Underwater Pulse Waveform Recognition Based on Hash Aggregate Discriminant Network

Underwater pulse waveform recognition is an important method for underwater object detection. Most existing works focus on the application of traditional pattern recognition methods, which ignore the time- and space-varying characteristics in sound propagation channels and cannot easily extract valuable waveform features. Sound propagation channels in seawater are time- and space-varying convolutional channels. In the extraction of the waveform features of underwater acoustic signals, the effect of high-accuracy underwater acoustic signal recognition is identified by eliminating the influence of time- and space-varying convolutional channels to the greatest extent possible. We propose a hash aggregate discriminative network (HADN), which combines hash learning and deep learning to minimize the time- and space-varying effects on convolutional channels and adaptively learns effective underwater waveform features to achieve high-accuracy underwater pulse waveform recognition. In the extraction of the hash features of acoustic signals, a discrete constraint between clusters within a hash feature class is introduced. This constraint can ensure that the influence of convolutional channels on hash features is minimized. In addition, we design a new loss function called aggregate discriminative loss (AD-loss). The use of AD-loss and softmax-loss can increase the discriminativeness of the learned hash features. Experimental results show that on pool and ocean datasets, which were collected in pools and oceans, respectively, by using acoustic collectors, the proposed HADN performs better than other comparative models in terms of accuracy and mAP.