A joint sparse Bayesian learning for wideband DOA estimation

Yonghong Zhao; Jisong Liu; Junlong Wang; Shuxin Dong

doi:10.1117/12.3037518

4 December 2024 A joint sparse Bayesian learning for wideband DOA estimation

Yonghong Zhao, Jisong Liu, Junlong Wang, Shuxin Dong

Author Affiliations +

Proceedings Volume 13283, Conference on Spectral Technology and Applications (CSTA 2024); 1328352 (2024) https://doi.org/10.1117/12.3037518
Event: Conference on Spectral Technology and Applications (CSTA 2024), 2024, Dalian, China

Abstract

Wideband direction of arrival (DOA) estimation using sensor array is a noteworthy problem frequently occurring in many applications involving radar, sonar, and communication. We present a wideband DOA method based on a sparse Bayesian learning of multiple sub-bands jointly with the redundant dictionary matrix at the reference frequency. Explicitly enforcing the focusing operation and the singular value decomposition (SVD) to sensor measurements is motivated by a desire to decrease the computational complexity and storage capacity. A joint sparse Bayesian learning framework is proposed to deal with multiple sub-bands under one segment to obtain the DOA information. Moreover, the proposed method can be directly extended to multiple segments. Comparing to other wideband DOA estimation methods, the proposed method has the advantages of increased resolution, higher accuracy, and correlation of the sources, as well as not requiring the initial angles for the focusing matrix. Simulations are provided to show the effectiveness and improved performance of the proposed method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yonghong Zhao, Jisong Liu, Junlong Wang, and Shuxin Dong "A joint sparse Bayesian learning for wideband DOA estimation", Proc. SPIE 13283, Conference on Spectral Technology and Applications (CSTA 2024), 1328352 (4 December 2024); https://doi.org/10.1117/12.3037518

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