Affiliation:
1. Taif University, College of Engineering, Dept. of Electrical Engineering, Al-Haweiah, Saudi Arabia
Abstract
The sample covariance matrix (SCM) is commonly used in direction-of-arrival
(DOA) estimation methods when the noise or observations are circular complex
Gaussian (C CG) distributed. However, with a very heavy-tailed non-Gaussian
noise model, the SCM-based DOA estimation methods fail to provide an
accurate estimate of DOA. This paper presents a numerical analysis of the
resolving capability of subspace-based circular (C) and non-circular (NC)
multiple signal classification (MUSIC) DOA estimation methods of arbitrarily
narrowband correlated signal sources corrupted by circular complex
elliptical symmetric (C CES) distributed noise. It evaluates the robustness
of these methods for correlated C and NC sources by employing the robust
complex M-estimators instead of SCM. It study also the effects of
correlation on robust MUSIC-based DOA estimation algorithms accuracy as a
function of the magnitude and phase of the correlation coefficients.
Simulations results show that the NC MUSIC algorithm which requires fewer
sensor elements yields robust estimates of DOA for correlated sources than
the C MUSIC algorithm using the M-estimators.
Publisher
National Library of Serbia
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Mechanical Engineering,Energy Engineering and Power Technology,Control and Systems Engineering
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