Robust Frequency Estimation Under Additive Symmetric α-Stable Gaussian Mixture Noise

作     者：Peng Wang Yulu Tian Bolong Men Hailong Song 

作者机构：Beijing Orient Institute of Measurement and TestBeijing10083China University of Science&Technology BeijingBeijing10083China 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第36卷第4期

页      面：83-95页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Key R&D Program of China(Grant No.2018YFF01012600) National Natural Science Foundation of China(Grant No.61701021) Fundamental Research Funds for the Central Universities(Grant No.FRF-TP-19-006A3) 

主　　题：Additive symmetricα-stable Gaussian mixture metropolis-hastings algorithm robust frequency estimation probability density function approximation 

摘      要：Here the estimating problem of a single sinusoidal signal in the additive symmetricα-stable Gaussian(ASαSG)noise is *** ASαSG noise here is expressed as the additive of a Gaussian noise and a symmetricα-stable distributed *** the probability density function(PDF)of the ASαSG is complicated,traditional estimators cannot provide optimum *** on the Metropolis-Hastings(M-H)sampling scheme,a robust frequency estimator is proposed for ASαSG ***,to accelerate the convergence rate of the developed algorithm,a new criterion of reconstructing the proposal covar-iance is derived,whose main idea is updating the proposal variance using several previous samples drawn in each *** approximation PDF of the ASαSG noise,which is referred to the weighted sum of a Voigt function and a Gaussian PDF,is also employed to reduce the computational *** computer simulations show that the performance of our method is better than the maximum likelihood and the lp-norm estimators.