Vehicle detection method and system of millimeter wave radar

Abstract

The invention discloses a millimeter wave radar vehicle detection method and system, and relates to the technical field of radar signal processing, and the method comprises the following steps: S1, respectively installing a road end millimeter wave radar all-in-one machine and a vehicle end millimeter wave radar all-in-one machine at the top of an underground roadway and a trackless rubber-tyred vehicle, the millimeter wave radar all-in-one machine emits millimeter waves and receives echoes to obtain original radar echo data, wherein the original radar echo data comprises the position and motion information of the trackless rubber-tyred vehicle in the detection roadway; according to the method, a mine environment electromagnetic model and a radar simulation model are established, and according to the actual structure and the material of a mine tunnel, the absorption condition of radar signals is further combined, so that the performance parameter signals of the millimeter-wave radar all-in-one machines arranged under a mine and on a trackless rubber-tyred vehicle are subjected to precise optimization parameter adjustment processing, and the performance parameters of the millimeter-wave radar all-in-one machines are obtained. The interference of reflection, diffraction and shielding in a complex mining environment is avoided, and the accuracy and precision of the feature data extracted from the original radar echo data are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle control devices, and particularly relates to a method and system for detecting vehicles using millimeter-wave radar. Background Art

[0002] Currently, most mines use positioning based on UWB technology. Since the UWB technology cannot accurately identify the requirements of vehicle speed, vehicle direction, and vehicle distance, the congestion problem cannot be solved in many cases. Some mines use lidar, but due to the influence of factors such as the changing underground environment over time, signal detection and signal coverage have blind spots, resulting in problems such as a high false alarm rate and large distance errors in positioning target objects. It is difficult to capture effective and accurate signal sources, and from a systematic perspective, it is impossible to effectively count the traffic flow and vehicle speed statistics data in a specific roadway during a specific time period. Therefore, under the multiple factors of equipment hardware failures, complex underground mine environment interference, electromagnetic waves generated by wireless signal sources, and unstable positions of base station signal coverage, neither UWB technology nor lidar technology can effectively and accurately detect vehicles.

[0003] The current transportation environment of underground trackless rubber-tired vehicles relies on the self-discipline of drivers to abide by the traffic regulations of underground trackless rubber-tired vehicles. Due to the small space in the underground environment, especially in the roadway for round trips to the working face, drivers often do not know whether there are vehicles traveling in the roadway, their traveling positions, and how far they are from the chamber, resulting in roadway traffic jams. In this case, one trackless rubber-tired vehicle must reverse to give way to the other, which will lead to the defect of reduced utilization rate of trackless rubber-tired vehicles. Even more, when multiple trackless rubber-tired vehicles enter the same roadway during the same time period, it will inevitably lead to vehicle congestion and may also cause unnecessary accidents.

[0004] The prior art has the following deficiencies: However, in a complex mine environment, the transportation environment of trackless rubber-tired vehicles has unique terrain and irregular tunnel walls, which may cause reflection and diffraction phenomena of millimeter-wave radar in the complex mine environment, resulting in misjudgment of the vehicle detection system. And in a narrow mine roadway, when there are multiple trackless rubber-tired vehicle targets at the same time, the millimeter-wave radar signal may be affected by reflection and occlusion, making it difficult for the vehicle detection system to accurately identify and locate multiple targets. In addition, electromagnetic interference generated by radio transmission equipment and electric mechanical equipment will affect the stability of radar signals and the robustness of the vehicle detection system.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and therefore it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The object of the present invention is to provide a vehicle detection method and system for a millimeter-wave radar. The present invention obtains the position and movement information of vehicles in the roadway, the radar information and movement information of the trackless rubber-tired vehicle through a millimeter-wave radar integrated machine, extracts feature data through Kalman filtering and performs radar imaging processing, establishes a mine environment electromagnetic model and a radar simulation model, improves the accuracy of radar performance parameters, and uses a convolutional neural network model to deeply analyze and optimize the detection data to generate a risk controllable coefficient, and performs visual warning processing by comparing with the warning decision threshold to solve the problems in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: A vehicle detection method for a millimeter-wave radar, including the following steps: S1. Install a roadside and vehicle-mounted millimeter-wave radar integrated machine on the top of the underground roadway and the trackless rubber-tired vehicle respectively. Transmit millimeter waves through the millimeter-wave radar integrated machine and receive the echoes to obtain the original radar echo data. The original radar echo data includes the position and movement information of the trackless rubber-tired vehicle in the detected roadway and the self-movement speed information of the detected trackless rubber-tired vehicle. S2. Apply Kalman filtering technology to perform preprocessing operations of noise removal and data fusion on the original radar echo data for vehicle detection, generate preprocessed data, and perform feature extraction and interference suppression on the preprocessed data to generate feature data for subsequent processing. S3. Use the signal processing algorithms of synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) for radar imaging recognition to obtain radar imaging data, and combine the UWB technology to locate the position information of the target vehicle, and track the movement state of the target vehicle through the multi-target tracking (MHT) algorithm. According to the preset traffic rules and strategies in the mine, combined with the traffic facilities such as the linked traffic signal control box, chamber LED lamp device, and gate device, perform necessary traffic management on multiple vehicles in the mine passage. S4. Establish a mine environment electromagnetic model. By accurately modeling the electromagnetic characteristics in the roadway, use Maxwell's equations to simulate the propagation and reflection characteristics of millimeter-wave signals under mine roadway conditions, and construct a radar simulation model. Use computer simulation software to simulate and test the performance of the millimeter-wave radar under different working conditions. The performance includes signal-to-noise ratio (SNR), range-velocity resolution, detection probability and false alarm rate. Use the genetic algorithm to find the optimal parameter set of the radar and adaptively optimize and adjust the parameters and update the model. S5. Use the convolutional neural network algorithm to deeply and intelligently analyze the feature data. Through the automatic learning technology of training, testing, verification, analysis and optimization, to identify the features of different targets, judge the attributes of the detected targets and whether they are potential hazard sources, and generate a risk controllable coefficient to improve the accuracy and robustness of the detection system. S6. Use WIFI, 4G or MESH technology for data communication and exchange among various modules in the detection system, convert the risk controllability coefficient into visual information and synchronize it to the driver in the trackless rubber-tired vehicle. The visual information includes the traffic flow status of the full-scene passage, and compare the decision-making threshold of early warning according to the judgment result of the visual information, and then trigger an alarm.

[0008] Preferably, the acquisition logic of the original radar echo data is as follows: A1. Install a road-end millimeter-wave radar integrated machine on the top of the underground roadway. By continuously transmitting pulse signals and recording the echo signals, obtain the radar echo signals in the detection roadway, and calibrate them as ; A2. Analyze and process the radar echo signals in the roadway by calculating the delay and frequency change of to determine the position and movement information of the trackless rubber-tired vehicle in the roadway, and calibrate them as and ; A3. The calculation steps for the position and movement information of the trackless rubber-tired vehicle in the roadway are as follows: Measure the frequency difference between the millimeter-wave radar transmission signal and the received signal in the roadway , calculate the distance, and calibrate it as , where represents the speed of light, that is , represents the bandwidth of the radar echo signal in the roadway; Use the Doppler effect to measure the speed of the detection target relative to the radar in the roadway according to the Doppler frequency shift , and calibrate it as , then , where represents the speed of light, that is , represents the carrier frequency, and , where represents the wavelength transmitted by the radar; Use the beamforming algorithm to measure the angle of the detection target relative to the phased array radar in the roadway, and calibrate it as , then , represents the wavelength transmitted by the radar, represents the th spacing between radar array elements, represents the th phase difference of the receiving array elements; A4. By measuring the distances calculated separately by a number of radar stations in the roadway, the speed and angle, the trilateration technology is used to accurately locate the vehicle position, and the vehicle's movement trajectory is deduced. The Kalman filtering algorithm is used to integrate multiple measurement results, filter out noise and more accurately predict the vehicle position, and provide a smooth and continuous movement trajectory of the vehicle in the roadway; A5. The vehicle-mounted millimeter-wave radar installed on the trackless rubber-tired vehicle continuously emits pulse signals to detect the surrounding environment and records the echo signals to obtain the vehicle radar echo signals, which are calibrated as ; A6. The vehicle radar echo signals are analyzed and processed. The vehicle's own movement speed information is obtained based on the radar echo delay and Doppler frequency change, which is calibrated as . In addition, a UWB positioning module is installed on the trackless rubber-tired vehicle, and the UWB positioning technology is combined to determine the position information of the trackless rubber-tired vehicle in the roadway, which is calibrated as ; A7. The calculation steps for the movement speed information of the trackless rubber-tired vehicle and its position information in the roadway are as follows: The movement speed information of the trackless rubber-tired vehicle includes the radar echo delay time and Doppler frequency change, which are respectively calibrated as and , and are respectively used to calculate the distance between the detection target in the roadway and the vehicle radar, which is calibrated as , and the speed of the detection target in the roadway relative to the vehicle radar, which is calibrated as . Then the distance calculation formula is . In the formula, represents the time delay between the radar transmitting pulse and the received echo, and the speed calculation formula is . In the formula, represents the speed of the trackless rubber-tired vehicle relative to the radar, represents the transmitting frequency of the radar; A8. The UWB positioning module provides high security, high-speed data transmission and high positioning accuracy technology, and absolute positions and calibrates the radar information by analyzing the time difference and track information of the vehicle radar echo signals . The Kalman filtering algorithm is used again to integrate multiple measurement results and reduce errors to provide the position information of the trackless rubber-tired vehicle under the actual movement state in the roadway.

[0009] Preferably, the acquisition logic of the preprocessed data is as follows: For noise filtering and data fusion in the preprocessing operation, the Kalman filtering technology is adopted in combination with the original radar echo data, calibrated as , and , , , for prediction and update; Define the original radar echo data as the state variable, that is , noise removal and data fusion adopt the Kalman filtering algorithm, and by predicting and updating the state variable, the preprocessed data is obtained, calibrated as , that is , where represents the position and motion information of the vehicle in the roadway, the moving speed information on the trackless rubber-tyred vehicle, and the position information in the roadway at the time step to generate the optimal state estimate value.

[0010] Preferably, the acquisition logic of the feature data is as follows: Perform adaptive filtering on the preprocessed data using the least mean square error algorithm to eliminate interference signals, and extract the feature information that can distinguish the target from the data after interference suppression for subsequent detection and tracking tasks. Among them, the calculation formula of the least mean square error algorithm for adaptive interference cancellation is , where represents the error signal of the preprocessed data at the current time step , represents the updated weight coefficient vector of the preprocessed data , represents the weight coefficient vector of the preprocessed data , represents the step size parameter, represents the complex conjugate of the input signal of the preprocessed data at the current time step ; Then, extract the feature data from the preprocessed data after interference suppression, calibrated as , then , where represents the set of feature data changing with time , represents the feature data at the moment The state value, the is represented as feature data including the distance, speed, and angular position features of the vehicle and the trackless rubber-tyred vehicle in the roadway

[0011] Preferably, the recognition and acquisition logic of the radar imaging data is as follows: The millimeter-wave radar integrated machine transmits and receives the original radar echo data reflected by the target , converting the time series of the echo into an electrical signal, converting it into a digital signal through an AD converter, and extracting feature data through a series of Kalman filtering processes to obtain the feature data ; Use the synthetic aperture radar (SAR) signal processing algorithm for range compression, R-D domain conversion, and azimuth compression calculations to improve the range resolution and azimuth resolution of the millimeter-wave radar. Among them, the formula for range compression is , where represents the compressed signal represents the characteristic signal received in the millimeter-wave radar echo signal represents the millimeter-wave radar transmission signal; The formula for R-D domain conversion is , where represents the Fourier transform represents the RD domain signal respectively represent the range frequency and Doppler frequency describing the target motion parameters; The formula for azimuth compression is , where represents the azimuth difference resolution represents the signal sampling rate represents the linear minimum interval of the target relative motion; Use the inverse synthetic aperture radar (ISAR) signal processing algorithm for Doppler center frequency estimation and motion compensation calculations to obtain the distance and speed information of the detected target and obtain a clearer target image. Among them, the formula for Doppler center frequency estimation is , where represents the frequency offset caused by the motion of the detected target in the Doppler spectrum of the processed radar echo signal represents the frequency of taking the maximum value represents the complex convolution function represents the time-domain signal received by the radar, that is, the time series of the echo signal at moment; The formula for motion compensation is , where represents the detected target at After moment motion compensation The signal of Is expressed as the imaginary part exponential function of a complex number Is expressed as the sampling time After SAR and ISAR imaging processing, radar imaging data is generated. Combining with the UWB base stations arranged in the mine, using the UWB tags equipped on the trackless rubber-tired vehicle to transmit UWB signals, the UWB base stations receive the signals from the UWB tags of the trackless rubber-tired vehicle, and according to the received signal strength and time difference, calculate the accurate position of the vehicle UWB tag relative to the base station through trilateration, that is, combining SAR, ISAR imaging data and UWB positioning data to construct a panoramic view of the detailed situation of the trackless rubber-tired vehicle inside the mine.

[0012] Preferably, the steps of the multi-target tracking MHT algorithm for tracking the motion state of the target vehicle are as follows From the feature data Measure the observed value of the initial motion state of the detected target vehicle at time step And transform the state of the observed value into matrix representation, marked as ; Use the motion model to perform state prediction on the initial motion state of each known detected target vehicle to predict the position and speed of the detected target vehicle at the next time step That is, the prediction formula is , where Represents the predicted state of the detected target vehicle at time , Represents the state estimation of the detected target vehicle at time , Represents the control input matrix of the detected target vehicle at time , Represents the control vector of the detected target vehicle at time , Represents the process noise of the detected target vehicle at time ; Use the Mahalanobis distance to calculate the data association degree. Then, for each newly received measurement value of the detected target vehicle, calculate the matching degree with the predicted motion state of the target vehicle. The calculation formula of the Mahalanobis distance is , where Represents the Mahalanobis distance between the observation And the target , Respectively represent the predicted value of the predicted target and the measured value of the observed target Represents the actual observation Represents the observation matrix Expressed as observations The covariance matrix associated with the target ; Data is updated according to the result of data association degree, and the target state estimation is updated by using the observation data for Kalman filtering calculation. The calculation formula of Kalman filtering is , where Represents the state estimation of the detected target vehicle after update at time ; Represents at time The Kalman gain; Represents the actual observation of the detected target vehicle at time ; Represents the detected target vehicle at time The observation matrix;

[0013] Preferably, the steps for establishing the electromagnetic model of the mine environment are as follows: Since the mine tunnel structure and the electromagnetic properties of the wall materials underground will affect the propagation process of radar signals, that is, affect the reflection and absorption of millimeter waves, the Maxwell equation is introduced to calculate the losses in the signal propagation process, including the free space loss and the additional loss of material attenuation, which are respectively calibrated as , ; Among them, the free space loss The calculation formula is , where Represents the distance, Represents the radar signal frequency; The additional loss of material attenuation The calculation formula is , where Represents the magnetic permeability of the material, Represents the conductivity of the material;

[0014] Preferably, the logical steps for parameter optimization and model update of the radar simulation model are as follows: The radar simulation model is established and simulated by using computer radar simulation software, simulating the transmitter, receiver and signal processing part of the radar, and accurately simulating according to multiple factors such as the size, shape and reflection characteristics of the detected target vehicle, the mine tunnel structure and the wall material properties. The genetic algorithm is used to optimize the parameter indexes that quantify the radar performance. The parameter indexes include signal-to-noise ratio, range resolution, velocity resolution, detection probability and false alarm rate, which are respectively calibrated as ; The genetic algorithm is a loop of selection, crossover, mutation, and fitness evaluation based on radar performance indicators and encoded parameters to find the optimal solution, and continuously adjusts the parameters in the simulation model according to the results of the genetic algorithm. The steps of the genetic algorithm optimization process are as follows: Generate an initial population composed of radar parameters, labeled as , and the initial population ; Calculate the fitness of the initial population , labeled as , and score according to the performance indicators of the predetermined signal-to-noise ratio, range resolution, velocity resolution, detection probability, and false alarm rate. The function formula for fitness evaluation is , where represents the fitness for evaluating the th radar performance parameter , represents the weight factors of the signal-to-noise ratio, range resolution, velocity resolution, detection probability, and false alarm rate, represents the signal-to-noise ratio parameter set, represents the range resolution parameter set, represents the velocity resolution parameter set, represents the false alarm rate parameter set, represents the nd individual in the radar performance parameter set; Select the parent individuals based on the fitness values and use single-point crossover calculation to generate the next generation, and randomly change the gene values with a relatively low probability of 0.1% - 1.0% in the offspring genes to obtain gene mutation individuals to form a new population to maintain population diversity and find a new solution space; After several gene genetic calculations, until a predetermined number of generations is reached or an individual with the best fitness appears, that is, extract the individual with the highest fitness as the optimal parameter solution for radar performance.

[0015] Preferably, the acquisition logic steps of the risk controllable coefficient are as follows: Establish a convolutional neural network algorithm, use the data obtained from the mine environment electromagnetic model and the radar simulation model as the validation set, and combine the feature data as the training data. During training, obtain the prediction result through forward propagation, and calculate the error between the actual output and the expected output through the loss function, and update the weights and biases through the backpropagation algorithm. After training is completed, evaluate the convolutional neural network model on the validation set, and predict and determine the probability of the detected target vehicle being dangerous, the probability of abnormal behavior, and the contribution value of environmental factors to the degree of danger, labeled as , , ; The risk controllability coefficient, calibrated as is the probability of directly outputting the risk by a neuron in the fully connected layer of the convolutional neural network model , the probability of abnormal behavior and the contribution value of environmental factors to the degree of risk to calculate the risk score, which is used to evaluate and control the risk degree of the detected target vehicle. Among them, the risk controllability coefficient has the calculation formula as , in the formula, respectively represent the probability of risk , the probability of abnormal behavior and the contribution value of environmental factors to the degree of risk of the relative weight factors, and , represents the deviation constant of the risk coefficient.

[0016] Preferably, it includes a millimeter-wave radar integrated machine, which is responsible for transmitting millimeter-wave signals and receiving radar signals reflected from the vehicle to provide original radar echo data for vehicle detection; The data acquisition and preprocessing module directly receives the original radar echo data provided by the millimeter-wave radar integrated machine, and performs preliminary noise filtering and data fusion preprocessing, and performs feature extraction and interference suppression on the preprocessed data again; The environment modeling and radar simulation module creates a model of the trackless rubber-tired vehicle and the underground mine roadway environment according to the preprocessed data in the data acquisition and preprocessing module, and simulates the performance of the millimeter-wave radar under different conditions by setting environmental characteristic data; The vehicle detection and tracking module receives the radar data passed through the environment modeling and radar simulation module, and detects the vehicle in real time from the radar data, and performs real-time tracking on the detected vehicle; The signal processing AI analysis and optimization module uses the deep learning algorithm in machine learning technology to intelligently analyze and optimize the radar signals and vehicle trajectory data of the vehicle detection and tracking module, judge the risk status of the detection target, and output the analysis results to the visualization warning module; The visualization warning module receives the target risk analysis results of the signal processing AI analysis and optimization module, and displays them to the user in an intuitive manner, providing a real-time alarm function.

[0017] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention establishes a mine environment electromagnetic model and a radar simulation model, and according to the actual structure and material of the mine tunnel, further combines the absorption of radar signals, and accurately optimizes and adjusts the performance parameter signals of the millimeter wave radar integrated machine deployed in the mine and on the trackless rubber-wheeled vehicle, thereby avoiding the interference of reflection, diffraction and occlusion in the complex mining environment, ensuring the accuracy and precision of the feature data extracted from the original radar echo data, and streamlining the number of detection targets, so as to improve the accurate recognition efficiency and accurate positioning effect of target vehicle detection; The present invention adopts a convolutional neural network model to deeply analyze and optimize the detection data to generate a controllable coefficient of danger, compares the warning decision threshold to perform visual warning processing, and further accurately predicts and analyzes the danger source of the detection target vehicle, thereby ensuring the safety of driving underground in the mine. In addition, the traffic signal control box in the lane is linked according to the warning strategy to implement necessary traffic management to improve the operating efficiency of the trackless rubber-tyred vehicle, and UWB technology is used to locate the position of the vehicle in the lane, thereby ensuring high-speed, reliable and low-latency transmission of communication, while improving the positioning effect of the detection target vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0019] Figure 1 The figure is a flow chart of the vehicle detection method of the millimeter wave radar of the present invention.

[0020] Figure 2 The figure is a schematic diagram of the structure of the vehicle detection system of the millimeter wave radar of the present invention. DETAILED DESCRIPTION

[0021] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art. Example 1

[0022] like Figure 1 As shown, the present invention provides a vehicle detection method using a millimeter wave radar, comprising the following steps: S1. Install road-end and vehicle-end millimeter-wave radar integrated machines on the top of the underground roadway and the trackless rubber-tired vehicle respectively. The millimeter-wave radar integrated machine emits millimeter waves and receives echoes to obtain the original radar echo data, which includes the position and movement information of the trackless rubber-tired vehicle in the detected roadway and the self-movement speed information of the trackless rubber-tired vehicle. It should be noted that the acquisition logic of the original radar echo data is as follows: A1. Install a road-end millimeter-wave radar integrated machine on the top of the underground roadway. By continuously emitting pulse signals and recording the echo signals, the radar echo signals in the detected roadway are obtained and calibrated as ; A2. Analyze and process the radar echo signals in the roadway by calculating the delay and frequency change of to determine the position and movement information of the trackless rubber-tired vehicle in the roadway, which are respectively calibrated as and A3. The calculation steps for the position and movement information of the trackless rubber-tired vehicle in the roadway are as follows: By measuring the frequency difference between the millimeter-wave radar transmitted signal and the received signal in the roadway , calculate the distance, which is calibrated as , then , where represents the speed of light, that is , represents the bandwidth of the radar echo signal in the roadway; Use the Doppler effect. According to the Doppler frequency shift measure the speed of the detected target relative to the radar in the roadway, which is calibrated as , then, , where represents the speed of light, that is , represents the carrier frequency, and , where represents the wavelength transmitted by the radar; Use the beamforming algorithm to measure the angle of the detected target relative to the phased array radar in the roadway, which is calibrated as , then , represents the wavelength transmitted by the radar, represents the spacing between the th and th radar array elements, A4. By measuring in the roadway The distances calculated separately by a radar station for speed and angle, use triangulation technology to accurately locate the vehicle position, deduce the vehicle's movement trajectory, and use the Kalman filter algorithm to integrate multiple measurement results, filter out noise and more accurately predict the vehicle position, and provide a smooth and continuous movement trajectory of the vehicle in the roadway; A5. The vehicle-mounted millimeter-wave radar installed on the trackless rubber-tyred vehicle continuously emits pulse signals to detect the surrounding environment and records the echo signals to obtain the vehicle radar echo signals, calibrated as ; A6. Analyze and process the vehicle radar echo signals to obtain the vehicle's own movement speed information based on the radar echo delay and Doppler frequency change, calibrated as , and install a UWB positioning module on the trackless rubber-tyred vehicle, and combine the UWB positioning technology to determine the position information of the trackless rubber-tyred vehicle in the roadway, calibrated as ; A7. The calculation steps for the movement speed information of the trackless rubber-tyred vehicle and the position information in the roadway are as follows: The movement speed information of the trackless rubber-tyred vehicle includes the radar echo delay time and Doppler frequency change, respectively calibrated as and , and are respectively used to calculate the distance between the detection target in the roadway and the vehicle radar, calibrated as , and calculate the speed of the detection target in the roadway relative to the vehicle radar, calibrated as . Then the distance calculation formula is . In the formula, represents the time delay between the radar transmitting pulse and receiving the echo, and the speed calculation formula is . In the formula, represents the speed of the trackless rubber-tyred vehicle relative to the radar, represents the transmitting frequency of the radar; A8. The UWB positioning module provides high security, high-speed data transmission and high positioning accuracy technology, and absolutely locates and calibrates the radar information by analyzing the time difference and track information of the vehicle radar echo signals . Once again, use the Kalman filter algorithm to integrate multiple measurement results, reduce errors, and provide the position information of the trackless rubber-tyred vehicle under the actual movement state in the roadway.

[0023] S2. Apply Kalman filtering technology to perform preprocessing operations of noise removal and data fusion on the original radar echo data of vehicle detection, generate preprocessed data, and perform feature extraction and interference suppression on the preprocessed data to generate feature data for subsequent processing; It should be noted that the acquisition logic of the preprocessed data is as follows: The preprocessing operations include noise filtering, data fusion, feature extraction, and interference suppression. For noise filtering and data fusion, Kalman filtering technology is used in combination with the original radar echo data, calibrated as and , , , to perform prediction and update to evaluate the position and speed of the vehicle; for feature extraction, the Fourier transform algorithm is used to extract frequency domain features from the original radar echo data ; as for interference suppression, the spatial filtering algorithm is used to eliminate the signals of non-target vehicles to suppress the interference generated by the environment; Define the original radar echo data as the state variable, that is , noise removal and data fusion use the Kalman filtering algorithm. By performing prediction and update processing on the state variable, the preprocessed data is obtained, calibrated as , that is , where represents the position and motion information of the vehicle in the roadway, the motion speed information on the trackless rubber-tyred vehicle, and the position information in the roadway at the time step to generate the best state estimate value.

[0024] It should be noted that the acquisition logic of the feature data is as follows: Perform adaptive filtering on the preprocessed data using the least mean square error algorithm to eliminate interference signals, and extract distinguishable target feature information from the data after interference suppression for subsequent detection and tracking tasks. Among them, the calculation formula of the least mean square error algorithm used for adaptive interference cancellation is , where represents the error signal of the preprocessed data at the current time step , represents the updated weight coefficient vector of the preprocessed data , represents the weight coefficient vector of the preprocessed data , represents the step size parameter, represents the preprocessed data At the current time step the complex conjugate of the input signal; Then, according to the preprocessed data after interference suppression extract feature data and calibrate it as then wherein, is expressed as the set of feature data changing with time variation, is expressed as the feature data at the moment the state value at which it is located, and the is expressed as the feature data the position features of the distance, speed, and angle of the vehicles and trackless rubber-tired vehicles in the roadway included therein.

[0025] S3. Use the signal processing algorithms of synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) for radar imaging recognition to obtain radar imaging data, and combine the UWB technology to locate the position information of the target vehicle, and track the motion state of the target vehicle through the multi-target tracking (MHT) algorithm. According to the preset traffic rules and strategies in the mine, combined with the traffic facilities of the linkage traffic signal control box, chamber LED lamp device, and gate device, conduct necessary traffic management on multiple vehicles in the mine passage; It should be noted that the recognition and acquisition logic of the radar imaging data is as follows: Transmit and receive the original radar echo data reflected by the target object by the millimeter-wave radar integrated machine , convert the time series of the echo into an electrical signal, convert it into a digital signal through an AD converter, and extract feature data through a series of processes such as Kalman filtering to obtain the feature data ; Use the synthetic aperture radar (SAR) signal processing algorithm for range compression, R-D domain conversion, and azimuth compression calculation to improve the range resolution and azimuth resolution of the millimeter-wave radar. Among them, the calculation formula for range compression is wherein, is expressed as the compressed signal, is expressed as the feature signal received in the millimeter-wave radar echo signal, is expressed as the millimeter-wave radar transmitted signal; The calculation formula for R-D domain conversion is wherein, is expressed as the Fourier transform, is expressed as the RD domain signal, respectively represent the range frequency and Doppler frequency describing the target motion parameters; The calculation formula for azimuth compression is wherein, Expressed as the azimuth resolution, Expressed as the signal sampling rate, Expressed as the linear minimum interval of the relative motion of the target.

[0026] The inverse synthetic aperture radar (ISAR) signal processing algorithm is used for Doppler center frequency estimation and motion compensation calculation to obtain the distance and speed information of the detected target and obtain a clearer target image. Among them, the calculation formula for Doppler center frequency estimation is , where Expressed as the frequency shift caused by the motion of the detected target in the Doppler spectrum of the processed radar echo signal, Expressed as the frequency taking the maximum value, Expressed as the complex convolution function, Expressed as the time-domain signal received by the radar, that is, the time series of the echo signal at time; The calculation formula for motion compensation is , where Expressed as the signal after motion compensation of the detected target at time, Expressed as the imaginary part exponential function of the complex number, Expressed as the sampling time; After the imaging processing of SAR and ISAR, radar imaging data is generated. Combining with the UWB base stations arranged in the mine, using the UWB tags equipped on the trackless rubber-tired vehicle to transmit UWB signals, the UWB base stations receive the signals from the UWB tags of the trackless rubber-tired vehicle, and according to the received signal strength and time difference, calculate the precise position of the vehicle UWB tag relative to the base station through trilateration, that is, combining SAR, ISAR imaging data and UWB positioning data to construct a panoramic view of the detailed situation of the trackless rubber-tired vehicle inside the mine.

[0027] It should be noted that the steps of the multi-target tracking (MHT) algorithm to track the motion state of the target vehicle are as follows: from the feature data Measure the observed value of the initial motion state of the detected target vehicle at the time step , and transfer the state of the observed value into matrix representation, marked as ; Use the motion model to perform state prediction on the initial motion state of each known detected target vehicle to predict the position and speed of the detected target vehicle at the next time step , that is, the prediction formula is , where Expressed as the predicted state of the detected target vehicle at time , ​Denoted as the state estimation of the target vehicle at time ; Denoted as the control input matrix of the target vehicle at time ; Denoted as the control vector of the target vehicle at time ; Denoted as the process noise of the target vehicle at time ; Using the Mahalanobis distance to calculate the data association degree, for each newly received measurement value of the target vehicle, calculate the matching degree with the predicted motion state of the target vehicle. The calculation formula of the Mahalanobis distance is , where Denoted as the Mahalanobis distance between the observation and the target ; Denote the predicted value of the predicted target and the measured value of the observed target respectively, Denoted as the actual observation, Denoted as the observation matrix, Denoted as the covariance matrix associated between the observation and the target ; According to the result of the data association degree, perform data update, and use the observed data to perform Kalman filter calculation to update the target state estimation. The calculation formula of the Kalman filter is , where Denoted as the state estimation of the target vehicle at time after update, Denoted as the Kalman gain at time , Denoted as the actual observation of the target vehicle at time , Denoted as the observation matrix of the target vehicle at time .

[0028] S4. Establish an electromagnetic model of the mine environment. By accurately modeling the electromagnetic characteristics in the roadway, use Maxwell's equations to simulate the propagation and reflection characteristics of millimeter-wave signals under mine roadway conditions, and construct a radar simulation model. Use computer simulation software to simulate and test the performance of the millimeter-wave radar under different working conditions. The performance includes signal-to-noise ratio SNR, range-velocity resolution, detection probability, and false alarm rate. Use the genetic algorithm to find the optimal parameter set of the radar and adaptively optimize and adjust the parameters to update the model; It should be noted that the steps for establishing the electromagnetic model of the mine environment are as follows: Since the mine roadway structure and the electromagnetic properties of the wall materials underground will affect the propagation process of radar signals, that is, affect the reflection and absorption of millimeter waves, the Maxwell equations are introduced to calculate the losses in the signal propagation process, including free space loss and additional loss of material attenuation, which are respectively labeled as , ; Among them, the free space loss The calculation formula is , where represents the distance, represents the radar signal frequency; The additional loss of material attenuation The calculation formula is , where represents the magnetic permeability of the material, represents the conductivity of the material.

[0029] It should be noted that the logical steps for parameter optimization and model update of the radar simulation model are as follows: The radar simulation model is built and simulated using computer radar simulation software, which simulates the transmitter, receiver, and signal processing parts of the radar, and accurately simulates according to multiple factors such as the size, shape, and reflection characteristics of the detected target vehicle, the mine roadway structure, and the wall material properties. The genetic algorithm is used to optimize the parameter indicators that quantify the radar performance. The parameter indicators include signal-to-noise ratio, range resolution, velocity resolution, detection probability, and false alarm rate, which are respectively labeled as ; The genetic algorithm is a loop of encoding parameters, performing selection, crossover, mutation, and fitness evaluation according to the radar performance indicators to find the optimal solution, and continuously adjusting the parameters in the simulation model according to the results of the genetic algorithm to maximize the detection radar performance and minimize the error. Among them, the steps of genetic algorithm optimization are as follows: Generate an initial population composed of radar parameters, labeled as , and the initial population ; Calculate the fitness of the initial population , labeled as , and score according to the performance indicators of the predetermined signal-to-noise ratio, range resolution, velocity resolution, detection probability, and false alarm rate. Then the function formula for fitness evaluation is , where represents the fitness for evaluating the th radar performance parameter , represents the weight factor of signal-to-noise ratio, range resolution, velocity resolution, detection probability, and false alarm rate, represents the signal-to-noise ratio parameter set, Expressed as a set of range resolution parameters, Expressed as a set of velocity resolution parameters, Expressed as a set of false alarm rate parameters, Expressed as the th individual in the radar performance parameter set; Select the parent individuals based on the fitness value and use single-point crossover calculation to generate the next generation, and randomly change the gene values with a relatively low probability of 0.1% - 1.0% in the offspring genes to obtain mutant gene individuals to form a new population , which is used to maintain the population diversity and find a new solution space. Among them, single-point crossover is that the genes of the next generation are exchanged from the gene sequences of the parent individuals to generate new individuals with different characteristics, and gene mutation means that each gene in the gene sequence of each individual has a mutation probability of 0.1% - 1.0%. When any gene is selected to mutate, the gene sequence value of the individual itself will change; After several gene inheritance calculations, until a predetermined number of generations are reached or individuals with the best fitness appear, that is, extract the individual with the highest fitness as the optimal parameter solution of the radar performance.

[0030] S5. Use the convolutional neural network algorithm to conduct in-depth intelligent analysis on the feature data, an automatic learning technology that has been trained, tested, verified, analyzed and optimized to identify the features of different targets, judge the attributes of the detected targets and whether they are potential hazard sources, and generate a risk controllable coefficient to improve the accuracy and robustness of the detection system; It should be noted that the logical steps for obtaining the risk controllable coefficient are as follows: Establish a convolutional neural network algorithm, use the data obtained from the mine environment electromagnetic model and the radar simulation model as the validation set, and combine the feature data as the training data. During training, the prediction result is obtained through forward propagation, and the loss function calculates the error between the actual output and the expected output, and updates the weights and biases through the backpropagation algorithm. After training, the convolutional neural network model is evaluated on the validation set, and the probabilities of the detected target vehicle being dangerous, having abnormal behavior, and the contribution value of environmental factors to the degree of danger are predicted and determined, and are respectively calibrated as , , ; The risk controllable coefficient, calibrated as, is the probability of directly outputting the degree of danger by a neuron in the fully connected layer of the convolutional neural network model , the probability of abnormal behavior and the contribution value of environmental factors to the degree of danger to calculate the risk score, which is used to evaluate and control the degree of danger of the detected target vehicle. Among them, the risk controllable coefficient The calculation formula is , where respectively represent the probability of danger , the probability of abnormal behavior and the contribution value of environmental factors to the degree of danger is the relative weight factor of , and represents the deviation constant of the danger coefficient.

[0031] S6. For each module in the detection system, use WIFI, 4G or MESH technology for data communication and exchange, convert the danger controllable coefficient into visual information and synchronize it to the driver in the trackless rubber-tyred vehicle. The visual information includes the traffic flow status of the full-scene passage, and compare the judgment result of the visual information with the early warning decision threshold, and then trigger an alarm.

[0032] It should be noted that the setting logic of the early warning decision threshold is as follows: According to the accident records of trackless rubber-tyred vehicles that occurred in the mine in history, traffic flow data, and the environment and structure of the nodes where mine roadway accidents occur frequently, simulate different scenarios to test different danger controllable coefficients The effect of triggering an alarm by the value to adjust the early warning threshold to ensure that dangers can be effectively prevented and sufficient response time can be provided in the actual situation of the mine roadway; Set the early warning threshold to , and , where represents the minimum value of the danger controllable range, represents the maximum value of the danger controllable range. When the early warning threshold , continuously trigger an alarm in real time, synchronize the visual information of the vehicle position in the roadway and the traffic flow status of the full-scene passage, and link the traffic signal control box in the roadway. According to the preset traffic rules and strategies in the mine, implement necessary traffic management for multiple vehicles in the mine roadway. When the early warning threshold , trigger an alarm at intervals, synchronize the visual information of the vehicle position in the roadway and the traffic flow status of the full-scene passage, so that the driver of the trackless rubber-tyred vehicle actively controls the traffic operation status according to the traffic rules of the mine roadway; At the same time, combined with the alarm frequency, alarm sensitivity and false alarm rate, use WIFI, 4G or MESH technology to transmit data, and feedback the driver experience data to the convolutional neural network model for optimization training, and accurately generate the danger controllable coefficient , and further perform early warning processing, and quickly respond when detecting potential target vehicle risks in a timely manner, ensuring the driving safety in the mine while ensuring high-speed, reliable and low-latency transmission of communication.

[0033] Embodiment 2 As Figure 2As shown in the figure, the present invention provides a vehicle detection system for millimeter-wave radar, including an integrated millimeter-wave radar, which is the core component for vehicle detection in a mining machine and used for trackless rubber-tired vehicles. It is responsible for transmitting millimeter-wave signals and receiving radar signals reflected from vehicles to provide original radar echo data for vehicle detection. The data acquisition and preprocessing module directly receives the original radar echo data provided by the integrated millimeter-wave radar, and performs preliminary noise filtering and data fusion preprocessing, as well as feature extraction and interference suppression on the preprocessed data again, to improve the quality of radar signal data and provide a cleaner signal for the subsequent vehicle detection and tracking module and signal processing AI analysis and optimization module processing stages. The environment modeling and radar simulation module creates a model of the trackless rubber-tired vehicle and the underground roadway environment where it is located according to the preprocessed data in the data acquisition and preprocessing module, and simulates the performance of the millimeter-wave radar under different conditions by setting environmental characteristic data, to optimize the parameter settings in actual applications and improve the accuracy of vehicle detection. The vehicle detection and tracking module receives the radar data that has passed through the environment modeling and radar simulation module, and detects vehicles in the radar data in real time and tracks the detected vehicles in real time. The signal processing AI analysis and optimization module uses deep learning algorithms in machine learning technology to intelligently analyze and optimize the radar signals and vehicle trajectory data of the vehicle detection and tracking module, judge the dangerous state of the detection target, and output the analysis results to the visualization warning module to improve the accuracy and robustness of vehicle detection and tracking. The visualization warning module receives the target danger analysis results of the signal processing AI analysis and optimization module, and displays them to the user in an intuitive way, providing a real-time alarm function to assist vehicle driving and improve the monitoring task.

[0034] A vehicle detection method for millimeter-wave radar provided by an embodiment of the present invention is implemented by the above vehicle detection system for millimeter-wave radar. The specific methods and processes of a vehicle detection method and system for millimeter-wave radar are detailed in the embodiments of the above vehicle detection method and system for millimeter-wave radar, and will not be elaborated here.

[0035] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by software simulation of a large amount of collected data to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0036] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more sets of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0037] It should be understood that in various embodiments of the present application, the sequence numbers of the above processes do not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0038] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0039] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A vehicle detection method using millimeter wave radar, characterized in that: The steps include: S1. Install a road-side integrated millimeter-wave radar on the top of the underground tunnel and on the trackless rubber-tyred vehicle, respectively. The millimeter-wave radar integrated device transmits millimeter waves and receives echoes to obtain original radar echo data. The original radar echo data includes the position and movement information of the trackless rubber-tyred vehicle in the tunnel and the movement speed information of the trackless rubber-tyred vehicle itself. S2. Apply Kalman filtering technology to perform preprocessing operations of noise removal and data fusion on the original radar echo data of vehicle detection to generate preprocessed data, and perform feature extraction and interference suppression on the preprocessed data to generate feature data for subsequent processing; S3. Use the signal processing algorithms of synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) to perform radar imaging recognition, obtain radar imaging data, and locate the target vehicle position information in combination with UWB technology, and track the movement status of the target vehicle through the multi-target tracking (MHT) algorithm. According to the preset traffic rules and strategies in the mine, combined with the traffic facilities of the linkage traffic signal control box, chamber LED light device, and gate device, necessary traffic management is carried out for multiple vehicles in the mine passage; S4. Establish an electromagnetic model of the mine environment. By accurately modeling the electromagnetic characteristics in the tunnel, use Maxwell's equations to simulate the propagation and reflection characteristics of millimeter-wave signals under mine tunnel conditions, and build a radar simulation model. Use computer simulation software to simulate and test the performance of millimeter-wave radar under different working conditions, including signal-to-noise ratio (SNR), distance and velocity resolution, detection probability and false alarm rate. Use genetic algorithms to find the best radar parameter set and adaptively optimize and adjust the parameters and update the model. S5. Use convolutional neural network algorithm to conduct in-depth intelligent analysis of feature data, and use automatic learning technology that has been trained, tested, verified, analyzed and optimized to identify the characteristics of different targets, determine the attributes of the detected targets and whether they are potential sources of danger, and generate risk controllability coefficients to improve the accuracy and robustness of the detection system; S6. Use WIFI, 4G or MESH technology to exchange data among various modules in the detection system, and convert the controllable coefficient of danger into visual information to synchronize with the driver in the rubber-tyred trackless vehicle. The visual information includes the traffic flow status of the entire channel, and compares the warning decision threshold with the judgment result of the visual information, thereby triggering an alarm.

2. The vehicle detection method of millimeter wave radar according to claim 1, characterized in that: The acquisition logic of the original radar echo data is as follows: A1. Install a road-end millimeter-wave radar integrated device at the top of the underground tunnel, continuously transmit pulse signals and record echo signals to obtain radar echo signals in the detection tunnel, calibrated as ; A2. Radar echo signal in the tunnel Analyze and process The delay and frequency change of the trackless rubber-tyred vehicle in the lane are determined to determine the position and motion information of the trackless rubber-tyred vehicle in the lane, which are calibrated as and ; A3. Position of rubber-tyred trackless vehicles in the lane and sports information The calculation steps are as follows: By measuring the frequency difference between the transmitted and received signals of the millimeter-wave radar in the lane , calculate the distance and calibrate it as ,but , where Expressed as the speed of light, , Represented as radar echo signal in the tunnel bandwidth; Using the Doppler effect, according to the Doppler frequency shift Measure the speed of the detected target relative to the radar in the lane and calibrate it as ,but , where Expressed as the speed of light, , is the carrier frequency, and , where Expressed as the wavelength of radar transmission; The beamforming algorithm is used to measure the angle of the detected target relative to the phased array radar in the tunnel and calibrate it as ,but , Expressed as the wavelength of radar transmission, Expressed as The spacing between radar array elements is Expressed as The phase difference of the receiving array elements; A4. By measuring the inside of the tunnel The distance calculated for each radar station ,speed and angle , using triangulation technology to accurately locate the vehicle position and infer the vehicle's trajectory, and using the Kalman filter algorithm to integrate multiple measurement results, filter out noise and more accurately predict the vehicle's position, and provide a smooth and continuous trajectory of the vehicle in the lane; A5. The vehicle-side millimeter-wave radar installed on the rubber-tyred trackless vehicle detects the surrounding environment by continuously emitting pulse signals and recording echo signals to obtain the vehicle radar echo signal, which is calibrated as ; A6. Vehicle radar echo signal Analyze and process the vehicle's own speed information based on the radar echo delay and Doppler frequency change, and calibrate it as , and installed a UWB positioning module on the trackless rubber-tyred vehicle, and combined with UWB positioning technology to determine the position information of the trackless rubber-tyred vehicle in the lane and calibrate it ; A7. Speed ​​information on rubber-tyred trackless vehicles and location information in the lane The calculation steps are as follows: Movement speed information on rubber-tyred trackless vehicles Including radar echo delay time and Doppler frequency change, calibrated as and , and are used to calculate the distance between the detected target and the vehicle radar in the lane and calibrate , and calculate the speed of the detected target in the lane relative to the vehicle radar, calibrated as , then the distance calculation formula is , where It is expressed as the time delay between the radar transmitting pulse and the received echo, and the speed calculation formula is , where It is expressed as the speed of the rubber-tyred trackless vehicle relative to the radar, Expressed as the transmitting frequency of the radar; A8, UWB positioning module provides high security, high-speed data transmission and high positioning accuracy technology, and analyzes the vehicle radar echo signal The time difference and track information are used to absolutely locate and calibrate the radar information, and the Kalman filter algorithm is used again to integrate multiple measurement results and reduce errors to provide the position information of the trackless rubber-tyred vehicle in the actual motion state in the lane. .

3. The vehicle detection method of millimeter wave radar according to claim 2, characterized in that: The acquisition logic of the preprocessed data is as follows: The preprocessing operation uses Kalman filtering technology to combine the original radar echo data and calibrate it for noise filtering and data fusion. ,and , , , make predictions and updates; Defining raw radar echo data is the state variable, that is ,Noise removal and data fusion are carried out by using Kalman filtering algorithm, which obtains preprocessed data and calibrates them by predicting and updating state variables. ,Right now , where Represented as the position of the vehicle in the lane and sports information , Movement speed information on rubber-tyred trackless vehicles and location information in the lane At time step The best state estimate produced.

4. The vehicle detection method of millimeter wave radar according to claim 3, characterized in that: The acquisition logic of the feature data is as follows: Preprocessing data The minimum mean square error algorithm is used for adaptive filtering to eliminate interference signals, and feature information that can distinguish targets is extracted from the interference suppressed data for subsequent detection and tracking tasks. The calculation formula of the minimum mean square error algorithm used in adaptive interference elimination is: , where Represented as preprocessed data At the current time step The error signal, Represented as preprocessed data The updated weight coefficient vector, Represented as preprocessed data The weight coefficient vector of is represented as the step size parameter, Represented as preprocessed data At the current time step The complex conjugate of the input signal; According to the preprocessed data after interference suppression Extract feature data and mark it as ,but , where Represented as feature data over time A collection of changes, Represented as feature data At the moment The state value, Represented as feature data The position characteristics of distance, speed and angle of vehicles and rubber-tyred trackless vehicles in the lane are included.

5. The vehicle detection method of millimeter wave radar according to claim 4, characterized in that: The recognition and acquisition logic of the radar imaging data is as follows: The millimeter wave radar integrated device transmits and receives the original radar echo data reflected by the target object , so that the time series of echoes is converted into electrical signals, and then converted into digital signals through AD converters, and feature data is extracted after a series of processing through Kalman filtering to obtain feature data ; The synthetic aperture radar SAR signal processing algorithm is used to perform range compression, RD domain conversion and azimuth compression calculations to improve the range resolution and azimuth resolution of the millimeter wave radar. The calculation formula for range compression is: , where Represented as the compressed signal, It is represented as the characteristic signal received in the millimeter-wave radar echo signal. Represents the millimeter wave radar transmission signal; The calculation formula for RD domain conversion is: , where Expressed as Fourier transform, Represented as an RD domain signal, They are respectively represented as the range frequency and Doppler frequency describing the target motion parameters; The calculation formula for azimuth compression is: , where Expressed as the azimuth difference resolution, Expressed as the signal sampling rate, It is expressed as the linear minimum interval of the target's relative motion; The inverse synthetic aperture radar (ISAR) signal processing algorithm is used to perform Doppler center frequency estimation and motion compensation calculation to obtain the distance and speed information of the detected target and obtain a clearer target image. The calculation formula for Doppler center frequency estimation is: , where It is expressed as the frequency shift caused by detecting target motion in the Doppler spectrum of the radar echo signal. It is expressed as the frequency of taking the maximum value, is represented as a complex convolution function, It is represented as the time domain signal received by the radar, that is, the echo signal is Time series of moments; The calculation formula for motion compensation is: , where Represents the detection target in After motion compensation signal, Expressed as the exponential function of the imaginary part of a complex number, It is represented as sampling time; Radar imaging data is generated after SAR and ISAR imaging processing. Combined with the UWB base station arranged in the mine, the UWB tag equipped with the trackless rubber-tyred vehicle is used to transmit the UWB signal. The UWB base station receives the signal from the UWB tag of the trackless rubber-tyred vehicle and calculates the precise position of the vehicle's UWB tag relative to the base station through trilateral measurement based on the received signal strength and time difference. That is, the SAR, ISAR imaging data and UWB positioning data are combined to construct a panoramic picture of the detailed situation of the trackless rubber-tyred vehicle inside the mine.

6. The vehicle detection method of millimeter wave radar according to claim 5, characterized in that: The steps of the multi-target tracking MHT algorithm to track the motion state of the target vehicle are as follows: From the feature data The target vehicle is detected at the time step The observation value of the initial motion state is converted into a matrix representation, marked as ; Use the motion model to predict the initial motion state of each known detection target vehicle to predict the detection target vehicle at the next time step. The position and speed of , where Represents the detection of the target vehicle at time The predicted state of Represents the detection of the target vehicle at time The state estimate of Represents the detection of the target vehicle at time The control input matrix, Represents the detection of the target vehicle at time The control vector of Represents the detection of the target vehicle at time process noise; The Mahalanobis distance is used to calculate the data association degree. For each newly received measurement value of the detected target vehicle, the matching degree with the predicted target vehicle motion state is calculated. The calculation formula of the Mahalanobis distance is: , where Represented as observation With the goal The Mahalanobis distance between They are respectively represented as the predicted value of the predicted target and the measured value of the observed target, Represents actual observations, It is represented as the observation matrix, Represented as observation With the goal The covariance matrix of the associations between ; The data is updated according to the result of data association, and the target state estimation is updated by Kalman filter calculation using the observed data. The calculation formula of Kalman filter is: , where Represents the updated detection target vehicle at time The state estimate of Expressed as at time The Kalman gain, Represents the detection of the target vehicle at time The actual observation of Represents the detection of the target vehicle at time The observation matrix.

7. The vehicle detection method of millimeter wave radar according to claim 6, characterized in that: The steps for establishing the mine environment electromagnetic model are as follows: Since the electromagnetic properties of the tunnel structure and wall materials in the mine will affect the propagation process of the radar signal, that is, affect the reflection and absorption of the millimeter wave, the Maxwell equations are introduced to calculate the loss in the signal propagation process, including free space loss and additional loss due to material attenuation, which are calibrated as , ; Among them, free space loss The calculation formula is , where Expressed as distance, Expressed as radar signal frequency; Additional loss due to material attenuation The calculation formula is , where Expressed as the magnetic permeability of the material, Expressed as the electrical conductivity of the material.

8. The vehicle detection method using millimeter wave radar according to claim 7, characterized in that: The logical steps of parameter optimization and model updating of the radar simulation model are as follows: The radar simulation model is modeled and simulated using computer radar simulation software to simulate the transmitter, receiver and signal processing parts of the radar. It is accurately simulated based on the size, shape and reflection characteristics of the target vehicle, the tunnel structure and the wall material properties. The genetic algorithm is used to optimize the parameter indicators of the quantitative radar performance. The parameter indicators include signal-to-noise ratio, distance resolution, velocity resolution, detection probability and false alarm rate, which are calibrated as ; The genetic algorithm is based on radar performance indicators, encoding parameters, and a cycle of selection, crossover, mutation, and fitness evaluation to find the optimal solution, and continuously adjust the parameters in the simulation model according to the results of the genetic algorithm. The steps of the genetic algorithm optimization process are as follows: Generate an initial population consisting of radar parameters, calibrated as , and the initial population ; Calculate the initial population The fitness of , and score according to the predetermined performance indicators of signal-to-noise ratio, distance resolution, velocity resolution, detection probability and false alarm rate, then the function formula for fitness evaluation is: , where Represents the evaluation Radar performance parameters The fitness of Expressed as weighting factors for signal-to-noise ratio, range resolution, velocity resolution, detection probability, and false alarm rate, Expressed as a signal-to-noise ratio parameter set, Expressed as a range resolution parameter set, Expressed as a velocity resolution parameter set, Expressed as a false alarm rate parameter set, Represented as the radar performance parameter set individual; The parent individuals are selected based on the fitness value and the next generation is generated using single-point crossover calculation. The gene values ​​in the offspring genes are randomly changed with a relatively low probability of 0.1%-1.0%, and the gene mutation individuals are obtained to form a new population. , used to maintain population diversity and find new solution spaces; After several genetic calculations, until a predetermined number of generations or an individual with the best fitness appears, the individual with the highest fitness is extracted as the optimal parameter solution for radar performance.

9. The vehicle detection method using millimeter wave radar according to claim 8, characterized in that: The logical steps for obtaining the risk controllable coefficient are as follows: A convolutional neural network algorithm was established, and the data obtained from the mine environment electromagnetic model and radar simulation model were used as the validation set, combined with the feature data As training data, the prediction results are obtained through forward propagation during training, and the loss function calculates the error between the actual output and the expected output, and the weights and biases are updated through the back propagation algorithm. After the training is completed, the convolutional neural network model is evaluated on the validation set to predict the probability of detecting the target vehicle being dangerous, the probability of abnormal behavior, and the contribution of environmental factors to the degree of danger, which are calibrated as , , ; Danger controllable coefficient, calibrated as It is the probability of directly outputting danger based on a neuron in the fully connected layer of the convolutional neural network model. , the probability of abnormal behavior and environmental factors' contribution to the risk level The risk score is calculated to evaluate and control the danger level of the detected target vehicle, among which the controllable risk coefficient The calculation formula is , where The probability of dangerousness , the probability of abnormal behavior and environmental factors' contribution to the risk level The relative weight factor of , Expressed as a deviation constant for the hazard factor.

10. A vehicle detection system using a millimeter wave radar, according to a vehicle detection method using a millimeter wave radar according to any one of claims 1 to 9, characterized in that: It includes a millimeter wave radar integrated device, which is responsible for transmitting millimeter wave signals and receiving radar signals reflected from vehicles to provide raw radar echo data for vehicle detection; The data acquisition preprocessing module directly receives the original radar echo data provided by the millimeter-wave radar integrated device, performs preliminary noise filtering and data fusion preprocessing, and performs feature extraction and interference suppression on the preprocessed data again; The environment modeling and radar simulation module creates a model of the trackless rubber-tyred vehicle and the underground passage environment in the mine based on the preprocessed data in the data acquisition preprocessing module, and simulates the performance of the millimeter-wave radar under different conditions by setting environmental characteristic data; The vehicle detection and tracking module receives the radar data from the environment modeling and radar simulation modules, detects vehicles from the radar data in real time, and tracks the detected vehicles in real time; The signal processing AI analysis and optimization module uses the deep learning algorithm in machine learning technology to intelligently analyze and optimize the radar signal and vehicle trajectory data of the vehicle detection and tracking module, determine the dangerous state of the detection target, and output the analysis results to the visual warning module; The visual warning module receives the target hazard analysis results of the signal processing AI analysis and optimization module, and displays them to the user in an intuitive manner, providing real-time alarm function.

Images