基于粒子群优化和M-H抽样粒子滤波的传感器网络目标跟踪方法

蒋鹏; 宋华华; 林广

doi:10.3969/j.issn.1000-436x.2013.11.002

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基于粒子群优化和M-H抽样粒子滤波的传感器网络目标跟踪方法

学术论文

- 基于粒子群优化和M-H抽样粒子滤波的传感器网络目标跟踪方法
- Target tracking algorithm for wireless sensor networks based on particle swarm optimization and metropolis-hasting sampling particle filter
- 通信学报 2013年34卷第11期页码：8-17
- 作者机构：
  
  1. 杭州电子科技大学信息与控制研究所，浙江杭州 310018
  2. 浙江省环境监测中心，浙江杭州 310012
- 作者简介：
  
  [ "蒋鹏（1975-），男，浙江衢州人，博士，杭州电子科技大学教授、博士生导师，主要研究方向为无线传感器网络、物联网技术及其应用、嵌入式系统及其应用、智能仪表。" ]
  [ "宋华华（1986-），男，浙江湖州人，杭州电子科技大学硕士生，主要研究方向为无线传感器网络。" ]
  [ "林广（1975-），男，浙江温州人，硕士，浙江省环境监测中心高级工程师，主要研究方向为环境监测、环境质量评价。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61273072);浙江省自然科学基金资助项目(Y1100054);浙江省自然科学基金资助项目(Y1111220);浙江省环保科技计划基金资助项目(2013A034);浙江省安全生产科技计划基金资助项目(2013A1006);杭州市科技局科技计划基金资助项目(20120433B40)
- DOI：10.3969/j.issn.1000-436x.2013.11.002
  中图分类号： TP393
- 网络出版日期：2013-11，
  
  纸质出版日期：2013-11-25
- 稿件说明：
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蒋鹏, 宋华华, 林广. 基于粒子群优化和M-H抽样粒子滤波的传感器网络目标跟踪方法[J]. 通信学报, 2013,34(11):8-17.

Peng JIANG, Hua-hua SONG, Guang LIN. Target tracking algorithm for wireless sensor networks based on particle swarm optimization and metropolis-hasting sampling particle filter[J]. Communication journal, 2013, 34(11): 8-17.
蒋鹏, 宋华华, 林广. 基于粒子群优化和M-H抽样粒子滤波的传感器网络目标跟踪方法[J]. 通信学报, 2013,34(11):8-17. DOI： 10.3969/j.issn.1000-436x.2013.11.002.

Peng JIANG, Hua-hua SONG, Guang LIN. Target tracking algorithm for wireless sensor networks based on particle swarm optimization and metropolis-hasting sampling particle filter[J]. Communication journal, 2013, 34(11): 8-17. DOI： 10.3969/j.issn.1000-436x.2013.11.002.

摘要

针对实际应用条件下传感器节点的观测数据与目标动态参数间呈现为非线性关系的特性，提出了一种基于粒子群优化和M-H抽样粒子滤波的传感器网络目标跟踪方法。该方法采用分布式结构，在动态网络拓扑结构下，由粒子群优化和M-H抽样技术实现滤波中的重抽样过程，抑制粒子退化现象，并通过粒子间共享历史信息，降低单个粒子历史状态间的相关性使各粒子能快速收敛至最优分布，从而实现高精度的目标跟踪效果。仿真结果表明，相比现有的基于信息粒子滤波和并行粒子滤波技术的传感器网络目标跟踪方法，所提出的方法能降低网络总能耗，同时保证目标跟踪的精度。

Abstract

For the characteristic of the nonlinear relationship between the observation information of sensor nodes and the target dynamic parameters under the real application conditions

a target tracking algorithm for wireless sensor networks based on particle swarm optimization and Metropolis-Hasting sampling particle filter was proposed. Distributed archi-tecture is adopted in this target tracking scheme. And under the dynamic network topology

particle swarm optimization and Metropolis-Hasting sampling are introduced into the resampling period to reduce sample degeneracy. In order to achieve the goal of high-precision tracking performance

the history information is shared among the particles to reduce the correlation between the history states of a single particle

so that the particles can rapidly converge to an optimal dis-tribution. The simulations corroborate that compared with currently existing target tracking schemes based on the tech-nology of information particle filter and parallel particle filter

the proposed scheme can reduce the total energy consump-tion

while ensuring the accuracy of target tracking.

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Keywords

references

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