Estimation of time-varying unknown nongaussian noise with DPM

Bo Yang, Jian Ping Yuan, Jian Jun Luo, Xiao Kui Yue, Wei Hua Ma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Dirichlet process mixture (DPM) model, which is the state-of-the-art Bayesian nonparametric model, was introduced here to signal processing research field. In present Bayesian statistics it is used to model and inference random nongaussian distributions. We explored its ability to model and estimate nongaussian unknown stationary noise and our work will help dealing with problems in many fields of signal processing. Through some modifications, we also revealed its potential to model and estimate unknown nonstationary nongaussian noise. Sequential Monte Carlo based inference algorithm was developed to estimate time varying unknown nongaussian noise with DPM. Simulation results show the efficiency of our algorithm.

Original languageEnglish
Title of host publication2008 9th International Conference on Signal Processing, ICSP 2008
Pages272-275
Number of pages4
DOIs
StatePublished - 2008
Event2008 9th International Conference on Signal Processing, ICSP 2008 - Beijing, China
Duration: 26 Oct 200829 Oct 2008

Publication series

NameInternational Conference on Signal Processing Proceedings, ICSP

Conference

Conference2008 9th International Conference on Signal Processing, ICSP 2008
Country/TerritoryChina
CityBeijing
Period26/10/0829/10/08

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