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A Group-Sparsity-Based Hyperparameter-Free Framework for Wideband DOA Estimation
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DOI：10.1109/taes.2026.3715587.png)
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Abstract
En 中文
A group-sparsity-based (GS-based) underdetermined wideband direction-of-arrival (DOA) estimation framework, which is free of hyperparameters, is proposed to resolve more sources than the number of sensors. Based on the subband model via frequency decomposition, a direct wideband extension of the narrowband sparse iterative covariance estimation method is first presented, fusing subband results jointly. Then, in order to tackle the underdetermined DOA estimation problem with a uniform linear array, a GS-based wideband sparse iterative covariance estimation (GS-WSPICE) framework is proposed, where information acquired by subbands of interest is exploited simultaneously. Specifically, considering two a priori information on source spectrum under the proposed framework, the wideband cost function is reformulated to yield two improved objective functions and constraint structures, respectively. It significantly reduces the estimation parameters while fully exploiting the subband model's enhanced degrees of freedoms. Accordingly, two methods, i.e., GS-WSPICE (${{\mathcal {P}}_{\text{up}}}$) and GS-WSPICE (${{\mathcal {P}}_{\text{uf}}}$), are introduced as effective underdetermined solutions, where sparse arrays are not needed. The proposed methods are proved to be convex and can be expressed in semidefinite programming (SDP) form. Then, an efficient cyclic solution is developed, while a dynamic-dictionary-based hybrid cyclic and SDP solution is proposed to further reduce computational complexity. Numerical and experimental results validate the effectiveness of the proposed hyperparameter-free methods, where the time-consuming hyperparameter tuning process is no more required compared to existing wideband methods.
Keywords:
Direction of arrival (DOA)
hyperparameter-free
underdetermined
wideband
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