请帮忙查询EI检索号,Trackingapplicationaboutsingermodelbasedonmarginalizedparticlefilter请帮忙查询EI检索号,TrackingapplicationaboutsingermodelbasedonmarginalizedparticlefilterZHOUFei,HEWei-jun,FANXin-yue

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请帮忙查询EI检索号,Tracking application about singer model based on marginalized particle filter
请帮忙查询EI检索号,Tracking application about singer model based on marginalized particle filter
ZHOU Fei,HE Wei-jun,FAN Xin-yue

Answer 1

Accession number:20103813241917
Title:Tracking application about singer model based on marginalized particle filter
Authors:Zhou,Fei1 ; He,Wei-Jun1 ; Fan,Xin-Yue1
Author affiliation:1 Institute of Wireless Location and Space Measurement,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
Corresponding author:Zhou,F.([e-mail])
Source title:Journal of China Universities of Posts and Telecommunications
Abbreviated source title:J.China Univ.Post Telecom.
Volume:17
Issue:4
Issue date:2010
Publication year:2010
Pages:47-51+124
Language:English
ISSN:10058885
CODEN:JCUPCO
Document type:Journal article (JA)
Publisher:Editorial Deparment,P.O.Box 231,10 Xi Tucheng Road,Beijing,100876,China
Abstract:This article deals with the problem of maneuvering target tracking which results in a mixed linear/non-linear model estimation problem.For maneuvering tracking system,extended Kalman filter (EKF) or particle filter (PF) is traditionally used to estimate the states.In this article,marginalized particle filter (MPF) is presented for application in a mixed linear/non-linear model estimation problem.MPF is a combination of Kalman filter (KF) and PF.So it holds both advantage of them and can be used for mixed linear/non-linear substructure,where the conditionally linear states are estimated using KF and the nonlinear states are estimated using PF.Simulation results show that MPF guarantees the estimation accuracy and alleviates the potential computational burden problem compared with PF and EKF in maneuvering target tracking application.© 2010 The Journal of China Universities of Posts and Telecommunications.
Number of references:12
Main heading:Target tracking
Controlled terms:Estimation - Kalman filters - Nonlinear filtering
Uncontrolled terms:Computational burden - Linear model - Maneuvering target tracking - Maneuvering tracking - Marginalized particle filters - Model-based - Nonlinear state - Particle filter - Simulation result - Tracking application
Classification code:716.2 Radar Systems and Equipment - 731.1 Control Systems - 921 Mathematics
DOI:10.1016/S1005-8885(09)60486-6
Database:Compendex
Compilation and indexing terms,© 2010 Elsevier Inc.