吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (2): 658-662.doi: 10.13229/j.cnki.jdxbgxb201502048

• 论文 • 上一篇    下一篇

基于Unscented信息滤波器的分布式目标融合跟踪

杨小军   

  1. 长安大学 信息工程学院, 西安 710064
  • 收稿日期:2013-08-05 出版日期:2015-04-01 发布日期:2015-04-01
  • 作者简介:杨小军(1971),男,教授,博士.研究方向:统计信号处理,多目标跟踪,非线性滤波,无线传感器网络与多源信息融合.E-mail:xjyang@chd.edu.cn
  • 基金资助:
    国家自然科学基金项目(61473047,60975028);陕西省科学技术发展研究计划项目(2014K05-26).

Distributed fusion and target tracking based on Unscented information filter

YANG Xiao-jun   

  1. School of Information Engineering, Chang'an University, Xi'an 710064, China
  • Received:2013-08-05 Online:2015-04-01 Published:2015-04-01

摘要: 针对无线传感器网络下的非线性运动目标跟踪问题,提出一种基于Unscented信息滤波器的分布式融合跟踪算法。该算法在信息滤波器框架下将Unscented变换与扩展信息滤波器相结合,有效地解决了运动目标和量测的非线性。在网络拓扑结构和通讯带宽的约束下,利用卡尔曼一致性滤波算法对所有传感器节点估计值进行分布式信息融合。仿真结果表明了该算法的有效性和优越性。

关键词: 信息处理技术, 传感器网络, 分布式估计, Unscented变换

Abstract: A distributed tracking and fusion algorithm based on Unscented information filter is proposed for nonlinear target tracking in wireless sensor networks. In this algorithm, the unscented transformation is combined with extended information filter to handle the nonlinearity of the target motion and measurement in the framework of information filtering. The Kalman consensus filter is used as distributed fusion structure to combine the estimate of each local sensor node in the sensor networks with constrained topology and limited bandwidth. The efficiency and the superiority of the proposed algorithm are demonstrated by simulation results.

Key words: information processing technology, sensor networks, distributed estimation, Unscented transformation

中图分类号: 

  • TN911.23
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