Title :
A sequential Monte Carlo method for PHD approximation with conditionally linear/Gaussian models
Author_Institution :
Dept. of Electr. & Electron. Eng., Univ. of Melbourne, Parkville, VIC, Australia
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;
Conference_Titel :
Information Fusion (FUSION), 2010 13th Conference on
Conference_Location :
Edinburgh
Print_ISBN :
978-0-9824438-1-1
DOI :
10.1109/ICIF.2010.5711986
