计算机应用 ›› 2010, Vol. 30 ›› Issue (1): 167-170.

• 人工智能 • 上一篇    下一篇

新型粒子滤波算法及其在纯方位目标跟踪中的应用

王法胜1,张应博2   

  1. 1. 大连东软信息学院
    2.
  • 收稿日期:2009-07-15 修回日期:2009-08-27 发布日期:2010-01-01 出版日期:2010-01-01
  • 通讯作者: 王法胜

Novel particle filtering algorithm with application to bearing-only tracking

  • Received:2009-07-15 Revised:2009-08-27 Online:2010-01-01 Published:2010-01-01

摘要: 针对基本粒子滤波算法没有融合当前时刻观测值的缺点,提出了一种卡尔曼粒子滤波算法。该算法针对每一个粒子使用卡尔曼滤波器进行更新,在更新过程中融合最新的观测信息,提高粒子滤波器的估计精度。针对纯方位目标跟踪问题进行实验,与基本粒子滤波算法及卡尔曼滤波进行了对比。实验结果表明,卡尔曼粒子滤波算法的跟踪性能明显优于其他两种算法。

关键词: 粒子滤波, 卡尔曼滤波器, 目标运动分析, 线性跟踪系统

Abstract: The conventional bootstrap filter suffers a main drawback of not incorporating the latest observations. Therefore, this paper proposed a Kalman Particle Filter (KPF) algorithm, and applied this new algorithm to bearingonly target tracking. An improved scheme was presented to handle this problem and yield a Kalman particle filter. The underlying idea of the new algorithm is that each particle is updated using Kalman filter incorporating the coming observations. A bearingonly tracking model was experimented and compared with bootstrap filter and KPF. The experimental results verify its superiority.

Key words: particle filter, Kalman filter, target motion analysis, linear tracking system