SSP'05 -- Proceedings

	IEEE/SP 13th workshop on Statistical Signal Processing July, 17-20, 2005 - Bordeaux - France

Information regarding the paper

Title

Particle Filtering with Alpha-Stable Distributions

Author(s)

Lyudmila Mihaylova	Bristol University, UK
Paul Brasnett	Bristol University, UK
Alin Achim	Bristol University, UK
Nishan Canagarajah	Bristol University, UK
David Bull	Bristol University, UK

Get the paper in PDF format

To obtain Acrobat Reader (version 5 minimum required) necessary to his read.

Abstract

In this paper we introduce a novel sequential Monte Carlo technique, which is based on the family of symmetric alpha-stable (SAS) distributions. Sequential Bayesian estimation generally involves recursive estimation of filtering and predictive distributions of unobserved signals from their noisy measurements. In our proposed algorithm, the relevant density functions are approximated by particles drawn from stable distributions. We prefer to call this novel technique SAS particle filtering (SASPF). We assess the performance of the SASPF in comparison with the Gaussian Sum Particle filter (GSPF) and a standard (non-parametric) particle filter (PF). Results obtained using highly nonlinear models with simulated data show that the SASPF outperforms the GSPF and compares very favorably with the PF.