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Enhanced Deconvolved Beamforming via Adaptive Subspace Separation to Improve Underwater Target Detection
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DOI：10.1109/JSEN.2026.3692678.png)
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Abstract
En 中文
Using sonar for underwater target detection holds importance for underwater sensing. In underwater target-detection scenarios, conventional beamforming (CBF) is commonly used to estimate the direction of arrival (DOA) of targets. Deconvolved CBF (DCBF) further enhances CBF’s angular resolution by deconvolution. However, DCBF suffers from severe resolution degradation at low signal-to-noise ratios (SNRs) due to insufficient output gain during beamforming in the element domain. To address this issue, this article proposes an adaptive subspace separation DCBF (ASS-DCBF) to enhance DCBF performance under low SNRs. First, an optimized source number estimation is employed to adaptively partition the signal and noise subspaces, thereby enhancing the accuracy of subspace separation under low SNRs. Second, the CBF is performed in the signal subspace domain rather than the element domain, consequently improving the input SNR of the CBF. Finally, deconvolution is applied to further enhance the output resolution of the subspace domain CBF. Theoretical analysis and numerical simulations validate the effectiveness of the proposed algorithm in maintaining resolution capabilities under even lower SNRs and snapshots. Furthermore, water tank experiments provide additional empirical validation of the algorithm’s practical efficacy.
Keywords:
Adaptive subspace separation
deconvolved conventional beamforming (DCBF)
underwater target detection
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