文档介绍：第 41 卷 第 6 期 自 动 化 学 报 Vol. 41, No. 6
2015 年 6 月 ACTA AUTOMATICA SINICA June, 2015
多点测试的多模型机动目标跟踪算法
王 伟 1 余玉揆 1
摘 要 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.
关键词 机动目标跟踪, 多点测试, 多模型, 并行粒子滤波
引用格式 王伟, 余玉揆. 多点测试的多模型机动目标跟踪算法. 自动化学报, 2015, 41(6): 1201−1212
DOI .
Multi-try and Multi-model Particle Filter for Maneuvering Target Tracking
WANG Wei1 YU Yu-Kui1
Abstract 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 filt