基于多域特征提取和深度学习的声源被动测距*

由于实际海洋环境中存在大量的非高斯噪声,一些基于高斯假设的传统去噪方法在实际海洋环境中性能下降甚至失效。针对非高斯噪声,如α稳定分布噪声、非平稳行船噪声下的脉冲信号的去噪与重构,该文提出一种基于深度学习的方法。去噪模型首先通过学习带噪信号短时傅里叶变换谱与残差谱之间的映射关系以去除环境噪声,之后对去噪信号的时频谱进行逆变换重构脉冲信号。仿真实验结果表明,深度学习模型在非高斯噪声环境下脉冲信号的去噪与重构任务中有着良好的表现,在实测样本上也表现出良好的泛化性,体现了一定的工程应用价值。

Source ranging based on deep learning and multi-domain feature extraction: synthetic results

Ranging of underwater acoustic sources is an important task in many practical applications. Deep neural networks (DNNs) have shown outstanding performance on illustrating complex nonlinear relationships. In terms of localization, DNNs were proved have low range estimation error under low SNR conditions. In this study, source localization is achieved by a deep learning method based on a set of multi-domain features extracted from sound signals. In this study, a comprehensive set of acoustic parameters are extracted from sound signals. The waveform, energy envelope, short-term Fourier transform are first estimated from the sound signals, based on which, the acoustic parameters are computed and used as the training data set for the estimation of source in a DNN. Training the deep architecture is achieved by Adam optimizer and drop-out for parameter regularization with mean squared error (MSE) loss functions.