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计算机工程

所属专题: WSN专题

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无线传感器网络中基于信念传播的分布式目标跟踪

王刘涛 1,夏栋梁 1,王建玺 1,马飞 1,2   

  1. (1.平顶山学院 软件学院,河南 平顶山 467000; 2.武汉大学 计算机学院,武汉 430072)
  • 收稿日期:2016-02-26 出版日期:2016-12-15 发布日期:2016-12-15
  • 作者简介:王刘涛(1981—),男,讲师、硕士,主研方向为图像处理、模式识别;夏栋梁、王建玺,讲师、硕士;马飞,副教授、博士研究生。
  • 基金资助:
    国家自然科学基金(61503206);河南省科技厅科技发展计划项目(142102210226)。

Distributed Target Tracking Based on Belief Propagation in Wireless Sensor Network

WANG Liutao  1,XIA Dongliang  1,WANG Jianxi  1,MA Fei  1,2   

  1. (1.Software Institute,Pingdingshan University,Pingdingshan,Henan 467000,China;2.School of Computer Science,Wuhan University,Wuhan 430072,China)
  • Received:2016-02-26 Online:2016-12-15 Published:2016-12-15

摘要: 为在分布式目标跟踪中交换局部似然函数的信息,研究常见的分布式目标跟踪方法,提出一种基于信念传播的分布式粒子滤波方法(DPF-BP)。在有限次的迭代中,计算图的最大直径。为避免网络评估的分歧性,在计算评估之前运用一致性最大化,将节点及迭代次数表示成函数形式,经过标准化和估值计算后重采样替换。仿真实验结果表明,与标准信念一致方法、随机流言方法和都市信念一致方法(MBC)相比,在相同配置下,DPF-BP方法的均方根误差指标较优,在环形网络中运用DPF-MBC方法较好,而在树状网络中运用DPF-BP方法最佳。

关键词: 目标跟踪, 似然函数, 分布式, 粒子滤波, 信念传播

Abstract: In order to exchange information of partial likelihood function in a distributed target tracking,several common distributed target tracking methods are studied,and a Distributed Particle Filter method based on Belief Propagation(DPF-BP) is proposed.The maximum diameter of the graph is calculated in a limited number of iterations.In order to avoid difference in network assessment,consistency maximization is used before assessing and nodes and the number of iterations are expressed as a function.After standardization and valuation calculations,the replacement is re-sampled.Simulation experimental results show that,compared with Standard Belief Consensus(SBC),Randomized Gossip(RG)and Metropolis Belief Consensus(MBC),under the condition of the same configuration,DPF-BP is excellent at RootMean Square Error(RMSE).In addition,DPF-MBC is best in the circular network,and DPF-BP is best in the tree network.

Key words: target tracking, likelihood function, distributed, particle filtering, belief propagation

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