基于辅助粒子滤波的机动目标跟踪研究

Research on Maneuvering Target Tracking by Auxiliary Particle Filtering

摘要:

针对机动目标跟踪中扩展卡尔曼算法(EKF)收敛速度慢、跟踪精度低的问题,基于粒子滤波(PF)和辅助粒子滤波(APF)的基本思想,结合目标先验信息将速度约束条件加入到跟踪过程中,对辅助粒子滤波算法进行了仿真分析,与扩展卡尔曼进行仿真对比,分析了跟踪性能和误差。仿真结果表明,对机动目标跟踪问题,辅助粒子滤波不仅解决了扩展卡尔曼线性化困难难题,与EKF相比还具有收敛速度快,跟踪精度高的优点。

Abstract:

To improve the poor performance of the Extended Kalman Filtering (EKF) in target tracking, Particle Filtering (PF) and Auxiliary Particle Filtering (APF) were introduced to tracking the maneuvering target together with the speed constraints. The performances were analyzed by comparison with the EKF. Simulation results show that APF algorithm can solve the difficult problem of linearity. Further more, the convergence rate and track accuracy of the algorithm can also be effectively improved.