A New Active Sonar Detector Based on Beamformed Deep Neural Network

Abstract

This article proposes a new active sonar detector based on a beamformed deep neural network (BDNN) in three steps. The process involves a preprocessing step, a deep neural network (DNN) application step, and a subsequent postprocessing step. In the preprocessing step, partial spectra are extracted from multiple directions through frequency-domain beamforming. These partial spectra from different directions serve as DNN input, yielding estimated target probabilities as output in the DNN application step. In the postprocessing step, a multiframe probability multiplication technique is proposed, and the number of frames is determined adaptively. The proposed BDNN generates a gridded azimuth-distance graph, where each grid cell represents the probability of a target's presence at a specific azimuth and distance. To guarantee real-time application, we also propose a graphics processing unit based parallel acceleration method, which increases the computation speed of the beamforming process by nearly two orders of magnitude compared to the CPU. The proposed BDNN is verified through sea and lake trials. The results demonstrate that the proposed BDNN achieves better detection performance compared to the conventional matched filter method and exhibits remarkable generalization capabilities.