基于AIMM-PF的多机动目标协同跟踪  

Cooperative tracking of multiple maneuvering targets based on AIMM-PF

作　　者：张洲[1] 梁军[1] 张致豪 陈小波 陈龙[1] 魏文权 李慧 ZHANG Zhou;LIANG Jun;ZHANG Zhihao;CHEN Xiaobo;CHEN Long;WEI Wenquan;LI Hui(Automotive Engineering Research Institute,Jiangsu University,Zhenjiang,Jiangsu 212013,China;School of Computer Science and Technology,Shandong Technology and Business University,Yantai,Shandong 264005,China;Jiangsu Hanfeng CNC Technology Co.,Ltd.,Taizhou,Jiangsu 225500,China)

机构地区：[1]江苏大学汽车工程研究院,江苏镇江212013 [2]山东工商学院计算机科学与技术学院,山东烟台264005 [3]江苏汉峰数控科技有限公司,江苏泰州225500

出　　处：《江苏大学学报（自然科学版）》2024年第4期434-440,共7页Journal of Jiangsu University：Natural Science Edition

基　　金：国家自然科学基金资助项目(61773184);国家重点研发计划项目(2018YFB1600503);江苏省“六大人才高峰”高层次人才计划项目(2015-DZXX-048)。

摘　　要：针对常规线性卡尔曼滤波越来越不能满足多机动目标跟踪精度需求的问题,提出一种基于自适应多模型粒子滤波的协同跟踪方法.首先,主车和协同车分别执行自适应交互式多模型粒子滤波(adaptive interactive multi model particle filter,AIMM-PF)算法,获得环境中目标车辆的运动状态;其次,协同车通过车车通信将跟踪到的目标状态发送给主车;最后,利用基于匈牙利算法和快速协方差交叉算法的数据关联和数据融合技术实现多机动目标的协同跟踪.搭建了V2V通信、雷达和定位仿真系统,选定两辆智能车作为主车和协同车,感知并跟踪200 m范围内的7辆目标车,进行了仿真试验.结果表明,与传统的单车跟踪相比,协同跟踪扩大了感知范围,且在不影响跟踪效率的情况下使跟踪误差降低了31.1%.To solve the problem that conventional linear Kalman filtering was increasingly unable to meet the demand of multi-motorized target tracking accuracy,a cooperative tracking method based on adaptive multi-model particle filtering was proposed.The host vehicle and the cooperative vehicle respectively executed the adaptive interactive multi model particle filter(AIMM-PF)algorithm to obtain the motion states of the target vehicles in the environment.By the cooperative vehicle,the tracked target state was sent to the host vehicle through vehicle-to-vehicle communication.The data association and data fusion techniques based on the Hungarian algorithm and the fast covariance crossover algorithm were utilized to achieve cooperative tracking of multiple maneuvering targets.The V2V communication,radar and localization simulation system were built to sense and track seven target vehicles within 200 meters range with two intelligent vehicles as the host vehicle and the cooperative vehicle,and the simulation experiments were completed.The results show that compared with the traditional single-vehicle tracking,by the cooperative tracking,the sensing range is expanded,and the tracking error is reduced by 31.1%without affecting the tracking efficiency.

关 键 词：智能网联汽车 车车通信 协同跟踪 多机动目标 交互式多模型 轨迹关联 轨迹融合 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]