2012Unpublished venueRequires access

Computational efficient DOA estimation algorithm based on MSWF and polynomial rooting

Hongbing Li, Jian Gong, Yiduo Guo

Open publisher page 2 citations

Abstract

A novel computational efficient direction of arrival (DOA) estimation algorithm using multi-stage Wiener filter (MSWF) and polynomial rooting is proposed. The signal subspace is obtained using MSWF, which avoid the known of desired signal, the estimation of data covariance matrix and its eigen-decomposition. Then, a new polynomial rooting based on the estimated signal subspace is adopted to realize DOA estimation. Therefore, comparing with the conventional DOA estimation algorithms, the proposed algorithm has smaller computational complexity. The correctness and effectiveness of the proposed method are verified by computer simulation results.

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What this paper is about

A novel computational efficient direction of arrival (DOA) estimation algorithm using multi-stage Wiener filter (MSWF) and polynomial rooting is proposed. The signal subspace is obtained using MSWF, which avoid the known of desired signal, the estimation of data covariance matrix and its eigen-decomposition. Then, a new polynomial rooting based on the estimated signal subspace is adopted to realize DOA estimation. Therefore, comparing with the conventional DOA estimation algorithms, the proposed algorithm has smaller computational complexity. The correctness and effectiveness of the proposed method are verified by computer simulation results.

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OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

A novel computational efficient direction of arrival (DOA) estimation algorithm using multi-stage Wiener filter (MSWF) and polynomial rooting is proposed. The signal subspace is obtained using MSWF, which avoid the known of desired signal, the estimation of data covariance matrix and its eigen-decomposition. Then, a new polynomial rooting based on the estimated signal subspace is adopted to realize DOA estimation. Therefore, comparing with the conventional DOA estimation algorithms, the proposed algorithm has smaller computational complexity. The correctness and effectiveness of the proposed method are verified by computer simulation results.

Key concepts: Computer science, Algorithm, Computational complexity theory, Estimation, Polynomial, Speech recognition, Mathematics, Engineering

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