A Gaussian mixture regression model based adaptive filter for non-Gaussian noise without a priori statistic

doi:10.1016/j.sigpro.2021.108314

	A Gaussian mixture regression model based adaptive filter for non-Gaussian noise without a priori statistic
	Cui, Haoran 2; Wang, Xiaoxu 2; Gao, Shuaihe 1,2; Li, Tiancheng 2
刊名	SIGNAL PROCESSING
	2022
卷号	190 页码:13
关键词	Nonlinear adaptive filter Unknown and non-Gaussian noises Variational Bayesian Gaussian mixture model
ISSN号	0165-1684
DOI	10.1016/j.sigpro.2021.108314
英文摘要	In many engineering systems, the distribution of measurement noise is unknown and non-Gaussian, such as skewed, multimodal, time-varying distributions and we can not obtain these prior statistics in advance. How to achieve state estimation via this kind of non-Gaussian measurement noises without prior statistic information is a challenging problem. In this paper, a novel Gaussian mixture regression model (GMRM) is proposed to model the unknown non-Gaussian measurement likelihood for Bayesian update to achieve nonlinear state estimation. Without any prior assumption or limitation of measurement noises' statistics and distributions, the GMRM can still describe the measurement likelihood accurately based on a group of parameters which are adjusted by maximizing the evidence lower bound. Based on the optimized GMRM, a new variational Bayesian Gaussian mixture filter is proposed by using the variational Bayesian approach. To eliminate the influence of the initialization of the introduced parameters, a learning scheme is proposed to adaptively optimize their hyper-parameters based on the historical measurements. Finally, simulation examples are employed to illustrate the effectiveness of the filter. (c) 2021 Elsevier B.V. All rights reserved.
资助项目	National Natural Science Foundation of China[61873208] ; National Natural Science Foundation of China[61573287] ; National Natural Science Foundation of China[61203234] ; National Natural Science Foundation of China[61135001] ; National Natural Science Foundation of China[61374023] ; Shaanxi Natural Science Foundation of China[2017JM6006] ; Aviation Science Foundation of China[2016ZC53018] ; Fundamental Research Funds for Central Universities[3102017jghk02009] ; National Key Research and Development Program of China[H8634001AA0406] ; Equipment Pre-research Foundation[2017-HT-XG]
WOS关键词	SYSTEM
WOS研究方向	Engineering
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000704396500008
资助机构	National Natural Science Foundation of China ; National Natural Science Foundation of China ; Shaanxi Natural Science Foundation of China ; Shaanxi Natural Science Foundation of China ; Aviation Science Foundation of China ; Aviation Science Foundation of China ; Fundamental Research Funds for Central Universities ; Fundamental Research Funds for Central Universities ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Equipment Pre-research Foundation ; Equipment Pre-research Foundation ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Shaanxi Natural Science Foundation of China ; Shaanxi Natural Science Foundation of China ; Aviation Science Foundation of China ; Aviation Science Foundation of China ; Fundamental Research Funds for Central Universities ; Fundamental Research Funds for Central Universities ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Equipment Pre-research Foundation ; Equipment Pre-research Foundation ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Shaanxi Natural Science Foundation of China ; Shaanxi Natural Science Foundation of China ; Aviation Science Foundation of China ; Aviation Science Foundation of China ; Fundamental Research Funds for Central Universities ; Fundamental Research Funds for Central Universities ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Equipment Pre-research Foundation ; Equipment Pre-research Foundation ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Shaanxi Natural Science Foundation of China ; Shaanxi Natural Science Foundation of China ; Aviation Science Foundation of China ; Aviation Science Foundation of China ; Fundamental Research Funds for Central Universities ; Fundamental Research Funds for Central Universities ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Equipment Pre-research Foundation ; Equipment Pre-research Foundation
内容类型	期刊论文
源URL	[http://210.72.145.45/handle/361003/10235]
专题	国家授时中心_量子频标研究室
通讯作者	Wang, Xiaoxu
作者单位	1.Chinese Acad Sci, Natl Time Serv Ctr, Beijing, Peoples R China 2.Northwestern Polytech Univ, Dept Automat, Xian 710072, Peoples R China
推荐引用方式 GB/T 7714	Cui, Haoran,Wang, Xiaoxu,Gao, Shuaihe,et al. A Gaussian mixture regression model based adaptive filter for non-Gaussian noise without a priori statistic[J]. SIGNAL PROCESSING,2022,190:13.
APA	Cui, Haoran,Wang, Xiaoxu,Gao, Shuaihe,&Li, Tiancheng.(2022).A Gaussian mixture regression model based adaptive filter for non-Gaussian noise without a priori statistic.SIGNAL PROCESSING,190,13.
MLA	Cui, Haoran,et al."A Gaussian mixture regression model based adaptive filter for non-Gaussian noise without a priori statistic".SIGNAL PROCESSING 190(2022):13.