Deep-Sea Source Ranging Method Using Modified General Regression Neural Network

To improve the accuracy of deep-sea source ranging, this study proposes a deep-sea acoustic source localization algorithm based on particle swarm optimization (PSO)–general regression neural network (GRNN). The method optimizes the parameters of the GRNN model using the particle swarm algorithm to reduce the influence of artificially determined parameters on the performance of the GRNN. The normalized sample covariance matrices associated with the acoustic source location are used as the input for the neural network model, and the acoustic source range is the output. The PSO–GRNN method is validated using the results of deep-sea experiments conducted in the South China Sea in 2017 and compares the performance of underwater target range estimation with the results of traditional matched field processing and convolutional neural networks. The validation demonstrates that the PSO–GRNN method has high prediction accuracy and strong stability and is less prone to human error than traditional methods. Therefore, the method proposed in this study is effective for underwater target ranging in deep-sea environments.