首页|Underwater Pulse Waveform Recognition Based on Hash Aggregate Discriminant Network

Underwater Pulse Waveform Recognition Based on Hash Aggregate Discriminant Network

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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 propaga-tion 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 per-forms better than other comparative models in terms of accuracy and mAP.

convolutional channelhash aggregate discriminative networkaggregate discriminant losswaveform recognition

WANG Fangchen、ZHONG Guoqiang、WANG Liang

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College of Computer Science and Technology,Ocean University of China,Qingdao 266100,China

College of Marine Technology,Ocean University of China,Qingdao 266100,China

National Key Research and Development Program of ChinaNatural Science Foundation of Shandong ProvinceNatural Science Foundation of Shandong ProvinceScience and Technology Program of Qingdao

2018 AAA0100400ZR2020MF131ZR2021ZD1921-1-4-ny-19-nsh

2024

10.1007/s11802-024-5544-x

中国海洋大学学报(自然科学英文版)
中国海洋大学

中国海洋大学学报(自然科学英文版)

CSTPCD
影响因子：0.268
ISSN：1672-5182
年,卷(期)：2024.23(3)
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