Cooperative tracking of multiple maneuvering targets based on AIMM-PF

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.