Qi-Hai Yao

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

Experimental results indicate that the adopted augmentation can considerably enlarge the training sample set, which consequently enhances the ranging accuracy, and the ResNet-UNet method effectively accomplishes range estimation and its performance markedly surpasses that of the other models.