Open Access

ARTICLE

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

Peng Wang¹, Yulu Tian², Bolong Men^1,*, Hailong Song¹

1 Beijing Orient Institute of Measurement and Test, Beijing, 10083, China
2 University of Science & Technology Beijing, Beijing, 10083, China

* Corresponding Author: Bolong Men. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 83-95. https://doi.org/10.32604/iasc.2023.027602

Received 22 January 2022; Accepted 19 April 2022; Issue published 29 September 2022

Abstract

Here the estimating problem of a single sinusoidal signal in the additive symmetric α-stable Gaussian (ASαSG) noise is investigated. The ASαSG noise here is expressed as the additive of a Gaussian noise and a symmetric α-stable distributed variable. As the probability density function (PDF) of the ASαSG is complicated, traditional estimators cannot provide optimum estimates. Based on the Metropolis-Hastings (M-H) sampling scheme, a robust frequency estimator is proposed for ASαSG noise. Moreover, to accelerate the convergence rate of the developed algorithm, a new criterion of reconstructing the proposal covariance is derived, whose main idea is updating the proposal variance using several previous samples drawn in each iteration. The 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 complexity. The computer simulations show that the performance of our method is better than the maximum likelihood and the -norm estimators.

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Keywords

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