Correntropy-based DOA estimation algorithm under impulsive noise environments

Abstract

In this paper, the direction of arrival (DOA) estimation of signals in the presence of impulsive noise environment is studied. Complex isotropic symmetric alpha-stable (SαS) random variables are modeled as impulsive noise, then a novel second-order statistic method that correntropy-based covariance matrix (CBCM) is defined, based on the combination of the CBCM of the array sensor outputs with the signal subspace technique (e.g., multiple signal classification (MUSIC)), which can be achieved source localization under impulsive noise environments. The Monte-Carlo simulation results illustrate the improved performance of CBCM-MUSIC for DOA estimation under a wide range of impulsive noise conditions.

A Novel Underwater Acoustic Signal Denoising Algorithm for Gaussian/Non-Gaussian Impulsive Noise

“…Therefore, it is essential to comprehensively investigate the UWA noise model. Several models have been proposed in the literature for the PDF of UWA noise [3], [7]- [11], [14]. The Gaussian mixture model is widely used to characterize the UWA noise.…”

“…However, it is unable to capture the heavier tail with a small number of Gaussian [3]. Compared with the Gaussian mixture model, the symmetric α-stable (SαS) distribution has "heavier tail" statistical characteristics of impulsive noise, which makes it consistent with the generation mechanism and the propagation conditions of the underwater impulsive noise [7]. Unfortunately, it has a limitation of not having a closedform distribution except for α = 1, 2 [3].…”