Open Access

ARTICLE

Robust Frequency Estimation Under Additive Mixture Noise

Yuan Chen¹, Yulu Tian¹, Dingfan Zhang², Longting Huang^3,*, Jingxin Xu⁴

1 School of Computer and Communication Engineering, University of Science & Technology Beijing, Beijing, 100083, China
2 School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
3 School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China
4 Department of Energy and Public Works, Queensland, 4702, Australia

* Corresponding Author: Longting Huang. Email:

Computers, Materials & Continua 2022, 72(1), 1671-1684. https://doi.org/10.32604/cmc.2022.022371

Received 05 August 2021; Accepted 01 December 2021; Issue published 24 February 2022

Abstract

In many applications such as multiuser radar communications and astrophysical imaging processing, the encountered noise is usually described by the finite sum of -stable variables. In this paper, a new parameter estimator is developed, in the presence of this new heavy-tailed noise. Since the closed-form PDF of the -stable variable does not exist except and , we take the sum of the Cauchy () and Gaussian () noise as an example, namely, additive Cauchy-Gaussian (ACG) noise. The probability density function (PDF) of the mixed random variable, can be calculated by the convolution of the Cauchy's PDF and Gaussian's PDF. Because of the complicated integral in the PDF expression of the ACG noise, traditional estimators, e.g., maximum likelihood, are analytically not tractable. To obtain the optimal estimates, a new robust frequency estimator is devised by employing the Metropolis-Hastings (M-H) algorithm. Meanwhile, to guarantee the fast convergence of the M-H chain, a new proposal covariance criterion is also devised, where the batch of previous samples are utilized to iteratively update the proposal covariance in each sampling process. Computer simulations are carried out to indicate the superiority of the developed scheme, when compared with several conventional estimators and the Cramér-Rao lower bound.

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Keywords

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