Robust Dual Target Range Estimation Method of Vertical Array Using Residual Convolution Neural Network

In the actual marine environment, it is often necessary to locate multiple underwater targets at the same time. Aiming at the problem of simultaneous localization of multiple sound sources, this paper studies the double target range estimation scene in the same depth and uses normal wave model to generate the underwater acoustic data of dual targets. The range estimation of dual targets in the same sea area and different sea areas is carried out, and two frequency combinations of dual targets, namely 280/385Hz and 49/388Hz, are taken as examples to analyze the impact of different frequencies on the range estimation results. The network structure adopts the convolution neural network (CNN) with residual connection. The simulation results show that the residual CNN model can effectively realize the range estimation of dual targets in the same sea area and different sea areas, and the greater the frequency difference, the more accurate the range estimation results of the dual targets. Therefore, all results show that the proposed method has excellent performance for the dual target range estimation.