Performance Analysis of the MUSIC Algorithm in the Presence of Correlated Noise

• Published in: IEEE access Vol. 13; pp. 91957 - 91971
• Main Authors: Ham, Hyeong Woo, Kwon, Hyuk-In, Lee, Joon-Ho
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analytic expression; Antennas; Classification algorithms; correlated noise; Correlation; Covariance matrices; Direction finding; Direction of arrival; direction-of-arrival (DOA); Direction-of-arrival estimation; Estimation; Exact solutions; means-square-error (MSE); Monte Carlo simulation; Multiple signal classification; multiple signal classification (MUSIC); Noise; Performance analysis; Performance prediction; Sensors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2025.3569878

Passive direction finding has been studied over the past decades and theoretic performance prediction is a fundamental problem. The performance of the MUSIC algorithm for Direction-of-arrival (DOA) estimation under measurement uncertainty is analyzed. In the conventional MUSIC algorithm performance analysis, noise is assumed to be uncorrelated. In this paper, the noise correlation between antenna elements is considered according to the distance between antenna elements. The Mean Squared Error (MSE) obtained through Monte-Carlo simulations is compared with the MSE derived from analytical expressions. The primary contribution of this paper is to reduce the computational complexity of the performance analysis of the MUSIC algorithm compared to the existing Monte-Carlo simulation-based performance analysis. To our knowledge, no previous studies have derived a closed-form analytical performance analysis of the algorithm in the presence of correlated noise.