Adaptive Bayesian signal processing - a sequential Monte Carlo paradigm

Author

Wang, Xiaodong ; Chen, Rong ; Liu, Jun S.

Author_Institution

Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA

fYear

2000

fDate

2000

Firstpage

239

Lastpage

242

Abstract

We provide a general framework for using Monte Carlo methods in dynamic systems and discuss its wide application in adaptive signal processing. All of these methods are partial combinations of three ingredients: importance sampling and resampling, rejection sampling and Markov chain iterations. Examples from target tracking and digital communication applications are provided to demonstrate the effectiveness of these novel statistical signal processing techniques

Keywords

Bayes methods; Markov processes; adaptive signal processing; digital communication; importance sampling; iterative methods; radar signal processing; signal sampling; target tracking; Bayesian signal processing; Markov chain iterations; adaptive signal processing; digital communication; dynamic systems; importance resampling; importance sampling; rejection sampling; sequential Monte Carlo methods; statistical signal processing; target tracking; Adaptive signal processing; Bayesian methods; Equations; Filtering; Inference algorithms; Monte Carlo methods; Signal sampling; Statistics; Target tracking; Yttrium;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal and Array Processing, 2000. Proceedings of the Tenth IEEE Workshop on

Conference_Location

Pocono Manor, PA

Print_ISBN

0-7803-5988-7

Type

conf

DOI

10.1109/SSAP.2000.870119

Filename

870119