Target tracking using distributed Unscented particle filter in sensor network

XIAO Yan-guo; WEI Jian-ming; XING Tao; Yin Ze-jie; LIU Hai-tao

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Target tracking using distributed Unscented particle filter in sensor network

Article | 更新时间：2020-08-12

- Target tracking using distributed Unscented particle filter in sensor network
- Optics and Precision Engineering Vol. 17, Issue 7, Pages: 1707-1713(2009)
- 作者机构：
  
  1. 中国科学技术大学近代物理系,安徽合肥,230026
  2. 中国科学院上海微系统与信息技术研究所上海,200050
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN915;TN911.7
- Received：14 March 2008，
  
  Revised：03 June 2008，
  
  Published Online：25 July 2009，
  
  Published：25 July 2009
- 稿件说明：
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肖延国, 魏建明, 邢涛, 阴泽杰, 刘海涛. 分布式Unscented粒子滤波跟踪[J]. 光学精密工程, 2009,17(7): 1707-1713

XIAO Yan-guo, WEI Jian-ming, XING Tao, Yin Ze-jie, LIU Hai-tao. Target tracking using distributed Unscented particle filter in sensor network[J]. Editorial Office of Optics and Precision Engineering, 2009,17(7): 1707-1713
肖延国, 魏建明, 邢涛, 阴泽杰, 刘海涛. 分布式Unscented粒子滤波跟踪[J]. 光学精密工程, 2009,17(7): 1707-1713 DOI：

XIAO Yan-guo, WEI Jian-ming, XING Tao, Yin Ze-jie, LIU Hai-tao. Target tracking using distributed Unscented particle filter in sensor network[J]. Editorial Office of Optics and Precision Engineering, 2009,17(7): 1707-1713 DOI：

摘要

提出了一种新的分布式粒子跟踪算法

该算法主要考虑传感网络能量受限、通信受限等特性

改善了通常的分布式粒子滤波粒子数目大、节点间信息交换多的弊端

能够用较少的节点计算得到对机动目标更好的跟踪结果

实现了改进的分布式粒子滤波(DUPF)。DUPF算法的主要思想是利用Unscented Kalman滤波改进分布式粒子滤波算法形成一个建议分布

用来生成粒子分布

在这个基础上

通过分布式粒子滤波实现目标的在线跟踪。仿真实验表明

和分布式粒子滤波相比

DUPF只需要其25%的粒子数目就能达到同样的跟踪精度

即可用较少的节点和通信消耗

实现高精度的目标跟踪。

Abstract

A new distributed target tracking algorithm

Distributed Unscented Particle Filter(DUPF)

is proposed to improve the usual distributed particle filter methods with more particles and more information communication between the two nodes. In consideration of the enengy-limited sensor network and the imperfect communication

a few node calculations are used in this thesis to get a better tracking results for manoeuvering targets.The Unscented Kalman Filter(UKF) in new DUPF is used to improve the particle filter to generate the proposed particle distribution

so the on line tracking for a target can be realized by the DUPF. A simulation experiment indicates that the number of particles needed by DUPF is only 25% that of the common distributed particle filter

which shows that DUPF has gotten more accurate tracking results with less communication nodes and energy cosumption.

关键词

Keywords

references

VERACAUTEREN T,WANG X D. Decentralized sigma-point information filters for target tracking in collaborative sensor networks[J]. IEEE Transactions on Signal Processing,2005,53(8):2997-3009.[2] RIBEIRO A, GIANNAKIS G B, ROUMELIOTIS S I. SOI-KF: Distributed kalman filtering with low-cost communications using the sign of innovations[J]. IEEE Transactions on Signal Processing, 2006,54(12):4782 - 4795.[3] GUPTA R, DAS S R. Tracking moving targets in a smart sensor network . Vehicular Technology Conference, IEEE 58th, Orlando, USA, IEEE Press, 2003(5):3035-303.[4] ING G, COATES M J. Parallel particle filters for tracking in wireless sensor network . 6th Workshop on Signal Processing Advances in Wireless Communications,2005, 935-939.[5] SHENG X H, HU Y H . Distributed particle filters for wireless sensor network target tracking . Acoustics, Speech, and Signal Processing, 2005. Proceedings(ICASSP'05),2005(4):845-848.[6] SHENG X H, HU Y H. Parameswaran Ra- manathan, Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network . 4th International Symposium on. Information Processing in Sensor Networks,2005:181-188.[7] VERCAUTEREN T, GUO D, WANG X D, Joint multiple target tracking and classification in collaborative sensor networks[J]. IEEE Journal on Selected Areas in Communications,2005,23(4):714 - 723.[8] 马瑞恒, 盛晓红. 无线传感网络中分布式粒子滤波的目标追踪算法[J]. 解放军理工大学学报(自然科学版),2006,7(5):421-426. MA R H, SHENG X H, Target tracking based on distributed particle filter in wireless sensor network[J]. Journal of PLA University of Science and Technology(Natural Science Edition),2006,7(5):421-426.(in Chinese)[9] 刘忠义, 张晓薇, 陈向群. 一种速度自适应的无线传感网络目标跟踪算法[J]. 华中科技大学学报(自然科学版),2005,33(z1): 335-339. LIU ZH Y, ZHANG X W, CHEN X Q. VATT: velocity adaptive target tracking algorithm for wireless sensor networks[J]. Journal of Huazhong University of Science and Technology(Nature Science),2005,33(z1):335-339. (in Chinese)[10] 唐剑, 史浩山, 韩忠祥. 无线传感器网络中的目标跟踪算法[J]. 空军工程大学学报(自然科学版)2006,7(5):25-29. TANG J, SHI H SH, HAN ZH X. Tracking algorithm for wireless sensor networks[J]. Journal of Air Force Engineering University(Natural Science Edition),2006,7(5):25-29.(in Chinese)[11] LI X R, JILKOV V P. Survey of maneuvering target tracking-part I: dynamic models[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003,39(4):1333-1364.[12] COATES M J, ING G. Sensor network particle filters: motes as particles . 2005 IEEE/SP 13th Workshop in Statistical Signal Processing,2005,1152-1157.[13] VIEIRA M A M, COELHO C N, DA SILVA D C. Survey on wireless sensor network devices . Emerging Technologies and Factory Automation,2003:537-544.[14] DOUCET A, GODSILL S, ANDRIEU C. On sequential Monte Carlo sampling methods for Bayesian filtering[J]. Statistics and Computing, 2000,10(3):197-208.[15] GORDON N J, SALMOND D J, SMITH A FM. Novel approach to nonlinear/non-Gaussian Bayesian state estimation[J]. IEEE Proceedings on Radar and Signal Processing, 1993,140(2):107-113.

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