of the Beidou navigation satellite system at the mth post-alignment timestamp, denotes a normalized carrier-to-noise ratio of the Beidou navigation satellite system at the mth post-alignment timestamp, denotes a normalized multipath mitigation ratio of the Beidou navigation satellite system at the mth post-alignment timestamp; a positioning quality feature representing the alignment after time stamp of the m-th ultra-wideband device, a mean of normalized communication signal strength representing the alignment after time stamp of the m-th ultra-wideband device, a standard deviation of normalized ranging results representing the alignment after time stamp of the m-th ultra-wideband device; a positioning quality feature representing the IMU device at the m-th alignment after timestamp, a normalized packet loss rate representing the IMU device at the m-th alignment after timestamp, a packet loss control parameter set to 5, denotes an exponential function with a natural constant as base; S3: taking the positioning coordinates of the Beidou navigation satellite system as a reference, fusing the weight information of the multi-source positioning device and the multi-source positioning data after space-time alignment, and constructing state information and observation information representing the positioning results of the target to be positioned in the multi-source positioning device; S4: combining the observation information to perform weighted extended Kalman filtering processing on the state information to obtain the filtering results of the positioning coordinates in the state information at different time points as high-precision fusion positioning coordinates of the target to be positioned; The positioning quality features are converted into weight information of the multi-source positioning device, including: The quantization formula of the weight information is: ； ； wherein, represents weight information of the multi-source positioning device at the mth alignment timestamp, represents weights of the Beidou navigation satellite system, the ultra-wideband device and the IMU device at the mth alignment timestamp, respectively, represents the weight of the jth positioning device at the mth alignment timestamp, wherein the Beidou navigation satellite system, the ultra-wideband device and the IMU device are the 1st-3rd positioning devices, respectively. a positioning quality feature indicative of the j-th positioning device at the m-th alignment post timestamp, is indicative of a pre-set lower confidence limit; denotes the positioning importance of the jth positioning device, and the positioning importance of the Beidou navigation satellite system, the ultra-wideband device, and the IMU device is set to be 4, 2, and 1, respectively; denotes the positioning quality feature , and the difference amplification factor of the positioning quality feature a positioning quality characteristic of the kth positioning device at the mth alignment post timestamp, a positioning importance of the kth positioning device.

2. The Beidou high-precision positioning method of multi-mode cooperation in a complex electromagnetic environment according to claim 1, characterized in that, The multi-source positioning device dominated by the Beidou navigation satellite system is deployed, and the multi-source positioning data is collected by using the multi-source positioning device, including: The multi-source positioning device includes the Beidou navigation satellite system, the ultra-wideband device, and the IMU device, wherein the ultra-wideband device includes a UWB anchor point device and a UWB target device; The collected multi-source positioning data includes Beidou positioning data, UWB positioning data, and IMU positioning data. The Beidou positioning data includes positioning coordinates of a target to be positioned collected by a Beidou navigation satellite system, pseudo-range observation values, carrier phase observation values, carrier-to-noise ratios in a positioning process, multipath suppression ratios, and observation time stamps. The UWB positioning data includes communication signal strengths between the target to be positioned and a UWB target device, ranging results, and communication time stamps collected by a UWB device. The IMU positioning data includes three-axis accelerations, speeds, angular speeds, attitude information of the target to be positioned, packet loss rates, and measurement time stamps in an inertial coordinate system. The observation time stamps are time stamps of communication between a satellite communication device and the Beidou navigation satellite system. 3.The Beidou high-precision positioning method of multi-mode coordination in a complex electromagnetic environment according to claim 2, characterized in that, The multi-source positioning data is subjected to spatio-temporal alignment processing to obtain spatio-temporally aligned multi-source positioning data, including: Obtaining external calibration parameters of each coordinate system in the multi-source positioning device relative to a reference coordinate system, including a translation vector and a rotation matrix. The reference coordinate system is a geodetic coordinate system, including latitude, longitude, and altitude. Converting and mapping data related to the coordinate system in the multi-source positioning data to the reference coordinate system using the external calibration parameters to obtain spatio-temporally aligned multi-source positioning data. Taking the clock of the satellite communication device as a master clock and synchronizing the observation time stamps in the communication process to the UWB device and the IMU device. The communication corresponding to the observation time stamps is taken as a Beidou time synchronization time. The communication time stamps and the measurement time stamps of the UWB device and the IMU device are synchronized.

4. The Beidou high-precision positioning method of multi-mode cooperation in a complex electromagnetic environment according to claim 1, characterized in that, Fusing the weight information of the multi-source positioning device and the spatio-temporally aligned multi-source positioning data to construct state information and observation information representing positioning results of the target to be positioned in the multi-source positioning device, including: The state information representing the positioning results of the target to be positioned in the multi-source positioning device is: ； wherein, represents state information of the target to be positioned at the mth post-alignment timestamp, represents positioning coordinates of the target to be positioned obtained by the Beidou navigation satellite system at the mth post-alignment timestamp, wherein the positioning coordinates are in a geodetic coordinate system, including longitude, latitude and height, represents a velocity vector of the target to be positioned collected by the IMU device at the mth post-alignment timestamp, wherein the velocity vector includes the velocity of the target to be positioned in longitude, latitude and height, and T represents transposition. The observation information representing the positioning results of the target to be positioned in the multi-source positioning device is: ； wherein, represents the observation information of the target to be positioned at the mth alignment time stamp, represents the ranging vector, the acceleration vector and the angular velocity vector of the target to be positioned at the mth alignment time stamp in turn, the ranging vector is composed of the ranging results between the UWB target device on the target to be positioned and the three nearest UWB anchor devices, the acceleration vector includes the acceleration of the target to be positioned in latitude, longitude and height, and the angular velocity vector includes the angular velocity of the target to be positioned in latitude, longitude and height, represents the weight information of the multi-source positioning device at the mth alignment time stamp.

5. The Beidou high-precision positioning method of multi-mode cooperation in a complex electromagnetic environment according to claim 4, characterized in that, Performing weighted extended Kalman filtering processing on the state information combined with the observation information to obtain filtering results of the positioning coordinates in the state information at different time instants as high-precision fused positioning coordinates of the target to be positioned, including: The weighted extended Kalman filtering processing procedure is: Acquiring state information of the target to be positioned at a first alignment time stamp ; M-1 step weighted extended Kalman filtering processing is performed on the obtained state information to obtain M-1 step filtered state information wherein is state information is the corresponding filtering result is state information is the corresponding filtering result The weighted extended Kalman filtering processing procedure of the M-1 step is as follows: Will and Perform splicing, using the Transformer model to... Make a prediction and obtain Prediction results and covariance Make a prediction and obtain the prediction result. Correlation covariance ,in For the filtering result The corresponding covariance; Status information The corresponding filtering results; Using a fully connected network to predict results Mapped to predictive observations Calculate observation information With predictive observations The difference between the values ​​is used as the observation residual. The observation residual covariance matrix is ​​calculated, and Kalman gain calculation, state update, and covariance update are performed sequentially to obtain the state information. Corresponding filtering results and covariance ; Computing the filtering result The difference between the state information The difference between the state information The difference between the state information ; The positioning coordinates are extracted from the filtering results as the positioning coordinates of the Beidou navigation satellite system at the mth alignment time stamp after filtering.

6. A Beidou high-precision positioning system for implementing the Beidou high-precision positioning method in a complex electromagnetic environment in multiple modes as claimed in any one of claims 1-5, characterized in that, The Beidou high-precision positioning system includes a data acquisition device, a positioning observation module, and a high-precision positioning module: The data acquisition device is used to deploy multi-source positioning devices guided by the Beidou navigation satellite system, collect multi-source positioning data using the multi-source positioning devices, and perform spatio-temporal alignment processing on the multi-source positioning data to obtain spatio-temporally aligned multi-source positioning data. The positioning observation module is used to extract positioning quality features of the multi-source positioning devices based on the spatio-temporally aligned multi-source positioning data, convert the positioning quality features into weight information of the multi-source positioning devices, and fuse the weight information of the multi-source positioning devices and the spatio-temporally aligned multi-source positioning data to construct state information and observation information representing positioning results of a target to be positioned in the multi-source positioning device based on positioning coordinates of the Beidou navigation satellite system. The high-precision positioning module is used for weighted extended Kalman filtering processing of state information combined with observation information, so as to obtain filtering results of positioning coordinates of the Beidou navigation satellite system at different times as high-precision fusion positioning coordinates of the target to be positioned.

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