基于路侧毫米波雷达的群体车辆目标识别与跟踪

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

为了提升道路交通流检测精度,本文提出了一种基于路侧毫米波雷达的群体车辆识别与跟踪方法。首先,基于预处理后的城市多车道主干路交通流毫米波雷达检测数据,提出了一种基于高斯核距离的带噪声密度空间聚类(DBSCAN)算法,实现对群体车辆所反射有效雷达信号的时空聚类;其次,提出了一种无迹卡尔曼滤波(UKF)和线性高斯混合概率假设密度(GMPHD)融合算法,以提升非线性运动群体车辆的跟踪精度;最后,在仿真和实际环境中进行算法测试,仿真结果验证了UK-GMPHD算法能够精准、稳定地跟踪非线性运动车辆。实测结果表明:基于核距离的DBSCAN算法能够有效改善经典算法特征向量的调参问题;与GMPHD算法对比,UKGMPHD算法对目标跟踪的距离、速度和角度均方根误差分别减少了21.03%、23.41%和20.67%。

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 UKGMPHD 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.