Line spectrum target recognition algorithm based on time-delay autoencoder

Effective extraction of target features has always been a key issue in target recognition technology in the field of signal processing. Traditional deep learning algorithms often require extensive data for pre-training models to ensure the accuracy of feature extraction. Moreover, it is challenging to completely remove noise due to the complexity of the underwater environment. A Time-Delay Autoencoder (TDAE) is employed to extract ship-radiated noise characteristics by leveraging the strong coherent properties of line spectrum. This approach eliminates the need for previous data to adaptively develop a nonlinear model for line spectrum extraction. The test data was processed using three distinct approaches, and plots of recognition accuracy curves at various signal-to-noise ratios were made. On the dataset utilised in the research, experimental results show that the proposed approach achieves over 75% recognition accuracy, even at a signal-to-noise ratio of -15 dB. The strong coherent properties of line spectral features are utilised to extract ship radiation noise features using Time-delay Autoencoder (TDAE) without relying on prior data for adaptively constructing a nonlinear model for line spectral feature extraction. Experimental results demonstrate that the proposed algorithm achieves over 75% recognition accuracy on the dataset used even at a signal-to-noise ratio of -15 dB. image