Line Spectrum Enhancement of Underwater Acoustic Targets Based on a Time-Frequency Attention Network
Deep learning-based line spectrum enhancement methods have received increasing attention for improving the detection performance of underwater low-noise targets using passive sonar.Among them,Long Short-Term Memory(LSTM)-based line spectrum enhancement networks have high flexibility due to their nonlinear processing capabilities in time and frequency domains.However,their performance requires further improvement.Therefore,a Time-Frequency Attention Network(TFA-Net)is proposed herein.The line spectrum enhancement effect of the LOw-Frequency Analysis Record(LOFAR)spectrum can be improved by incorporating the time and frequency-domain attention mechanisms into LSTM networks,In TFA-Net,the time-domain attention mechanism utilizes the correlation between the hidden states of LSTM to increase the model's attention in the time domain,while the frequency-domain attention mechanism increases the model's attention in the frequency domain by designing the full link layer of the shrinkage sub-network in deep residual shrinkage networks as a one-dimensional convolutional layer.Compared to LSTM,TFA-Net has a higher system signal-to-noise ratio gain:when the input signal-to-noise ratio is-3 dB and-11 dB,the system signal-to-noise ratio gain is increased from 2.17 to 12.56 dB and from 0.71 to 10.6 dB,respectively.Experimental results based on simulated and real data show that TFA-Net could effectively improve the line spectrum enhancement effect of the LOFAR spectrum and address the problem of detecting underwater low-noise targets.
万方数据
维普
