A sequential Monte Carlo method for PHD approximation with conditionally linear/Gaussian models

Author

Morelande, M.

Author_Institution

Dept. of Electr. & Electron. Eng., Univ. of Melbourne, Parkville, VIC, Australia

fYear

2010

fDate

26-29 July 2010

Firstpage

1

Lastpage

7

Abstract

A new sequential Monte Carlo procedure for approximating the probability hypothesis density is proposed. The algorithm, based on the replacement of numerical approximation with exact computation, is applicable to the class of conditionally linear/Gaussian models. The proposed algorithm is applied with an efficient, measurement-directed importance density to multiple target tracking using range-bearings measurements. Performance for a given sample size is significantly better than the previously proposed SMC-PHD.

Keywords

Gaussian processes; Monte Carlo methods; approximation theory; filtering theory; sequential estimation; target tracking; PHD approximation; conditionally linear/Gaussian model; exact computation; measurement-directed importance density; multiple target tracking; numerical approximation; probability hypothesis density; range-bearing measurement; sequential Monte Carlo method; sequential Monte Carlo procedure; Approximation algorithms; Approximation methods; Computational modeling; Covariance matrix; Monte Carlo methods; Surveillance; Target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion (FUSION), 2010 13th Conference on

Conference_Location

Edinburgh

Print_ISBN

978-0-9824438-1-1

Type

conf

DOI

10.1109/ICIF.2010.5711986

Filename

5711986