A New Approach to Cost-Reference Particle Filtering

Author

Zhang, Zejie ; Bugallo, Monica F. ; Djuric, Petar M.

Author_Institution

Stony Brook Univ., Stony Brook

fYear

2007

fDate

4-7 Nov. 2007

Firstpage

711

Lastpage

714

Abstract

In this paper we propose a new cost-reference particle filter that exploits the concept of correlative learning. The objective of applying correlative learning is to obtain the centers of probability distributions that are used for particle generation. Such distributions should provide particles in regions of the state space that have low costs. The new cost-reference particle filter is compared to the original one through computer simulations of a target tracking system that uses range and bearings-only sensors, which are colocated.

Keywords

correlation methods; learning (artificial intelligence); particle filtering (numerical methods); statistical distributions; bearings-only sensor; correlative learning; cost-reference particle filtering; particle generation; probability distribution; range sensor; target tracking system; Additive noise; Costs; Filtering; Particle filters; Particle measurements; Probability distribution; Signal processing; State estimation; State-space methods; Stochastic processes; correlative learning; dynamic systems; particle filtering;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2007. ACSSC 2007. Conference Record of the Forty-First Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

978-1-4244-2109-1

Electronic_ISBN

1058-6393

Type

conf

DOI

10.1109/ACSSC.2007.4487307

Filename

4487307