Sonar Dereverberation via Independent Component Analysis and Deep Learning

Sonar images are often affected by reverberation, posing challenges to detecting and recognizing target signals. This paper combines the strong interpretability and controllability of signal processing model with the powerful data mining capability of deep learning technology to propose a novel sonar image dereverberation method. Initially, an unsupervised deep learning, guided by domain knowledge from Independent Component Analysis (ICA), is employed as the first-level network. The first-level called ICANET, enables end-to-end dereverberation without labeled data. Subsequently, the supervised deep learning network called SKRNET (Supervised Knowledge-based Reverberation Network), further enhances performance by incorporating labeled data, along with knowledge obtained from ICANET. Sea trial experiments demonstrate that compared to benchmark methods, SKRNET achieves the highest signal-to-reverberation ratios (SRR) at 35.76 and entropy at 7.39, resulting in improvements of 20.2% and 4.8% over the raw data, respectively. Simulation experiments also confirm the superior signal restoration capability of the proposed method.