Positioning system of moving target

Abstract

The invention discloses a positioning system of a moving target. The positioning system comprises a positioning signal receiving module; the receiving module receives positioning information; the multi-path effect suppression module is responsible for processing a multi-path effect problem in the received pre-processed signal; the fusion positioning module is responsible for fusing data of different positioning sources; the real-time positioning and map building module is responsible for building a map of a surrounding environment and estimating a target position in real time; the target state estimation and prediction module is responsible for estimating the dynamic state of the target and predicting the future position of the target according to the motion model of the target; according to the invention, the received positioning signals are screened and optimized through the multi-path effect suppression module by adopting an advanced signal processing technology, so that the interference caused by the multi-path effect is effectively eliminated. The method is particularly important in a complex scene, and the positioning accuracy can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of target positioning, and in particular to a positioning system for a mobile target. Background Art

[0002] Mobile target positioning refers to determining the real-time position of mobile objects (such as mobile phones, vehicles, people, etc.) in space through specific positioning technology. This technology is widely used in many fields such as smartphone positioning, vehicle navigation, logistics tracking, emergency rescue, etc., providing great convenience for people.

[0003] After searching, the invention patent with Chinese patent number CN116256739B discloses a mobile target coordinate positioning system based on radio multi-point detection, including: a target detection module: used to detect environmental disturbances to radio signals within the monitoring range by transmitting radio signals at fixed points of monitoring points; a target positioning module: used to locate the mobile target according to the detection data of the target detection module; a target height measurement module: used to locate the height of the mobile target; a target speed measurement module: used to measure the speed of the mobile target; a target display module: used to display according to the comprehensive positioning data of the mobile target. The present invention estimates the possible existence area of ​​the mobile target through a positioning algorithm, analyzes the deviation between the theoretical signal strength value and the actual measurement value of the monitoring point signal, and then solves the minimum value of the sum of the deviations to calculate the position of the mobile signal. At the same time, the positioning data of the mobile target can be automatically adjusted according to the required measurement accuracy, which does not increase the cost and improves the measurement accuracy. Compared with the prior art, the invention patent with Chinese patent number CN116256739B solves the problem of how to improve the positioning accuracy of the mobile target based on the existing radio monitoring equipment and monitoring data without increasing the cost.

[0004] However, in actual use, in complex scenarios, such as in areas with tall buildings in the city center, when a vehicle is traveling at high speed on the road, the signal may be blocked and reflected by surrounding tall buildings, bridges, underground parking lots and other facilities, causing a multipath effect, resulting in delays and strength changes in the received signal, making it difficult for the system to accurately calculate the true position of the target. Therefore, a positioning system for mobile targets is proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art in complex scenarios, such as in areas with tall buildings in the city center, when a vehicle is traveling at high speed on the road, the signal may be blocked and reflected by surrounding tall buildings, bridges, underground parking lots and other facilities, causing a multipath effect, resulting in delays and strength changes in the received signal, making it difficult for the system to accurately calculate the true position of the target, and to propose a mobile target positioning system.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A positioning system for a mobile target, comprising:

[0008] Positioning signal receiving module; responsible for receiving positioning information;

[0009] Multipath effect suppression module: responsible for processing the multipath effect problem in the received pre-processed signal;

[0010] Fusion positioning module: responsible for fusing data from different positioning sources;

[0011] Real-time positioning and map construction module: responsible for building a map of the surrounding environment and estimating the target position in real time;

[0012] Target state estimation and prediction module: responsible for estimating the dynamic state of the target and predicting its future position based on the target's motion model;

[0013] The positioning information receiving module is responsible for receiving target positioning information from various signal sources, including global navigation satellite systems, Wi-Fi and Bluetooth positioning, UWB signals, and lidar, etc. The positioning signal receiving module transmits the received information to the multipath effect suppression module, which is responsible for using signal processing technology to screen and optimize the received information, eliminate the interference caused by the multipath effect, and provide clear and accurate signal input for subsequent positioning calculations. The multipath effect suppression module transmits the signal after multipath effect suppression to the fusion positioning module, which is responsible for using the Kalman filtering method to combine the data of multiple sensors through an iterative process to minimize the prediction error, thereby providing a more accurate positioning result. The data from different positioning sources (such as satellite navigation, ground base stations, inertial navigation, etc.) are fused, and the fused positioning module passes the fused positioning result to the real-time positioning and map construction module. The real-time positioning and map construction module is responsible for estimating the target position in real time and building a map of the surrounding environment in real time through on-board sensors (such as lidar, camera, etc.) when the global navigation satellite system information cannot be accurately obtained, so as to help the target to be positioned in real time and provide navigation function. The real-time positioning and map construction module passes the real-time positioning information and map data to the target state estimation and prediction module. The target state estimation and prediction module estimates the motion state of the target in real time, and predicts the motion trajectory of the target in the future based on the motion model of the target, such as the dynamic model of the vehicle.

[0014] The above technical solution further includes:

[0015] Preferably, the specific steps of receiving positioning information are:

[0016] Initialize the system receiving equipment, check the antenna connection, calibrate the receiving frequency, set the receiving parameters, etc. After the initialization is completed, the positioning signal receiver searches for the positioning signal by scanning a specific frequency range to find the transmission signal from the satellite or ground base station;

[0017] When a positioning signal is detected, the positioning signal receiving module captures the positioning signal. The capture process usually includes locking the frequency and phase of the signal and extracting the coding information of the signal. After successful capture, the positioning signal receiving module enters the tracking phase and continuously monitors the signal strength and phase changes to ensure the stability and accuracy of the signal. If the signal quality decreases or is lost, the module will try to recapture the signal.

[0018] Demodulate and decode the tracked positioning signal. Demodulation is the process of converting the received RF signal into a baseband signal, while decoding is the process of extracting useful positioning information (such as satellite timestamp, satellite orbit parameters, etc.) from the baseband signal, and verifying and correcting the decoded positioning information.

[0019] The verified and corrected positioning data is output to the multipath effect suppression module for further processing.

[0020] Preferably, the multipath effect suppression module includes a signal preprocessing unit, a multipath signal detection unit, a multipath signal separation unit, a multipath effect suppression unit, a signal reinforcement and compensation unit, a multipath effect evaluation unit and an output unit, wherein the signal preprocessing unit is responsible for preprocessing the received signal, the multipath signal detection unit is responsible for detecting the multipath components in the received signal, the multipath signal separation unit separates the multipath signal, the multipath effect suppression unit is responsible for eliminating interference in the multipath effect, the signal reinforcement and compensation unit is responsible for strengthening or compensating the signal, the multipath effect evaluation unit is responsible for evaluating the effect of multipath effect suppression and adjusting the previous processing strategy according to the evaluation result, and the output unit is responsible for outputting the processed signal to the fusion positioning module.

[0021] Preferably, the specific process of processing the multipath effect problem in the received pre-processed signal is:

[0022] Perform pre-processing operations on the received signal, such as filtering and denoising, to improve the signal quality and extract key features of the signal, such as signal strength, phase, frequency, etc., for subsequent multipath effect detection and suppression;

[0023] The presence of multipath effect can be identified by analyzing the Doppler frequency shift of the signal. The Doppler frequency shift is the frequency change caused by the relative motion between the signal source and the receiver. The specific calculation formula of the Doppler frequency shift is: Where Δf is the Doppler frequency shift, f0 is the original frequency of the signal source, v is the relative speed between the signal source and the receiver, c is the speed of light, and θ is the angle between the signal source and the receiver;

[0024] The presence of a multipath effect can be further confirmed by analyzing the phase change of the signal. Multipath signals will cause phase changes due to different propagation paths. The specific calculation formula for phase change is: in, is the phase change, Δd is the difference in propagation paths, and λ is the wavelength of the signal;

[0025] After confirming the existence of multipath effect, the adaptive filter is used to adjust the filter parameters according to the real-time changes of the signal to suppress it, which can reduce the impact of multipath signal. The specific calculation formula is: y(n) = w(n)T*x(n), where y(n) is the output of the filter, w(n) is the weight coefficient vector of the filter, T is the transpose operation of the matrix, and x(n) is the input signal vector;

[0026] By analyzing the Doppler frequency shift and phase change characteristics of the signal, multipath signals are identified and eliminated, and only direct signals or main signals are retained to improve positioning accuracy. The remaining signals are reconstructed to restore the waveform and characteristics of the original signal.

[0027] Preferably, the specific process of fusing data from different positioning sources is:

[0028] Each positioning source provides measurement data for z i , where i represents different positioning sources, and the measurement model provided by each positioning source i is Among them, H i is the measurement matrix of the localization source i, which is used to transform the state Mapped to the measurement space, v i is the measurement noise, which represents the noise caused by sensor errors and environmental factors;

[0029] The Kalman filter calculates the influence of each positioning source on the final state estimation in a weighted manner. First, the Kalman gain K used to represent the fusion weight of different positioning source data is calculated. k , Among them, P k - is the forecast error covariance matrix, R i is the measurement noise covariance matrix of the ith positioning source, and then through the Kalman gain K k Forecast status To update, in, is the state estimate after fusion, is the predicted state at time k, and finally updates the covariance matrix P k , which represents the uncertainty after fusion: Where I is the identity matrix.

[0030] Preferably, the specific steps of constructing a map of the surrounding environment and estimating the target position in real time are:

[0031] First, multiple sensors on the vehicle (such as lidar, cameras, etc.) collect data in real time to provide the system with environmental information, including surrounding obstacles, landmarks, road markings, etc., and pre-process the data of multiple sensors to extract features from image data, such as edge detection and corner detection;

[0032] Using the image feature matching algorithm, the current image is matched with the features in the known map. Based on the matching results, the current vehicle's position and posture are determined. The point cloud data obtained by the lidar is converted into three-dimensional coordinates in the map. According to the vehicle's position and posture, the point cloud data is superimposed on the map to form a three-dimensional point cloud map. The map is smoothed using a filtering algorithm (such as Kalman filtering, particle filtering, etc.) to eliminate noise and errors.

[0033] Preferably, the target state estimation and prediction module includes a state estimation unit, a motion model and state transfer unit, a motion prediction unit, an error estimation and correction unit, and a prediction result correction unit. The state estimation unit is responsible for estimating the dynamic state of the target based on the real-time data of the sensor, and the motion model and state transfer unit is responsible for describing the motion law of the target according to the motion characteristics of the target. The motion prediction unit is responsible for predicting the state of the target at a future moment according to the motion model, the error estimation and correction unit is responsible for estimating and correcting the error in the state estimation process to ensure the accuracy of the target state, and the prediction result correction unit is responsible for correcting the prediction result so that the predicted trajectory is more in line with the actual situation.

[0034] Preferably, the specific steps of establishing the motion model include:

[0035] Collect and analyze data: Collect vehicle driving data (including speed, acceleration, steering angle, position, etc.) in different scenarios and pre-process the collected data;

[0036] Determine model parameters and structure: According to the vehicle's motion characteristics, select appropriate parameters to describe the vehicle's motion state, such as mass, speed, acceleration, steering angle, etc., and determine the model structure, including the form of the equation and the relationship between variables;

[0037] Establishing mathematical model: Based on the collected data and analysis results, establish the dynamic mathematical model of the vehicle, select key variables that can describe the vehicle's motion state, establish differential equations that describe the vehicle's motion state based on Newton's second law and the vehicle's motion characteristics, and consider the impact of external factors on vehicle motion, such as wind resistance, road conditions, tire friction, etc.;

[0038] Model verification and optimization: Use the collected data to verify the model, compare the differences between the model's prediction results and the actual data, and adjust the model's parameters and structure based on the verification results to improve the model's accuracy and robustness. Perform iterative optimization and continuously iterate the model verification and optimization process until the difference between the model's prediction results and the actual data reaches an acceptable range.

[0039] The present invention has the following beneficial effects:

[0040] 1. In the present invention, the multipath effect suppression module uses advanced signal processing technology to screen and optimize the received positioning signal, effectively eliminating the interference caused by the multipath effect. This is particularly important in complex scenarios and can significantly improve the accuracy of positioning.

[0041] 2. In the present invention, the fusion positioning module uses the Kalman filter method to fuse data from different positioning sources. This fusion of multi-source data not only improves the accuracy of positioning, but also enhances the robustness of the system, and can ensure the accuracy of positioning even when the signal of a certain positioning source is unstable or fails. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a system architecture diagram of a mobile target positioning system proposed by the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0044] like Figure 1 As shown, a positioning system for a mobile target includes:

[0045] Positioning signal receiving module; responsible for receiving positioning information;

[0046] Multipath effect suppression module: responsible for processing the multipath effect problem in the received pre-processed signal;

[0047] Fusion positioning module: responsible for fusing data from different positioning sources;

[0048] Real-time positioning and map construction module: responsible for building a map of the surrounding environment and estimating the target position in real time;

[0049] Target state estimation and prediction module: responsible for estimating the dynamic state of the target and predicting its future position based on the target's motion model;

[0050] The positioning information receiving module is responsible for receiving target positioning information from various signal sources. The positioning signal receiving module passes the received information to the multipath effect suppression module. The multipath effect suppression module is responsible for using signal processing technology to screen and optimize the received information and eliminate the interference caused by the multipath effect. The multipath effect suppression module passes the signal after multipath effect suppression to the fusion positioning module. The fusion positioning module is responsible for using the Kalman filtering method to fuse data from different positioning sources. The fusion positioning module passes the fusion positioning result to the real-time positioning and map construction module. The real-time positioning and map construction module is responsible for estimating the target position in real time through the vehicle-mounted sensors and building a map of the surrounding environment in real time when the global navigation satellite system information cannot be accurately obtained. The real-time positioning and map construction module passes the real-time positioning information and map data to the target state estimation and prediction module. The target state estimation and prediction module estimates the motion state of the target in real time and predicts the motion trajectory of the target in the future based on the target's motion model.

[0051] In one embodiment, the specific steps of receiving the positioning information are:

[0052] Initialize the system receiving device. After the initialization is completed, the positioning signal receiver searches for the positioning signal;

[0053] When a positioning signal is detected, the positioning signal receiving module captures the positioning signal. After successful capture, the positioning signal receiving module enters the tracking phase;

[0054] Demodulate and decode the tracked positioning signal, and verify and correct the decoded positioning information;

[0055] The verified and corrected positioning data is output to the multipath effect suppression module for further processing.

[0056] In the embodiment of the present invention, signal tracking estimates the time and position of signal arrival through a matched filter, and its specific formula is r(t)=∫x(t)·h(t)dt, where r(t) is the correlation value of the correlation signal, x(t) is the received signal, and h(t) is the matched filter response. The goal of the demodulation and decoding process is to extract the positioning information carried therein. Common positioning signal demodulation methods include using synchronous demodulation or using a demodulation algorithm that matches the signal encoding. For GPS positioning signals, the demodulation formula is Where y(t) is the demodulated signal, x(t) is the received signal, and f 1 is the carrier frequency, is the phase shift.

[0057] In one embodiment, the multipath effect suppression module includes a signal preprocessing unit, a multipath signal detection unit, a multipath signal separation unit, a multipath effect suppression unit, a signal reinforcement and compensation unit, a multipath effect evaluation unit and an output unit. The signal preprocessing unit is responsible for preprocessing the received signal, the multipath signal detection unit is responsible for detecting the multipath components in the received signal, the multipath signal separation unit separates the multipath signal, the multipath effect suppression unit is responsible for eliminating interference in the multipath effect, the signal reinforcement and compensation unit is responsible for strengthening or compensating the signal, the multipath effect evaluation unit is responsible for evaluating the effect of multipath effect suppression, and the output unit is responsible for outputting the processed signal to the fusion positioning module.

[0058] In the embodiment of the present invention, signal preprocessing includes denoising, gain control, etc., to facilitate subsequent multipath signal detection and separation.

[0059] In one embodiment, the specific process of processing the multipath effect problem in the received pre-processed signal is as follows:

[0060] Perform preprocessing operations on the received signal and extract the key features of the signal;

[0061] By analyzing the Doppler frequency shift of the signal, it is possible to identify whether there is a multipath effect. The specific calculation formula for the Doppler frequency shift is: Where Δf is the Doppler frequency shift, f 0 is the original frequency of the signal source, v is the relative speed between the signal source and the receiver, c is the speed of light, and θ is the angle between the signal source and the receiver;

[0062] Then, the presence of multipath effect is further confirmed by analyzing the phase change of the signal. The specific calculation formula of the phase change is: in, is the phase change, Δd is the difference in propagation paths, and λ is the wavelength of the signal;

[0063] After confirming the existence of multipath effect, an adaptive filter is used to adjust the filter parameters according to the real-time changes of the signal to suppress it. The specific calculation formula is: y(n) = w(n)T*x(n), where y(n) is the output of the filter, w(n) is the weight coefficient vector of the filter, T is the transpose operation of the matrix, and x(n) is the input signal vector;

[0064] By analyzing the Doppler frequency shift and phase change characteristics of the signal, multipath signals are identified and eliminated, the remaining signals are reconstructed, and the waveform and characteristics of the original signal are restored.

[0065] In one embodiment, the specific process of fusing data from different positioning sources is as follows:

[0066] Each positioning source provides measurement data for z i , where i represents different positioning sources, and the measurement model provided by each positioning source i is Among them, H i is the measurement matrix of localization source i, v i is the measurement noise;

[0067] The Kalman filter calculates the influence of each positioning source on the final state estimation in a weighted manner. First, the Kalman gain K used to represent the fusion weight of different positioning source data is calculated. k , in, is the forecast error covariance matrix, R i is the measurement noise covariance matrix of the ith positioning source, and then through the Kalman gain K k Forecast status To update, in, is the state estimate after fusion, is the predicted state at time k, and finally updates the covariance matrix P k , which represents the uncertainty after fusion: Where I is the identity matrix.

[0068] In the embodiment of the present invention, the initial state of the system is defined in the initial stage of Kalman filtering. Define the error covariance P of the initial state estimate 0 ,In the prediction step, the Kalman filter uses the state transition model and control input to predict the future state of the target. Assume that the target dynamic model is: Bu k , where F is the state transfer matrix, which represents the state change from time k-1 to time k, B is the control input matrix, which represents the influence of the control input on the state, and u kis the control input, such as acceleration, etc. Then the corresponding prediction covariance for: Where Q is the process noise covariance matrix, which represents the error caused by imperfect model or external interference.

[0069] In one embodiment, the specific steps of constructing a map of the surrounding environment and estimating the target position in real time are:

[0070] First, data is collected in real time through various sensors on the vehicle, and the data from various sensors is pre-processed;

[0071] Using the image feature matching algorithm, the current image is matched with the features in the known map. Based on the matching results, the current vehicle's position and posture are determined. The point cloud data obtained by the lidar is converted into three-dimensional coordinates in the map. According to the vehicle's position and posture, the point cloud data is superimposed on the map to form a three-dimensional point cloud map, and the map is smoothed using a filtering algorithm.

[0072] In an embodiment of the present invention, an image feature matching algorithm is used to match the current image with features in a known map, including feature point detection and description and feature matching. Feature point detection and description uses the FAST algorithm to extract key feature points in the image, and determines whether the point is a corner point by detecting the brightness change in the neighborhood around each pixel. For each pixel I (x, y), the FAST algorithm defines a circular neighborhood around it. If the pixel meets certain conditions in these neighborhood points, then it is considered to be a feature point. Feature matching finds the match between the feature points in the image and the corresponding feature points in the map through the nearest neighbor search. The matching process is measured by the Euclidean distance: Among them, x i and i The point cloud data acquired by the LiDAR is converted into three-dimensional coordinates in the map using homogeneous coordinate transformation. Assuming that the transformation from the LiDAR coordinate system to the world coordinate system is given by the rotation matrix R and the translation vector t, the transformation formula of the point cloud coordinate P is: world =R·P lidar +t, where P world Point cloud coordinates in the world coordinate system, P lidar are the point cloud coordinates in the lidar.

[0073] In one embodiment, the target state estimation and prediction module includes a state estimation unit, a motion model and state transfer unit, a motion prediction unit, an error estimation and correction unit, and a prediction result correction unit. The state estimation unit is responsible for estimating the dynamic state of the target based on the real-time data of the sensor, and the motion model and state transfer unit is responsible for describing the motion law of the target according to the motion characteristics of the target. The motion prediction unit is responsible for predicting the state of the target at a future time according to the motion model, the error estimation and correction unit is responsible for estimating and correcting the error in the state estimation process, and the prediction result correction unit is responsible for correcting the prediction result.

[0074] In one embodiment, the specific steps of establishing the motion model include:

[0075] Collect and analyze data: collect vehicle driving data in different scenarios and pre-process the collected data;

[0076] Determine model parameters and structure: According to the vehicle's motion characteristics, select appropriate parameters to describe the vehicle's motion state and determine the model's structure;

[0077] Establish mathematical model: Based on the collected data and analysis results, establish the dynamic mathematical model of the vehicle, select key variables that can describe the vehicle's motion state, establish differential equations that describe the vehicle's motion state based on Newton's second law and the vehicle's motion characteristics, and consider the impact of external factors on the vehicle's motion;

[0078] Model verification and optimization: Use the collected data to verify the model, compare the differences between the model's prediction results and the actual data, adjust the model's parameters and structure based on the verification results, and perform iterative optimization.

[0079] In the embodiment of the present invention, in establishing the mathematical model, it is assumed that the state of the vehicle is determined by the position x 1 (t), speed v 1 (t) and heading angle θ 1 (t) describes that for a vehicle moving in the forward direction, the vehicle is affected by external forces (such as engine force, friction, slope, etc.) on the ground, which can be expressed as Where m is the vehicle weight, is the vehicle acceleration, F enginc is the thrust provided by the engine, F friction is the reaction force generated by the friction between the tire and the ground, F air is the air resistance, F road It is the additional force generated by factors such as road slope and friction. air Calculated by the following formula: Where ρ is the air density, C dis the vehicle's air resistance coefficient, A is the vehicle's front projection area, v 1 (t) is the speed of the vehicle. road Calculated by the following formula: F road =m·g·sin(θ slope ), where g is the acceleration due to gravity, θ slope It is the road slope. Although the embodiments of the present invention have been shown and described, it is understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A mobile target positioning system, characterized in that: include: Positioning signal receiving module; Responsible for receiving positioning information; Multipath effect suppression module: responsible for processing the multipath effect problem in the received pre-processed signal; Fusion positioning module: responsible for fusing data from different positioning sources; Real-time positioning and map construction module: responsible for building a map of the surrounding environment and estimating the target position in real time; Target state estimation and prediction module: responsible for estimating the dynamic state of the target and predicting its future position based on the target's motion model; The positioning information receiving module is responsible for receiving target positioning information from various signal sources, the positioning signal receiving module transmits the received information to the multipath effect suppression module, the multipath effect suppression module is responsible for using signal processing technology to screen and optimize the received information and eliminate the interference caused by the multipath effect, the multipath effect suppression module transmits the signal after the multipath effect is suppressed to the fusion positioning module, the fusion positioning module is responsible for using the Kalman filtering method to fuse data from different positioning sources, the fusion positioning module transmits the fusion positioning result to the real-time positioning and map construction module, the real-time positioning and map construction module is responsible for estimating the target position in real time and constructing a map of the surrounding environment in real time through the vehicle-mounted sensor when the global navigation satellite system information cannot be accurately obtained, the real-time positioning and map construction module transmits the real-time positioning information and map data to the target state estimation and prediction module, the target state estimation and prediction module estimates the motion state of the target in real time, and predicts the motion trajectory of the target in the future based on the motion model of the target.

2. A mobile target positioning system according to claim 1, characterized in that: The specific steps of receiving positioning information are: Initialize the system receiving device. After the initialization is completed, the positioning signal receiver searches for the positioning signal; When a positioning signal is detected, the positioning signal receiving module captures the positioning signal. After successful capture, the positioning signal receiving module enters the tracking phase; Demodulate and decode the tracked positioning signal, and verify and correct the decoded positioning information; The verified and corrected positioning data is output to the multipath effect suppression module for further processing.

3. A mobile target positioning system according to claim 1, characterized in that: The multipath effect suppression module includes a signal preprocessing unit, a multipath signal detection unit, a multipath signal separation unit, a multipath effect suppression unit, a signal reinforcement and compensation unit, a multipath effect evaluation unit and an output unit. The signal preprocessing unit is responsible for preprocessing the received signal, the multipath signal detection unit is responsible for detecting the multipath components in the received signal, the multipath signal separation unit separates the multipath signal, the multipath effect suppression unit is responsible for eliminating the interference in the multipath effect, the signal reinforcement and compensation unit is responsible for strengthening or compensating the signal, the multipath effect evaluation unit is responsible for evaluating the effect of multipath effect suppression, and the output unit is responsible for outputting the processed signal to the fusion positioning module.

4. A mobile target positioning system according to claim 1, characterized in that: The specific process of processing the multipath effect problem in the received pre-processed signal is as follows: Perform preprocessing operations on the received signal and extract the key features of the signal; By analyzing the Doppler frequency shift of the signal, it is possible to identify whether there is a multipath effect. The specific calculation formula for the Doppler frequency shift is: Where Δf is the Doppler shift, f0 is the original frequency of the signal source, v is the relative speed between the signal source and the receiver, c is the speed of light, and θ is the angle between the signal source and the receiver; Then, by analyzing the phase change of the signal, we can further confirm whether there is a multipath effect phase change. The specific calculation formula for the phase change is: in, is the phase change, Δd is the difference in propagation paths, and λ is the wavelength of the signal; After confirming the existence of multipath effect, an adaptive filter is used to adjust the filter parameters according to the real-time changes of the signal to suppress it. The specific calculation formula is: y(n) = w(n)T*x(n), where y(n) is the output of the filter, w(n) is the weight coefficient vector of the filter, T is the transpose operation of the matrix, and x(n) is the input signal vector; By analyzing the Doppler frequency shift and phase change characteristics of the signal, multipath signals are identified and eliminated, the remaining signals are reconstructed, and the waveform and characteristics of the original signal are restored.

5. A mobile target positioning system according to claim 1, characterized in that: The specific process of fusing data from different positioning sources is as follows: Each positioning source provides measurement data for z i , where i represents different positioning sources, and the measurement model provided by each positioning source i is Among them, H i is the measurement matrix of localization source i, v i is the measurement noise; The Kalman filter calculates the influence of each positioning source on the final state estimation in a weighted manner. First, the Kalman gain K used to represent the fusion weight of different positioning source data is calculated. k , in, is the forecast error covariance matrix, R i is the measurement noise covariance matrix of the ith positioning source, and then through the Kalman gain K k Forecast status To update, in, is the state estimate after fusion, is the predicted state at time k, and finally updates the covariance matrix P k , which represents the uncertainty after fusion: Where I is the identity matrix.

6. A mobile target positioning system according to claim 1, characterized in that: The specific steps of constructing a map of the surrounding environment and estimating the target position in real time are as follows: First, data is collected in real time through various sensors on the vehicle, and the data from various sensors is pre-processed; Using the image feature matching algorithm, the current image is matched with the features in the known map. Based on the matching results, the current vehicle's position and posture are determined. The point cloud data obtained by the lidar is converted into three-dimensional coordinates in the map. According to the vehicle's position and posture, the point cloud data is superimposed on the map to form a three-dimensional point cloud map, and the map is smoothed using a filtering algorithm.

7. A mobile target positioning system according to claim 1, characterized in that: The target state estimation and prediction module includes a state estimation unit, a motion model and state transfer unit, a motion prediction unit, an error estimation and correction unit, and a prediction result correction unit. The state estimation unit is responsible for estimating the dynamic state of the target based on the real-time data of the sensor, and the motion model and state transfer unit is responsible for describing the motion law of the target according to the motion characteristics of the target. The motion prediction unit is responsible for predicting the state of the target at a future moment according to the motion model, the error estimation and correction unit is responsible for estimating and correcting the error in the state estimation process, and the prediction result correction unit is responsible for correcting the prediction result.

8. A mobile target positioning system according to claim 7, characterized in that: The specific steps of establishing the motion model include: Collect and analyze data: collect vehicle driving data in different scenarios and pre-process the collected data; Determine model parameters and structure: According to the vehicle's motion characteristics, select appropriate parameters to describe the vehicle's motion state and determine the model's structure; Establish mathematical model: Based on the collected data and analysis results, establish the dynamic mathematical model of the vehicle, select key variables that can describe the vehicle's motion state, establish differential equations that describe the vehicle's motion state based on Newton's second law and the vehicle's motion characteristics, and consider the impact of external factors on the vehicle's motion; Model verification and optimization: Use the collected data to verify the model, compare the differences between the model's prediction results and the actual data, adjust the model's parameters and structure based on the verification results, and perform iterative optimization.

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