摘要
以声压场采样协方差矩阵为特征,基于广义回归神经网络研究在强干扰下的水下声源测距问题,该文提出了优化扩展因子的方法以提高神经网络估计性能。使用仅有一个网络参数的广义回归神经网络,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为代表的模型驱动方法和以卷积神经网络、广义回归神经网络为代表的数据驱动方法在强干扰下的水下目标距离估计性能。结果表明,基于优化扩展因子的广义回归神经网络在强干扰下可以有效实现距离估计。
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(GRNN), which has only one network parameter, extension factor. It proposes a method of optimizing the extension factor to improve the estimation performance of neural network. The research uses the vector line array(VLA) data from event S59 of the SWellEx-96 experiment, comparing the range estimation performance of underwater targets under strong interference of model-driven traditional matched field processing, datadriven convolutional neural networks(CNN) and GRNN. The results show that GRNN based on the optimized extension factor can effectively realize the estimation of range under strong interference.
作者
姚琦海
汪勇
黎佳艺
杨益新
YAO Qihai;WANG Yong;LI Jiayi;YANG Yixin(School of Marine Science and Technology,Northwestern Polytechnical University,Xi’an 710072,China)
出处
《应用声学》
CSCD
北大核心
2021年第5期723-730,共8页
Journal of Applied Acoustics
基金
国家自然科学基金项目(61971353)。
关键词
距离估计
强干扰
垂直阵
广义回归神经网络
Range estimation
Strong interference
Vector line array
Generalized regression neural network
