PRWSF-ABSBL: a fast direction of arrival estimation method based on sparse Bayesian learning and partial relaxation approach

Fenting Liu; Xiang Pan; Junxiong Wang

doi:10.1117/12.3032946

5 July 2024 PRWSF-ABSBL: a fast direction of arrival estimation method based on sparse Bayesian learning and partial relaxation approach

Fenting Liu, Xiang Pan, Junxiong Wang

Author Affiliations +

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131846G (2024) https://doi.org/10.1117/12.3032946
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

This paper addresses the issue of slow convergence in direction of arrival (DOA) estimation algorithms based on sparse Bayesian learning (SBL). An innovative approach is proposed, termed PRWSF-ABSBL, which combines the Partial Relaxation (PR) method with SBL. This algorithm initializes the hyperparameters of the adaptive bow SBL (ABSBL) algorithm using the spatial spectrum and noise variance estimated by the Partially-Relaxed Weighted Subspace Fitting (PR-WSF) algorithm, enabling ABSBL to improve speed of convergence after a significantly reduced number of iterations. The numerical simulations and experimental data processing results have shown that the PRWSF-ABSBL demonstrates superior performance in the detection of weak targets and enhancement of discrimination between port and starboard sides during turns. Furthermore, its computational efficiency significantly outperforms other SBL-based algorithms.

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

Citation Download Citation

Fenting Liu, Xiang Pan, and Junxiong Wang "PRWSF-ABSBL: a fast direction of arrival estimation method based on sparse Bayesian learning and partial relaxation approach", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131846G (5 July 2024); https://doi.org/10.1117/12.3032946

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