Eigen Vector Association Method in Compressed Domain for Self-Noise Cancellation in Underwater Acoustics

Pawan Kumar,Karan Nathwani,Vinayak Abrol, Suresh Kumar Natarajan

OCEANS 2023 - Limerick(2023)

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摘要
This work considers self-noise cancellation on the towed array from the ship, whose higher power masks weak targets. We present two methods. The first method estimates the noise subspace of the self-noise data. Associated eigenvectors to self-noise are identified with the help of the correlation coefficient between the subspace of the noisy data and the self-noise data. The second method employs the correlation coefficient approach in the compressive domain. Here, target retrieval and self-noise suppression are performed in the compressive domain. Thus, the second method has low computational complexity compared to the state-of-the-art method. Experiments demonstrate convincingly that the proposed method is robust even at significantly lower signal-to-interference-noise ratios (SINR).
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关键词
Compressed Sensing, Self-Noise Cancellation, Multiple Target detection, Eigencomponent Association
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