Adaptive Tow Ship Noise Cancellation Using Deep Regression Neural Network

M. Remadevi; Gilu K Abraham; R. Rajesh; N. Sureshkumar

doi:10.3849/aimt.01522

Authors

M. Remadevi Scientist G, Naval Physical and Oceanographic Laboratory, DRDO, Kochi, Kerala, India
Gilu K Abraham Naval Physical and Oceanographic Laboratory, DRDO and Research Scholar affiliated to CUSAT, Kochi, Kerala, India
R. Rajesh Naval Physical and Oceanographic Laboratory, DRDO and Research Scholar affiliated to CUSAT, Kochi, Kerala, India
N. Sureshkumar Naval Physical and Oceanographic Laboratory, DRDO and Research Scholar affiliated to CUSAT, Kochi, Kerala, India

DOI:

https://doi.org/10.3849/aimt.01522

Keywords:

deep regression neural network, towed sensor array, tow ship noise cancellation, weight initialization

Abstract

This paper investigates the problem of cancellation of noise generated by own platform in shallow water scenario. In the case of underwater acoustics, the target signal detection and tracking in the presence of tow ship noise is a challenging task. A computationally intensive technique is necessary for tow ship noise suppression. In this paper, an algorithm using deep regression neural network (DRNN) along with minimum variance distortionless response (MVDR) beamformer is presented for tow ship noise cancellation. Nine DRNN’s each with different weight initialization techniques and activation functions are designed for effective tow ship noise cancellation. The designed DRNNs is tested using the simulated data and further validated using the real data collected during the trials from Arabian Sea.

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