Adaptive Bayesian signal processing - a sequential Monte Carlo paradigm

Xiaodong Wang, Rong Chen, Jun S. Liu

Research output: Contribution to conferencePaper

Abstract

We provide a general framework for using Monte Carlo methods in dynamic systems and discuss its wide applications in adaptive signal processing. All of these methods are partial combinations of three ingredients: important 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.

Original languageEnglish (US)
Pages239-242
Number of pages4
StatePublished - Jan 1 2000
Externally publishedYes
EventProceedings of the 10th IEEE Workshop on Statiscal and Array Processing - Pennsylvania, PA, USA
Duration: Aug 14 2000Aug 16 2000

Other

OtherProceedings of the 10th IEEE Workshop on Statiscal and Array Processing
CityPennsylvania, PA, USA
Period8/14/008/16/00

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'Adaptive Bayesian signal processing - a sequential Monte Carlo paradigm'. Together they form a unique fingerprint.

  • Cite this

    Wang, X., Chen, R., & Liu, J. S. (2000). Adaptive Bayesian signal processing - a sequential Monte Carlo paradigm. 239-242. Paper presented at Proceedings of the 10th IEEE Workshop on Statiscal and Array Processing, Pennsylvania, PA, USA, .