Application of an improved particle filter algorithm for target tracking in WSN

Xiao Ping Huang; Yan Wang; Chao Li; Jing Yu Shao

Application of an improved particle filter algorithm for target tracking in WSN

Xiao Ping Huang
, Yan Wang^*
, Chao Li
, Jing Yu Shao

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Beihang University

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

A novel PF algorithm was proposed. In this algorithm, the maximum likelihood (ML) was firstly used to estimate the latest position of mobile target, then the Kalman filter (KF) was used to get the more accurate state (their instantaneous locations and velocities) information and covariance of mobile target, which eventually provide a proposal distribution for PF. The simulation indicates that this improved PF has not only a higher tracking accuracy but also a better characteristic of real-time.

Original language	English
Pages (from-to)	895-899
Number of pages	5
Journal	Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology)
Volume	42
Issue number	SUPPL. 1
State	Published - Sep 2011

Keywords

Kalman filter (KF)
Maximum likelihood (ML)
Particle filter (PF)
Target tracking
Wireless sensor network (WSN)

Cite this

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title = "Application of an improved particle filter algorithm for target tracking in WSN",

abstract = "A novel PF algorithm was proposed. In this algorithm, the maximum likelihood (ML) was firstly used to estimate the latest position of mobile target, then the Kalman filter (KF) was used to get the more accurate state (their instantaneous locations and velocities) information and covariance of mobile target, which eventually provide a proposal distribution for PF. The simulation indicates that this improved PF has not only a higher tracking accuracy but also a better characteristic of real-time.",

keywords = "Kalman filter (KF), Maximum likelihood (ML), Particle filter (PF), Target tracking, Wireless sensor network (WSN)",

author = "Huang, \{Xiao Ping\} and Yan Wang and Chao Li and Shao, \{Jing Yu\}",

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month = sep,

language = "英语",

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journal = "Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology)",

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T1 - Application of an improved particle filter algorithm for target tracking in WSN

AU - Huang, Xiao Ping

AU - Wang, Yan

AU - Li, Chao

AU - Shao, Jing Yu

PY - 2011/9

Y1 - 2011/9

N2 - A novel PF algorithm was proposed. In this algorithm, the maximum likelihood (ML) was firstly used to estimate the latest position of mobile target, then the Kalman filter (KF) was used to get the more accurate state (their instantaneous locations and velocities) information and covariance of mobile target, which eventually provide a proposal distribution for PF. The simulation indicates that this improved PF has not only a higher tracking accuracy but also a better characteristic of real-time.

AB - A novel PF algorithm was proposed. In this algorithm, the maximum likelihood (ML) was firstly used to estimate the latest position of mobile target, then the Kalman filter (KF) was used to get the more accurate state (their instantaneous locations and velocities) information and covariance of mobile target, which eventually provide a proposal distribution for PF. The simulation indicates that this improved PF has not only a higher tracking accuracy but also a better characteristic of real-time.

KW - Kalman filter (KF)

KW - Maximum likelihood (ML)

KW - Particle filter (PF)

KW - Target tracking

KW - Wireless sensor network (WSN)

UR - https://www.scopus.com/pages/publications/82055171686

M3 - 文章

AN - SCOPUS:82055171686

SN - 1672-7207

VL - 42

SP - 895

EP - 899

JO - Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology)

JF - Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology)

IS - SUPPL. 1

ER -

Application of an improved particle filter algorithm for target tracking in WSN

Abstract

Keywords

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