基于广义回归神经网络的强干扰下垂直阵 目标距离估计方法*

以声压场采样协方差矩阵为特征,基于广义回归神经网络(Generalized Regression Neural Network,GRNN)研究强干扰下的水下声源测距问题,提出了优化扩展因子的方法以提高神经网络定位性能。本文利用仅有一个网络参数的GRNN,使用SWellEX-96实验S59航次的垂直阵数据,比较了以传统匹配场处理(Matched Field Processing,MFP)为代表的模型驱动方法和以CNN(Convolutional Neural Networks,CNN)、GRNN为代表的数据驱动方法在强干扰下的水下目标被动定位性能。结果表明,基于优化扩展因子的GRNN网络在强干扰下可以有效实现距离估计。

Source range estimation method of vertical array under strong interference based on GRNN

The sample covariance matrix of sound pressure field is made as the feature.The research on underwater sound source ranging under strong interference based on generalized regression neural network,which has only one network parameter,extension factor.It proposes a method of optimizing the extension factor to improve the localization performance of neural network.The research uses the VLA data from event S59 of the SWellEx-96 experiment,comparing the passive positioning performance of underwater targets under strong interference of model-driven traditional matched field processing,data-driven convolutional neural networks and GRNN.The results show that GRNN based on the optimized extension factor can effectively realize the estimation of range under strong interference.