计算机工程与应用 ›› 2015, Vol. 51 ›› Issue (17): 217-221.

• 信息与信号处理 • 上一篇    下一篇

基于自适应变结构信息滤波的目标跟踪算法

李  莹1,周德云1,俞  吉2   

  1. 1.西北工业大学 电子信息学院,西安 710129
    2.中国航空无线电电子研究所,上海 200241
  • 出版日期:2015-09-01 发布日期:2015-09-14

New maneuvering target tracking algorithm based on adaptive variable structure multi-model and information filtering

LI Ying1, ZHOU Deyun1, YU Ji2   

  1. 1.School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China
    2.China Aeronautical Radio Electronics Research Institute, Shanghai 200241, China
  • Online:2015-09-01 Published:2015-09-14

摘要: 目前目标跟踪算法采用的交互多模型,大多是通过固定模型之间的切换来完成目标跟踪,这容易出现模型集与目标真实运动不匹配问题,降低目标跟踪的精度。同时,现在大部分观测平台都能提供多传感器量测,这要求跟踪算法能对不同量测信息进行高效数据融合。针对上述问题,提出一种基于自适应变结构多模型和信息滤波的跟踪算法,它由少量模型构成模型集,通过在线更新模型集参数以自适应目标真实运动,采用无迹卡尔曼信息滤波融合多传感器量测信息,实现对目标的跟踪。仿真结果表明,该算法可以有效融合多传感器量测信息,自适应匹配目标真实运动,实现对目标稳定的高精度跟踪。

关键词: 目标跟踪, 交互多模型, 自适应变结构, 信息滤波, 无迹卡尔曼滤波

Abstract: Most current maneuvering target tracking algorithms use Interacting Multiple Model (IMM) by switching fixed models, which can easily cause problems like model-mismatching and thus resulting in inaccurate tracking. Meanwhile, multiple sensors are now generally available to provide multi-measurements for target tracking, provoking the needs to efficiently utilize them. To solve these problems, a new algorithm that consists of less models and integrates adaptive Variable Structure Multi-Model (VSMM) and Information Filtering (IF) is presented, which accomplishes better estimation of maneuvering target’s trajectory through updating the model-set’s parameters in real time and fusing multi-measurements by unscented Kalman IF. Simulation results prove that this new algorithm can efficiently fuse multi-measurements, adaptively match target’s real model and reach a high level of target tracking.

Key words: target tracking, Interacting Multiple Model(IMM), adaptive variable structure, information filtering, Unscented Kalman Filtering(UKF)