Abstract:

Contemporary anti-invasion mines have aspect-dependent shapes, making discrimination of mines from nonthreat objects, and classification of mines, a difficult task for human mine countermeasures personnel. Shallow-water (SW) noise and bottom reverberation substantially degrade the acoustic structure of mine echoes. Traditional MCM target strength and FFT classifiers are not effective under these conditions. The testing of a novel neural network classifier to solve the task of classifying mines in the SW acoustic environment was begun. Three mine types were ensonified at 1 deg rotations using synthetic dolphin clicks. A learning vector quantization network was trained to classify the mines using the Choi--Williams joint time-frequency distributions of echoes. A training set of 36 echoes per mine (5, 15, 25,..., 355 deg) was created, with the remaining echoes (0, 10, 20,..., 350 deg) reserved for generalization testing. The network correctly classified the mines at novel orientations with 85% accuracy. Performance using CWD will be contrasted with biomimetic sonar target classifiers under varying signal-to-noise ratios. [Research funded by ONR Underseas Active Signal Processing Program.]