Work

Multi-layer residual beamforming for underwater acoustic source bearing estimation

Year: 2026

Type: article

Abstract: Deconvolution beamforming maintains robustness against array mismatch and limited snapshots in underwater source localization, yet its resolution and accuracy require improvement. Inspired by deep learning architectures, this work proposes a multi-layer residual beamforming framework that processes array snapshots through 2 K layers of alternating phase compensation and source cancellation to estimate directions of arrival for K sources. Simulations validate the proposed method's robustness to array mismatch and its capacity for accurate source localization under challenging conditions, achieving estimation accuracy comparable to LASSO (i.e., the least absolute shrinkage and selection operator) while outperforming deconvolution beamforming. Experimental validation using the SWellEx-96 dataset confirms the method's practical feasibility.

Source: JASA Express Letters

DOI: 10.1121/10.0043201

Authors: Yong Chen, Fang Wang

Institution: Jiangxi Normal University

Language: English

Topic: Direction-of-Arrival Estimation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Natural Science Foundation of China

Award: G8806501969