Vehicle Tracking And Vehicle Identification System Based On FMCW Millimeter Wave Radar

The rapid development of China's national economy rapidly increases urban traffic volume.The problem of traffic jams and frequent traffic accidents have become increasingly serious.Real-time vehicle monitoring and control for traffic intersections can prevent most traffic accidents from the root,and intelligent traffic monitoring system based on traffic infrastructure has become an effective alternative to artificial monitoring.The intelligent traffic detection system can monitor the vehicle position and speed on the road in real time,count the number of vehicles in the section,further improve the traffic environment of the intersection,and improve the traffic safety guarantee.In this paper,the research status of traffic monitoring is analyzed from the technical level.By comparing the advantages and disadvantages of video based,lidar based and millimeter wave radar based technologies,the advantages of using FMCW millimeter wave radar technology in intelligent traffic detection system are proposed.In this paper,the method of acquiring vehicle point cloud data based on radar raw data is realized,the vehicle point cloud clustering algorithm based on ellipse is improved,the vehicle track determination algorithm and track correction algorithm based on the characteristics of vehicle point cloud are designed,a new vehicle type setting method is proposed and a reliable vehicle type determination algorithm is realized.In the vehicle point cloud data acquisition algorithm,firstly,the radar raw data is used for two-dimensional FFT algorithm to obtain the range and Doppler two-dimensional map data,and the coordinate information of vehicle point cloud is obtained by CFAR peak detection algorithm and DOA direction detection algorithm.In the implementation of vehicle point cloud clustering algorithm,the length,width,height and distribution characteristics of vehicle point cloud are statistically analyzed,and the clustering error analysis on two-dimensional and three-dimensional data level is discussed.In the vehicle tracking algorithm,the focus is on the distribution and moving characteristics of the vehicle point cloud,and the accuracy of the results is improved by the design of targeted trajectory correction algorithm.In the algorithm of vehicle classification,the feasibility analysis of the selection of vehicle classification features is carried out,and the simple and efficient vehicle classification is realized based on the track tracking algorithm.Based on Python and pyqt5,this paper develops an interface data analysis and display tool to realize algorithm development and application result display.It can detect the vehicle flow in the actual traffic monitoring scene many times and achieve good results.