Range estimation of low-frequency underwater acoustic target based on deep learning architecture with data augmentation

Abstract

Passive source localization is essential in underwater acoustics, yet both model‑based matched field processing (MFP) and conventional machine learning methods often suffer from limited accuracy and poor generalization. To address these challenges, this study presents a low-frequency underwater acoustic ranging approach that integrates data augmentation with the ResNet-UNet architecture. Using the real and imaginary components of the covariance matrix as inputs, the sample expansion is first performed by combining the deep convolutional generative adversarial network with several conventional augmentation techniques. Afterwards, a predictive model that fuses ResNet and U-Net is developed for target range estimation. The validity of the proposed method is examined through the SWellEX-96 sea trial data, where the performance is compared under two conditions, with and without the augmentation strategy, and also benchmarked against several reference methods, namely MFP, generalized regression neural network (GRNN), conventional convolutional neural network (CNN), ResNet, and the proposed ResNet-UNet. Experimental results indicate that the adopted augmentation can considerably enlarge the training sample set, which consequently enhances the ranging accuracy. The majority of MFP estimates fall beyond the acceptable error margin, while GRNN shows obvious weaknesses in generalization performance. Both the conventional CNN and ResNet are only capable of producing coarse range approximations. Nevertheless, when coupled with the proposed augmentation, the ResNet-UNet method effectively accomplishes range estimation and its performance markedly surpasses that of the other models. Moreover, it remains effective even under low low signal-to-noise ratio conditions.