Underwater acoustic source localization using generalized regression neural network

In this paper, machine learning is introduced to source localization in underwater ocean waveguides. Source localization is regarded as a supervised learning regression problem and is solved by generalized regression neural network (GRNN). As a feed-forward network, GRNN is built using training data with fixed structure and configuration. The normalized sample covariance matrix (SCM) formed over a number of snapshots, and the corresponding source position are used as the input and output for GRNN. The source position can be estimated directly from the normalized SCM with GRNN; the proposed approach is thus in theory data driven. In addition, there is only one parameter, the spread factor, to be learned for GRNN. The optimal spread factor is determined using cross-validation. The regression method of GRNN is compared with the classification method of feed-forward neural network (FNN), as well as the classical method of matched field processing (MFP) for vertical array data from the SWellEx-96 experiment. The results show that GRNN achieves a satisfactory localization performance that outperforms both FNN and MFP. The proposed approach provides an alternative way for underwater source localization, especially in the absence of a priori environmental information or an appropriate propagation model.