Underwater Acoustic Imaging via Online Bayesian Compressive Beamforming

Beamforming is extensively used in underwater acoustic imaging systems. As a high-resolution variety, a Bayesian compressive beamformer treats the acoustic echo of each snapshot independently and achieves enhanced recovery performance. However, its associated computational cost is extremely high compared with conventional beamformers, and its iterative implementation makes it difficult to be used online. To overcome these obstacles, an online Kalman filtering sparse Bayesian learning (online-KSBL) based compressive beamformer is proposed in this work, which is non-iterative and computationally efficient for a continual working scenario, where a long-term imaging task is conducted underwater. Imposing independent assumptions on snapshots, the online-SBL approach can be derived from online-KSBL. The hyperparameters of the model are estimated recursively, following efficient procedures that are implemented approximately with a sawtooth lag scheme. For the underwater imaging scenario, the correlation among snapshots is exploited and inferred from online-KSBL, and it is found that online-SBL performs competitively with online-KSBL, and it is sufficient to employ online-SBL without considering the snapshot correlation, retaining low complexity, as demonstrated by experimental results.