DRW-AE: A Deep Recurrent-Wavelet Autoencoder for Underwater Target Recognition

Abstract

The sonar received signals may contain various artifacts such as noise, reverberation, and clutter that affect the proper target recognition and classification capabilities of sonar systems. This performance reduction is exacerbated by multidirectional propagation, underwater heterogeneity, variable sound channels, and variable sound-speed profiles. To address these problems, this article develops a deep recurrent-wavelet autoencoder (DRW-AE) coupled with some machine learning classifiers to design an end-to-end underwater target classifier. In this approach, autoencoders play automatic feature extractors’ role in choosing the best feature composition in terms of types and dimensions without human intervention. Wavelet networks extract the vessels’ periodic frequency signatures, which are changed by various machinery conditions; finally, the recurrent network addresses the effects of time-varying and time-dependent inhomogeneous underwater environment. To investigate the efficiency of the hybrid DRW-AE, ShipsEar data set is exploited. Before comparing with other methods, the symmetric and asymmetric mother wavelet families were investigated to choose the proper wavelet function. Then, the efficiency of different combinations of deep autoencoders and proposed classifiers is investigated. Finally, the performance of the DRW-AE is compared with ten benchmark methods that have used this data set. The results show that the proposed algorithm with 94.49% accuracy and giga-multiplier–accumulators equal to 0.02 represents the best performance in terms of network accuracy and complexity compared to benchmark models.