Frequency-difference sparse Bayesian learning for unambiguous direction-of-arrival estimation

• DOI: 10.1121/10.0036752
• Publication Date: 2025-05-13T14:44:13Z
• Authors: Ze Yuan; Haiqiang Niu; Zhenglin Li; Wenyu Luo
• Keywords: Hyperparameter; Spurious relationship; Aliasing; Matrix (chemical analysis); Direction of arrival

The frequency-difference (FD) method uses the FD Hadamard product, comprising auto-products to model below-band acoustic fields and unintended cross-products, for efficient direction-of-arrival (DOA) estimation under spatial aliasing. Despite improved resolution from compressive sensing, spurious peaks arise as a result of cross-products lacking counterparts in sensing matrix. proposed addresses this by reconstructing matrix with full product applying sparse Bayesian learning estimate two-dimensional hyperparameter matrix, extracting its diagonal suppress DOAs. Simulations show that it outperforms previous methods detecting weak targets, where advantages increase source numbers grow.