车载毫米波雷达对前方目标的运动状态估计  

Target motion state estimation for vehicle-borne millimeter-wave radar

作　　者：高振海[1] 王竣[1] 佟静[1] 李红建[2] 郭章勇 娄方明[1] 

机构地区：[1]吉林大学汽车仿真与控制国家重点实验室,长春130022 [2]中国第一汽车集团公司技术中心,长春130011

出　　处：《吉林大学学报（工学版）》2014年第6期1537-1544,共8页Journal of Jilin University:Engineering and Technology Edition

基　　金：'973'国家重点基础研究发展计划项目-前期研究专项(2012CB723802);长江学者和创新团队发展计划项目(IRT1017)

摘　　要：基于对汽车前方目标运动特点和车载雷达信息检测机理的分析,在大地坐标系、本车的车辆运动坐标系和车载雷达运动坐标系的相对运动关系基础上,考虑了地面车辆运动以地表平面上二维运动为主、机动性小、跟踪坐标系运动的特点,建立了基于车载雷达运动坐标系的前方目标的运动状态模型。并考虑到系统过程噪声及雷达等车载传感器观测噪声的统计特性难以事先确定的问题,采用自适应卡尔曼滤波算法实现了前方目标的侧纵向速度和侧纵向位置等运动状态的完备准确实时估计。最终通过真实道路交通环境下装备毫米波雷达和高精度汽车状态测试系统的实车对比试验,对算法的可行性和估计精度进行了试验验证,试验结果显示:估计结果具有良好的精度,且长时间跟踪过程中滤波收敛稳定。With the analysis of preceding object motion feature and vehicle-borne radar measuring principle, a novel target motion model is established in the vehicle-borne radar coordinate system. This target motion model considers the relative motion of the intertial coordinate, the vehicle coordinate and the radar coordinate system. Also other specialness of ground vehicle were taken into account in the model, such as that the motion of ground vehicle is a 2D motion because of the limitation of ground surface, the vehicle mobility is small and the tracking coordinate system is moving. The whole motion states of the preceding target, including the longitudinal and lateral velocities, the longitudinal and lateral positions were estimated by the algorithm of adaptive Kalman filter, because it was difficult to determine the statistics of the system process noise and measure noise. Finally, road experiments, in which the host car was equipped with millimeter-wave radar and the preceding car was equipped with high precision automotive testing equipments, were carried out to verify the feasibility and performance of the estimation method. The results prove that the method canprovide fine estimation accuracy, better filter convergence and stability.

关 键 词：车辆工程 车载毫米波雷达 前方目标运动模型 状态估计 自适应卡尔曼滤波 

分 类 号：U461.91[机械工程—车辆工程]