2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

一种适用于稀疏无线传感器网络的改进分布式UIF算法

汤文俊 张国良 曾静 孙一杰 吴晋

汤文俊, 张国良, 曾静, 孙一杰, 吴晋. 一种适用于稀疏无线传感器网络的改进分布式UIF算法. 自动化学报, 2014, 40(11): 2490-2498. doi: 10.3724/SP.J.1004.2014.02490
引用本文: 汤文俊, 张国良, 曾静, 孙一杰, 吴晋. 一种适用于稀疏无线传感器网络的改进分布式UIF算法. 自动化学报, 2014, 40(11): 2490-2498. doi: 10.3724/SP.J.1004.2014.02490
TANG Wen-Jun, ZHANG Guo-Liang, ZENG Jing, SUN Yi-Jie, WU Jin. An Improved Distributed Unscented Information Filter Algorithm for Sparse Wireless Sensor Networks. ACTA AUTOMATICA SINICA, 2014, 40(11): 2490-2498. doi: 10.3724/SP.J.1004.2014.02490
Citation: TANG Wen-Jun, ZHANG Guo-Liang, ZENG Jing, SUN Yi-Jie, WU Jin. An Improved Distributed Unscented Information Filter Algorithm for Sparse Wireless Sensor Networks. ACTA AUTOMATICA SINICA, 2014, 40(11): 2490-2498. doi: 10.3724/SP.J.1004.2014.02490

一种适用于稀疏无线传感器网络的改进分布式UIF算法

doi: 10.3724/SP.J.1004.2014.02490
基金项目: 

陕西省基金项目 (2012K06-45)资助

详细信息
    作者简介:

    张国良 第二炮兵工程大学教授, 博士.主要研究方向为机器人技术, 先进控制理论与应用. E-mail: zhgl@sohu.com

    通讯作者:

    汤文俊, 第二炮兵工程大学博士研究生.主要研究方向为多智能体协同导航与控制, 无线传感器网络信息融合. 本文通信作者. E-mail: 13468972665@163.com

An Improved Distributed Unscented Information Filter Algorithm for Sparse Wireless Sensor Networks

Funds: 

Supported by Fund Program of Shaanxi Province (2012K06-45)

  • 摘要: 分布式无迹信息滤波(Distributed unscented information filter,DUIF)算法是一种有效的非线性分布式状态估计多源信息融合方法,然而当将该算法应用于稀疏无线传感器网络(Wireless sensor networks,WSN)时,稀疏WSN中存在的无效节点会引起使滤波趋于发散的平均一致误差.针对该问题,本文提出一种改进DUIF算法.该算法不改变DUIF算法的级联结构,而是将其底层和上层滤波器分别改进为局部无迹信息滤波器(Local unscented information filter,LUIF)和加权平均一致性滤波器.LUIF对每个节点的局部多源观测信息进行局部融合,得到局部的后验估计信息向量和矩阵,进而将它们作为加权平均一致性滤波器的输入,最终得到不包含平均一致误差的分布式后验估计结果.其中,加权平均一致性滤波器是通过对由LUIF输出的局部后验估 计信息向量和矩阵分别进行平均一致性滤波而得以在改进DUIF算法框架下实现的.同时,在此过程中,相邻节点之间的状态估计互相关信息也被引入改进DUIF算法的输出结果中,进一步增强了滤波的可靠性.仿真实验结果表明,改进DUIF算法能够在稀疏WSN中对机动目标进行有效跟踪,在估计精度和抑制滤波发散方面明显优于标准DUIF算法.
  • [1] Luo Xu, Chai Li, Yang Jun. Offshore pollution source localization in static water using wireless sensor networks. Acta Automatica Sinica, 2014, 40(5): 849-861(罗旭, 柴利, 杨君. 无线传感器网络下静态水体中的近岸污染源定位. 自动化学报, 2014, 40(5): 849-861)
    [2] Olfati-Saber R, Shamma J S. Consensus filters for sensor networks and distributed sensor fusion. In: Proceedings of 44th IEEE Conference on Decision and Control. Seville, Spain: IEEE, 2005. 6698-6703
    [3] Saber R O, Murray R M. Consensus protocols for networks of dynamic agents. In: Proceedings of the 2003 American Control Conference. Denver, CO, USA: IEEE, 2003. 951-956
    [4] Olfati-Saber R, Murray R M. Consensus problems in networks of agents with switching topology and time-delays. IEEE Transactions on Automatic Control, 2004, 49(9): 1520-1533
    [5] Yang Hong-Yong, Guo Lei, Zhang Yu-Ling, Yao Xiu-Ming. Movement consensus of complex fractional-order multi-agent systems. Acta Automatica Sinica, 2014, 40(3): 489-496(杨洪勇, 郭雷, 张玉玲, 姚秀明. 复杂分数阶多自主体系统的运动一致性. 自动化学报, 2014, 40(3): 489-496)
    [6] Li W L, Jia Y M. Distributed consensus filtering for discrete-time nonlinear systems with non-Gaussian noise. Signal Processing, 2012, 92(10): 2464-2470
    [7] Vercauteren T, Wang X. Decentralized sigma-point information filters for target tracking in collaborative sensor networks. IEEE Transactions on Signal Processing, 2005, 53(8): 2997-3009
    [8] Li W L, Jia Y M. Consensus-based distributed multiple model UKF for jump Markov nonlinear systems. IEEE Transactions on Automatic Control, 2012, 57(1): 227-233
    [9] Olfati-Saber R. Distributed Kalman filter with embedded consensus filters. In: Proceedings of the 44th IEEE Conference on Decision and Control and European Control Conference. Seville, Spain: IEEE, 2005. 8179-8184
    [10] Kamgarpour M, Tomlin C. Convergence properties of a decentralized Kalman filter. In: Proceedings of the 47th IEEE Conference on Decision and Control. Cancun, Mexico: IEEE, 2008. 3205-3210
    [11] Bai H, Freeman R A, Lynch K M. Distributed Kalman filtering using the internal model average consensus estimator. In: Proceedings of the 2011 American Control Conference. San Francisco, CA: IEEE, 2011. 1500-1505
    [12] Li Chang-Sheng, Wang Yu-Zhen. Protocol design for output consensus of port-controlled Hamiltonian multi-agent systems. Acta Automatica Sinica, 2014, 40(3): 415-422(李长生, 王玉振. 端口受控哈密顿多智能体系统的输出一致性协议设计. 自动化学报, 2014, 40(3): 415-422)
    [13] Lee D J. Nonlinear estimation and multiple sensor fusion using unscented information filtering. Signal Processing Letters, 2008, 15: 861-864
    [14] Julier S, Uhlmann J, Durrant-Whyte H F. A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 2000, 45(3): 477-482
    [15] Sibley G, Sukhatme G, Matthies L. The iterated sigma point filter with applications to long range stereo. In: Proceedings of the Robotics Science and Systems. Philadelphia, USA: MIT Press, 2006: 263-270
    [16] Kingston D B, Beard R W. Discrete-time average-consensus under switching network topologies. In: Proceedings of the 2006 American Control Conference. Minnesota, USA: IEEE, 2006. 3551-3556
    [17] Demetriou M A. Design of consensus and adaptive consensus filters for distributed parameter systems. Automatica, 2010, 46(2): 300-311
    [18] Kamal A T, Ding C, Song B, Farrell J A, Roy-Chowdhury A K. A generalized Kalman consensus filter for wide-area video networks. In: Proceedings of Decision and Control and European Control Conference. Orlando, FL: IEEE, 2011. 7863-7869
    [19] Kamal A T, Ding C, Song B, Farrell J A, Roy-Chowdhury A K. Distributed Kalman filtering for sensor networks. In: Proceedings of 46th IEEE Conference on Decision and Control. New Orleans, LA: IEEE, 2007. 5492-5498
    [20] Olfati-Saber R, Fax J A, Murray R M. Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 2007, 95(1): 215-233
    [21] Yang Wen. Consensus Problem in Multi-Agent Systems [Ph.D. dissertation], Shanghai Jiao Tong University, China, 2009(杨文. 多智能体系统一致性问题研究 [博士学位论], 上海交通大学, 中国, 2009)
    [22] Olfati-Saber R. Kalman-consensus filter: optimality, stability, and performance. In: Proceedings of the 48th IEEE Conference on Decision and Control Held Jointly with the 28th Chinese Control Conference. Shanghai, China: IEEE, 2009. 7036-7042
  • 加载中
计量
  • 文章访问数:  1724
  • HTML全文浏览量:  61
  • PDF下载量:  883
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-09-26
  • 修回日期:  2014-06-30
  • 刊出日期:  2014-11-20

目录

    /

    返回文章
    返回