Active sonar target classification using a physics-cognizant feature representation

Active sonar target classification is challenging due to the non-linear overlap of changing oceanic and target parameters, creating entangled acoustic color spectra that should be disentangled prior to classification. A physics-cognizant feature extraction algorithm, used before interfacing with three machine learning techniques for active sonar target classification of experimental field data, is presented. The feature extraction algorithm convolves a two-dimensional Gabor wavelet across acoustic color spectra prior to threshold-based binarization, feature culling, and dimensional reduction. The optimal two-dimensional Gabor wavelet parameters are chosen through sensitivity analysis by a support vector machine (SVM) on a disjoint subset of data. Classification is performed on the second subset of data with an SVM, random forest tree, and neural network on the Gabor filtered spectra and unfiltered spectra to show the increased classification accuracy of the application of the geometric wavelet. Classification results are presented as confusion matrices for four targets of two public domain experiments. Ongoing and future work will include extending this feature extraction technique using various geometric feature representations to capture and describe far-field large scattering mechanisms from targets such as oil rigs, tankers, and shipwrecks. [This research is funded by the Office of Naval Research under Grant No. N00014-19-1-2436.]