Underwater acoustic target waveform recovery based on deep neural networks
WANG Quandong;GUO Lianghao;YAN Chao;State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences;University of Chinese Academy of Sciences;
A deep neural network(DNN) based method is proposed to recover underwater acoustic target signal waveform under noise. In single sensor condition, the log-power spectral(LPS) feature is extracted as input, and a DNN regression model is employed to adaptively learn the inherent pattern of target signal and output the enhanced LPS to recover the waveform. In multi-sensor condition, an array-based DNN which uses the concatenated feature from partial or all sensors as input is proposed to exploit spatial information. To fully use the rich temporal and spatial information from the array, we propose a two-stage DNN. In the first stage, the array is split into sub-arrays and each sub-array is processed by an array-based DNN, while in the second stage,the sub-array enhanced features and noisy array features are input to a DNN for integration. Experiments show that our single-sensor and two-stage DNN achieved far better recovery results than conventional beamforming,can accurately recover the target waveform and power and significantly improve the output signal-to-noise ratio.