一种基于M-APF的实时UAV目标跟踪算法

Real Time UAV Target Tracking Method Based on M-APF

针对中小型UAV(Unmanned Aerial Vehicle)平台运动目标跟踪系统由于受机载摄像条件及数据传输时延等因素,造成跟踪结果实时性不高、目标被遮挡时易丢失的问题,提出一种基于M-APF(Meanshift-Auxiliary Particle Filter)的UAV运动目标跟踪算法。该方法采用APF作为跟踪算法的主体框架,同时引入Meanshift计算少量辅助采样粒子的偏移,并将其移动到观测值的局部最优位置,解决了APF算法计算量大的问题,提高算法实时性。仿真结果显示:算法满足UAV平台运动目标跟踪要求,实时性及鲁棒性优于Meanshift和APF等算法。

Focusing on the problems of the target information estimated delay and the target lost for the occlusion reasons in the UAV moving target tracking system, a novel target tracking method based on M-APF （Meanshift-Auxiliary Particle Filter） was proposed. It adopted the APF as the main framework of the tracking algorithm, then the Meanshift was applied to calculate the offset of a few auxiliary particle and moved them to the local optimum position of the observed values. As a result, it solved the large calculation of the APF algorithm, and improved the real-time of the method. Simulation results show that the calculation cost and robustness of the M-APF is superior to Meanshift and APF algorithm, and satisfies the UAV target tracking requirements.