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%.
Abstract：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.
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