TDOA/FDOA based target tracking with imperfect position and velocity data of distributed moving sensors

Seul Ki Han, Won Sang Ra, Jin Bae Park

Research output: Contribution to journalArticle

Abstract

This paper addresses the target tracking problem using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measured by moving sensor network whose position and velocity are noise contaminated. It is a known fact that the existing approaches to this problem still have two unsolved technical issues; the unsatisfactory convergence behavior of the tracking filter mainly caused by severe nonlinearity of the problem itself and the tracking performance degradation due to the sensor position and velocity errors. In order to resolve these matters radically, the given target tracking problem is formulated as the robust state estimation problem of the linear system with stochastic uncertainties in its measurement matrix and solved by using the robust Kalman filter theory. The proposed scheme enables us to overcome the inherent limitations of the conventional nonlinear filters for its linear filter structure. It can also prevent the performance degradation due to imperfect sensor position and velocity information. Through the simulations, the effectiveness and reliable target tracking performance of the proposed method are demonstrated.

Original languageEnglish
Pages (from-to)1155-1166
Number of pages12
JournalInternational Journal of Control, Automation and Systems
Volume15
Issue number3
DOIs
Publication statusPublished - 2017 Jun 1

Fingerprint

Target tracking
Sensors
Degradation
State estimation
Kalman filters
Sensor networks
Linear systems
Time difference of arrival

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications

Cite this

@article{0146a9371c434212b22ba88ef9150840,
title = "TDOA/FDOA based target tracking with imperfect position and velocity data of distributed moving sensors",
abstract = "This paper addresses the target tracking problem using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measured by moving sensor network whose position and velocity are noise contaminated. It is a known fact that the existing approaches to this problem still have two unsolved technical issues; the unsatisfactory convergence behavior of the tracking filter mainly caused by severe nonlinearity of the problem itself and the tracking performance degradation due to the sensor position and velocity errors. In order to resolve these matters radically, the given target tracking problem is formulated as the robust state estimation problem of the linear system with stochastic uncertainties in its measurement matrix and solved by using the robust Kalman filter theory. The proposed scheme enables us to overcome the inherent limitations of the conventional nonlinear filters for its linear filter structure. It can also prevent the performance degradation due to imperfect sensor position and velocity information. Through the simulations, the effectiveness and reliable target tracking performance of the proposed method are demonstrated.",
author = "Han, {Seul Ki} and Ra, {Won Sang} and Park, {Jin Bae}",
year = "2017",
month = "6",
day = "1",
doi = "10.1007/s12555-015-0419-y",
language = "English",
volume = "15",
pages = "1155--1166",
journal = "International Journal of Control, Automation and Systems",
issn = "1598-6446",
publisher = "Institute of Control, Robotics and Systems",
number = "3",

}

TDOA/FDOA based target tracking with imperfect position and velocity data of distributed moving sensors. / Han, Seul Ki; Ra, Won Sang; Park, Jin Bae.

In: International Journal of Control, Automation and Systems, Vol. 15, No. 3, 01.06.2017, p. 1155-1166.

Research output: Contribution to journalArticle

TY - JOUR

T1 - TDOA/FDOA based target tracking with imperfect position and velocity data of distributed moving sensors

AU - Han, Seul Ki

AU - Ra, Won Sang

AU - Park, Jin Bae

PY - 2017/6/1

Y1 - 2017/6/1

N2 - This paper addresses the target tracking problem using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measured by moving sensor network whose position and velocity are noise contaminated. It is a known fact that the existing approaches to this problem still have two unsolved technical issues; the unsatisfactory convergence behavior of the tracking filter mainly caused by severe nonlinearity of the problem itself and the tracking performance degradation due to the sensor position and velocity errors. In order to resolve these matters radically, the given target tracking problem is formulated as the robust state estimation problem of the linear system with stochastic uncertainties in its measurement matrix and solved by using the robust Kalman filter theory. The proposed scheme enables us to overcome the inherent limitations of the conventional nonlinear filters for its linear filter structure. It can also prevent the performance degradation due to imperfect sensor position and velocity information. Through the simulations, the effectiveness and reliable target tracking performance of the proposed method are demonstrated.

AB - This paper addresses the target tracking problem using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measured by moving sensor network whose position and velocity are noise contaminated. It is a known fact that the existing approaches to this problem still have two unsolved technical issues; the unsatisfactory convergence behavior of the tracking filter mainly caused by severe nonlinearity of the problem itself and the tracking performance degradation due to the sensor position and velocity errors. In order to resolve these matters radically, the given target tracking problem is formulated as the robust state estimation problem of the linear system with stochastic uncertainties in its measurement matrix and solved by using the robust Kalman filter theory. The proposed scheme enables us to overcome the inherent limitations of the conventional nonlinear filters for its linear filter structure. It can also prevent the performance degradation due to imperfect sensor position and velocity information. Through the simulations, the effectiveness and reliable target tracking performance of the proposed method are demonstrated.

UR - http://www.scopus.com/inward/record.url?scp=85019929269&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85019929269&partnerID=8YFLogxK

U2 - 10.1007/s12555-015-0419-y

DO - 10.1007/s12555-015-0419-y

M3 - Article

VL - 15

SP - 1155

EP - 1166

JO - International Journal of Control, Automation and Systems

JF - International Journal of Control, Automation and Systems

SN - 1598-6446

IS - 3

ER -