Implementation of Bartlett matched-field processing using interpretable complex convolutional neural network

Neural networks have been applied to underwater source localization and achieved better performance than the conventional matched-field processing (MFP). However, compared with MFP, the neural networks lack physical interpretability. In this work, an interpretable complex convolutional neural network based on Bartlett processor (BC-CNN) for underwater source localization is designed, the output and structure of which have clear physical meanings. The relationship between the convolution weights of BC-CNN and replica pressure of MFP is discussed, which effectively presents the interpretability of the BC-CNN. Simulation experiments using two kinds of labels demonstrate the equivalence between the Bartlett MFP and BC-CNN.