Gaussian sum particle filtering

We use the Gaussian particle filter to build several types of Gaussian sum particle filters. These filters approximate the filtering and predictive distributions by weighted Gaussian mixtures and are basically banks of Gaussian particle filters. Then, we extend the use of Gaussian particle filters a...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on signal processing 2003-10, Vol.51 (10), p.2602-2612
Main Authors: Kotecha, J.H., Djuric, P.M.
Format: Article
Language:English
Subjects:
Additive noise
Algorithms
Applied sciences
Approximation
Bayesian methods
Decision support systems
Density
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Filtering
Filtration
Gaussian
Gaussian noise
Information, signal and communications theory
Monte Carlo methods
Noise
Non-Gaussian
Nonlinear equations
Particle filters
Signal and communications theory
Signal, noise
State-space methods
Stochastic systems
Telecommunications and information theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We use the Gaussian particle filter to build several types of Gaussian sum particle filters. These filters approximate the filtering and predictive distributions by weighted Gaussian mixtures and are basically banks of Gaussian particle filters. Then, we extend the use of Gaussian particle filters and Gaussian sum particle filters to dynamic state space (DSS) models with non-Gaussian noise. With non-Gaussian noise approximated by Gaussian mixtures, the non-Gaussian noise models are approximated by banks of Gaussian noise models, and Gaussian mixture filters are developed using algorithms developed for Gaussian noise DSS models. As a result, problems involving heavy-tailed densities can be conveniently addressed. Simulations are presented to exhibit the application of the framework developed herein, and the performance of the algorithms is examined.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2003.816754