多点测试的多模型机动目标跟踪算法

摘要

A novel multiple try and multiple model particle filter named independence multi-try method (IMTM) is presented for maneuvering target tracking. It can be divided into two stages: 1) A multi-try test structure is utilized to choose an optimal particle from a set of particles generated by multiple models;this stage brings about model interaction. 2) Independence Metropolis-Hastings (IMH) will reject or accept the particle produced in stage 1. This method does not require the model conversion matrix so that it can reduce the man-made error introduced by the model conversion matrix as used in traditional methods. It is a parallel and simple structure particle filter. With this method, the particles are exchanged between models automatically and effectively to adjust the model weight. Simulations show that this algorithm can effectively reduce the peak-error, and is a real-time algorithm.%针对机动目标跟踪问题,提出了一种多点测试多模型粒子滤波算法(Independence multi-try method, IMTM)。整个算法分为两个阶段,第一阶段为利用多点测试(Multi-try method, MTM)结构从各模型产生的粒子中选取一个最优粒子,实现了模型间的交互；第二阶段为利用IMH (Independence Metropolis-Hastings)滤波算法对第一阶段产生的粒子进行取舍,完成整个状态估计。相对于传统的交互式多模型(Interacting multiple model, IMM)算法,该算法无需事先设定模型转移概率矩阵且为整体并行结构,结构简单,能够充分地交互各模型之间的粒子,进而自动有效地调整各模型权值比重,降低了人为干扰。仿真表明,该算法能够有效地降低滤波峰值误差,整体跟踪精度较高,算法的实时性较好。