The LFT based PHD filter for nonlinear jump Markov models in multi-target tracking

Author

Pasha, Syed Ahmed ; Tuan, Hoang Duong ; Apkarian, Pierre

Author_Institution

Sch. of Electr. Eng. & Telecommun., Univ. of New South Wales, Sydney, NSW, Australia

fYear

2009

fDate

15-18 Dec. 2009

Firstpage

5478

Lastpage

5483

Abstract

The probability hypothesis density (PHD) filter is a computationally viable solution for tracking an unknown, and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and miss-detection. This paper presents a PHD filter for a broad class of problems by accommodating targets that follow nonlinear jump Markov system (JMS) models. Our approach is based on the framework of the virtual linear fractional transformation (LFT) model which has shown great potential in single target filtering applications. Simulation results demonstrate that the proposed PHD filtering algorithm is robust for tracking multiple maneuvering targets.

Keywords

Markov processes; filtering theory; PHD filter; data association uncertainty; multitarget tracking; nonlinear jump Markov models; probability hypothesis density filter; single target filtering; virtual linear fractional transformation model; 1f noise; Closed-form solution; Filtering algorithms; Nonlinear filters; Random processes; Robustness; State estimation; Surveillance; Target tracking; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on

Conference_Location

Shanghai

ISSN

0191-2216

Print_ISBN

978-1-4244-3871-6

Electronic_ISBN

0191-2216

Type

conf

DOI

10.1109/CDC.2009.5400720

Filename

5400720