Video target tracking using strong tracking UKF

Home > Archive>Volume 39, Issue 10, 2016 >95-99

Video target tracking using strong tracking UKF
DOI:
                        
                    
CSTR:
                        [cstr]
                    
Author:
                        Yang Xinxin1Yang Xinxin
School of Physics Science and Electronic, Central South University, Changsha 410083, China;
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
Deng Lianwen1Deng Lianwen
School of Physics Science and Electronic, Central South University, Changsha 410083, China;
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
Chen Hongfei2Chen Hongfei
Hunan CSU Yeshine Science and Technology Development Limited Company, Changsha 410083, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
Song Defu3Song Defu
Microelectronic R&D Institute, ZTE Corporation, Shenzhen 518057, China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
He Chenggong1He Chenggong
School of Physics Science and Electronic, Central South University, Changsha 410083, China;
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site

                    
Affiliation:1.School of Physics Science and Electronic, Central South University, Changsha 410083, China; 2.Hunan CSU Yeshine Science and Technology Development Limited Company, Changsha 410083, China; 3.Microelectronic R&D Institute, ZTE Corporation, Shenzhen 518057, China
Clc Number:TP2
Fund Project:

Article

Figures

Metrics

Reference

Cited by

Materials

Comments

Abstract:

Aiming to the slow tracking and steadystate errors that exist when UKF(unscented Kalman filter) applied in nonlinear systems state estimation, an algorithm of video target tracking using strong tracking UKF was presented. Based on the unscented transformation, combined with the advantages of strong tracking filter and UKF filter, this algorithm adjusts Kalman gain with introducing timevarying fading factor in priori error covariance matrix to force the output residuals to maintain orthogonal, extract useful information in the residuals and improve the tracking capability to status change. The simulation results show that, moving target tracking in vedio utilizing strong tracking UKF has higher tracking accuracy and smaller MSE in state filtering.

Key words:target tracking; unscented Kalman filter; unscented transform(UT); strong tracking filter; nonlinear systems; video target

Get Citation

Copy

Article Metrics

Abstract:1131
PDF: 1496
HTML: 0
Cited by: 0

History

Received:
Revised:
Adopted:
Online: November 24,2016
Published:

Home

Introduction

Editorial Committee

Policy

Contact Us

中文版

Get Citation

Share

Article Metrics

History

Article QR Code