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

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

通信学报 ›› 2013, Vol. 34 ›› Issue (11): 8-17.doi: 10.3969/j.issn.1000-436x.2013.11.002

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¹,宋华华¹,林广²

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

出版日期:2013-11-25 发布日期:2017-06-23
基金资助:
国家自然科学基金资助项目;浙江省自然科学基金资助项目;浙江省自然科学基金资助项目;浙江省环保科技计划基金资助项目;浙江省安全生产科技计划基金资助项目;杭州市科技局科技计划基金资助项目

Target tracking algorithm for wireless sensor networks based on particle swarm optimization and metropolis-hasting sampling particle filter

Peng JIANG¹,Hua-hua SONG¹,Guang LIN²

¹ Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China
² Zhejiang Province EnvironmentalMonitoring, Hangzhou 310012, China

Online:2013-11-25 Published:2017-06-23
Supported by:
The National Natural Science Foundation of China;The Natural Science Foundation of Zhejiang Province;The Natural Science Foundation of Zhejiang Province;The Environmental Protection Plan of Zhejiang Province;The Safety Science and Technology Plan of Zhejiang Province;The Science and Technology Program of Science and Technology Department of Hangzhou

摘要/Abstract

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。

关键词: 粒子群优化, metropolis-hasting抽样, 粒子滤波, 目标跟踪

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.

Key words: particle swarm optimization, metropolis-hasting sampling, particle filter, target tracking

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]. Journal on Communications, 2013, 34(11): 8-17.

图/表 11

表1

无线通信特征"

操作	能量消耗
发送电路E_Tx?elec
接收电路E_Rx?elec	50nJ/bit
E_Tx?elec=E_Rx?elec=E_elec
发送放大器E_amp	100pJ/bit/m²
程序执行器E_Rf	N_c×C× $V_{d d}^{2}$

表1

图1

表2

仿真参数设置"

参数名称	符号	取值	参数名称	符号	取值
监测区域	m×n	400m×400m	方差	$s_{0}^{2}$	1
节点数目	N	256	增益因子	g	±30%
感知半径	Rs	60m	衰减因子	x	2.2
通信半径	Rc	75m	传感器抽样次数	N_sam	1 000
初始状态	x₀	[-45 -20 -50 -35]^T	非线性常量	c	1.5
参数	a	180	粒子总数	N_p	400
步长	T	0.04	系统参量	j₁	2
控制参数	c₁	0.75	系统参量	j₂	2
控制参数	c₂	1	Burn-in period	B	20
控制常量	q	0.002	MCMC迭代次数	J	50

表2

图2

图3

图4

图5

图6

图7

图8

图9

参考文献 16

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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

Target tracking algorithm for wireless sensor networks based on particle swarm optimization and metropolis-hasting sampling particle filter

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