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.
Highlights
Array signal processing is an important branch in modern signal processing
We focused on the issue of direction of arrival (DOA) estimation algorithm in extremely high impulsive noise environments along with low generalized signal to noise ratio (GSNR) levels and fewer snapshots and introduced a new operator based on correntropy, namely, correntropy-based covariance matrices (CBCM), which can be combined with subspace algorithm, such as Multiple signal classification (MUSIC) algorithm, that is CBCMMUSIC algorithm
3 Proposed solution we proposed a new operator correntropy-based covariance matrix (CBCM), and it applied with MUSIC to estimating DOA in the presence of an impulsive noise environment
Summary
Array signal processing is an important branch in modern signal processing. It is widely used in the field of radar [1], sonar [2], 5G communication [3,4,5,6], and smart antenna [7]. In [16], the authors proposed a new operator referred to as the correntropy-based correlation (CRCO), and it can be applied with MUSIC algorithm; despite the CRCOMUSIC shows robustness in highly impulsive noise environments or in low generalized signal to noise ratio (GSNR) situation, the formulation for the robust CRCO statistics needs quite a number of snapshots. We focused on the issue of DOA estimation algorithm in extremely high impulsive noise environments along with low generalized signal to noise ratio (GSNR) levels and fewer snapshots and introduced a new operator based on correntropy, namely, correntropy-based covariance matrices (CBCM), which can be combined with subspace algorithm, such as MUSIC algorithm, that is CBCMMUSIC algorithm. (1) Consider the problem of DOA estimation in impulsive noise and proposes a new method to rebuild the covariance matrix based on correntropy.
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