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A Novel MUSIC Algorithm for Direction-of-Arrival Estimation without the Estimate of Covariance Matrix and Its Eigendecomposition

Lei Huang, Shunjun Wu, Linrang Zhang

Open publisher page 7 citations

Abstract

A new MUSIC algorithm for direction-of-arrival (DOA) estimation is developed, based on the multi-stage Wiener filter (MSWF). Unlike the classical MUSIC algorithm, the proposed method only involves the forward recursions of the MSWF to find the noise subspace, even in the case of coherent signals, and does not require the estimate of an array covariance matrix or its eigendecomposition. Therefore, the proposed method is computationally advantageous over the classical MUSIC algorithm that resorts to computing the sample covariance matrix and its eigenvectors. The performance of the proposed method is demonstrated by numerical results.

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What this paper is about

A new MUSIC algorithm for direction-of-arrival (DOA) estimation is developed, based on the multi-stage Wiener filter (MSWF). Unlike the classical MUSIC algorithm, the proposed method only involves the forward recursions of the MSWF to find the noise subspace, even in the case of coherent signals, and does not require the estimate of an array covariance matrix or its eigendecomposition. Therefore, the proposed method is computationally advantageous over the classical MUSIC algorithm that resorts to computing the sample covariance matrix and its eigenvectors. The performance of the proposed method is demonstrated by numerical results.

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OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

A new MUSIC algorithm for direction-of-arrival (DOA) estimation is developed, based on the multi-stage Wiener filter (MSWF). Unlike the classical MUSIC algorithm, the proposed method only involves the forward recursions of the MSWF to find the noise subspace, even in the case of coherent signals, and does not require the estimate of an array covariance matrix or its eigendecomposition. Therefore, the proposed method is computationally advantageous over the classical MUSIC algorithm that resorts to computing the sample covariance matrix and its eigenvectors. The performance of the proposed method is demonstrated by numerical results.

Key concepts: Eigendecomposition of a matrix, Covariance matrix, Direction of arrival, Algorithm, Multiple signal classification, Wiener filter, Covariance, Eigenvalues and eigenvectors

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