Direction-of-arrival estimation for weak underwater targets via flexible sparsity-aware modeling

Direction-of-arrival (DOA) estimation plays a critical role in underwater acoustic applications such as target detection, localization, tracking, and identification. Unlike image or speech processing, underwater array signal processing faces unique challenges because of low signal-to-noise ratio (SNR) and limited snapshots. In such conditions, whether traditional methods (e.g., conventional beamforming, multiple signal classification) or learning-based approaches (e.g., sparse Bayesian learning) suffer from a degradation in accuracy and resolution of DOA estimation. To improve performance at low SNR and limited observations, we propose a Bayesian probabilistic model leveraging the flexible sparsity-promoting properties of the generalized hyperbolic distribution. Within this framework, the closed-form inference updates are derived using the variational inference technique. Furthermore, to mitigate grid mismatch, we introduce a modified marginal likelihood computation, based on which an efficient adaptive approach is developed for refined DOA estimation. Numerical results, including simulation and SWellEx-96 Experiment data, validate the superior performance of the proposed method, especially in the low SNR and limited snapshots regime.