Facing the complex marine environment, it is extremely challenging to conduct underwater acoustic target recognition (UATR) using ship-radiated noise. Inspired by neural mechanism of auditory perception, this paper provides a new deep neural network trained by original underwater acoustic signals with depthwise separable convolution (DWS) and time-dilated convolution neural network, named auditory perception inspired time-dilated convolution neural network (ATCNN), and then implements detection and classification for underwater acoustic signals. The proposed ATCNN model consists of learnable features extractor and integration layer inspired by auditory perception, and time-dilated convolution inspired by language model. This paper decomposes original time-domain ship-radiated noise signals into different frequency components with depthwise separable convolution filter, and then extracts signal features based on auditory perception. The deep features are integrated on integration layer. The time-dilated convolution is used for long-term contextual modeling. As a result, like language model, intra-class and inter-class information can be fully used for UATR. For UATR task, the classification accuracy reaches 90.9%, which is the highest in contrast experiment. Experimental results show that ATCNN has great potential to improve the performance of UATR classification.
翻译:面对复杂的海洋环境,利用船舶辐照的噪音进行水下声控目标识别(UATR)是极具挑战性的。在听觉感知神经机制的启发下,本文件提供了一个由原始水下声学信号培训的新的深神经网络,由原始水下声波信号培训,其深度分解共变(DWS)和时间拉动神经神经网络,其名称为听觉感觉感应,其时间拉动共振动神经网络(ATCNN),然后对水下声波信号进行探测和分类。拟议的ATCNN模型由听觉感觉感知的可学习性能提取器和集成层以及语言模型所启发的经时间拉动演化器组成。本文将原始时间拉动的船舶噪声信号转换成不同频率组成部分,其深度分解共振荡(DWS)和时间拉动神经网络(NN),然后根据听觉知觉觉觉觉觉感应提取信号特征。时间拉动的演算法用于长期背景模拟。结果如语言模型、内部和阶级间信息层信息可充分用于UATR的演化过程。在最大实验中将UAT-9进行最精确的实验,UTR的性结果显示。UA-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A