Target recognition and tracking of group vehicles based on roadside millimeter-wave radar

A method of group vehicle recognition and tracking based on roadside millimetre-wave radar was proposed to improve roadway traffic detection accuracy. Based on pre-processed detection data of millimetre-wave radar on multi-lane traffic flow in an urban arterial road，a Gaussian kernel-distance based spatial clustering algorithm with noise density（DBSCAN）was proposed to conduct spatio-temporal clustering of effective radar signals reflected by group vehicles. Then，a fusion algorithm of unscented Kalman filter （UKF） and linear Gaussian mixture probability hypothesis density（GMPHD）was proposed to improve tracking accuracy of group vehicles which move nonlinearly on the road. The algorithms were tested in simulation and onsite environment. Simulation results verified that the UK-GMPHD algorithm can accurately and stably track nonlinear moving vehicles. Results of onsite test showed that the kernel-distance based DBSCAN algorithm can solve the problem of classical algorithm effectively that the parameter tuning of feature vector was difficult to adjust. The UK-GMPHD algorithm reduced the root mean square error of target tracking in term of target distance，velocity and angle by 21.03%，23.41% and 20.67% in comparison with GMPHD algorithm. © 2024 Editorial Board of Jilin University. All rights reserved.