Estimation of time-varying unknown nongaussian noise with DPM

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

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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.

源语言英语
主期刊名2008 9th International Conference on Signal Processing, ICSP 2008
272-275
页数4
DOI
出版状态已出版 - 2008
活动2008 9th International Conference on Signal Processing, ICSP 2008 - Beijing, 中国
期限: 26 10月 200829 10月 2008

出版系列

姓名International Conference on Signal Processing Proceedings, ICSP

会议

会议2008 9th International Conference on Signal Processing, ICSP 2008
国家/地区中国
Beijing
时期26/10/0829/10/08

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