Home > Table of Contents


Proceedings of 2009 International Workshop on Information Security and Application (IWISA 2009)

Qingdao, China, November 21-22, 2009

Editors: Feng Gao and Xijun Zhu

AP Catalog Number: AP-PROC-CS-09CN004

ISBN: 978-952-5726-06-0

Page(s): 108-111

A Novel Multi-Passive-Sensor Target Tracking Algorithm Based On Gaussian Filter

Juan-li Liu, Hong-bing Ji, and Hui Guo

Full text: PDF


This paper presents a new multi-passive-sensor target tracking algorithm which yields a nonlinear state estimator called Gaussian filter based on deterministic sampling. Firstly, this state estimator employs a deterministic sample selection scheme, where a parametric density function representation of the sample points is employed to approximate the cumulative distribution function of the prior Gaussian density. The performance of the filter is more accurate than the extended Kalman Filter (EKF) and the unscented Kalman Filter (UKF) in nonlinear dynamic system. Secondly, in order to avoid the unobservability problem of passive target tracking, a nonlinear measurement model of multiple passive sensors is founded. Finally, the algorithm performance has been verified by illustrating some simulation results.

Index Terms

Gaussian Filter, Target Tracking, Multi-Passive-Sensor

Copyright @ 2009 ACADEMY PUBLISHER All rights reserved