结合幅度信息的粒子滤波机动目标被动跟踪

为提高杂波条件下的机动目标被动跟踪的性能,提出了一种新的粒子滤波目标被动跟踪算法。在声纳的输出端,提取信号的幅度信息(AI),建立多模型对转弯机动目标进行状态估计,以粒子滤波算法作为基本跟踪滤波算法,将AI与概率数据关联(PDA)算法中的似然比相结合,详细推导了结合AI的粒子滤波目标被动跟踪算法(PF-AI)实现的具体过程。在同一被动目标跟踪场景,同时使用单纯PDA算法、结合辅助信息的PDA算法和PF-AI进行被动跟踪仿真,分析了轨迹跟踪性能,并使用均方根误差比较了误差性能。仿真结果表明,与两种基于PDA的跟踪算法相比,PF-AI具有更高的跟踪精度,且算法易于实现。

Amplitude information based particle filtering for passive tracking of maneuvering target

To improve the performance of passive tracking for maneuvering target in clutter,a new particle filtering algorithm for passive tracking is proposed.At the output of sonar,the Amplitude Information(AI) of signal is extracted.Based on the particle filtering,multiple models are constructed to obtain the state estimation of the target making turning maneuver.The likelihood ratio of AI is combined with that of Possibility Data Association(PDA),and detailed implementation steps of Particle Filtering tracking algorithm with Amplitude Information(PF-AI) are deduced.Passive tracking on simulated data is performed by the single PDA,the PDA with AI and the PF-AI schemes,and performance of tracking and error is analyzed by trajectory tracking and the root mean square error.Simulation results show that the PF-AI scheme has better performance than other two PDA-based schemes in tracking.Furthermore,the PFAI scheme is easy to implement.