Wavelet transform as a preprocessing method for neural classification of passive sonar signals

Abstract

A neural classifier is developed for passive sonar signals. For achieving data compaction and high performance on the identification of ship classes, the neural processing is performed on preprocessed data in the frequency domain. Preprocessing comprises averaged spectral analysis over contiguous acquisition windows, background noise estimation and wavelet transformation. The overall discrimination efficiency achieved was better than 94%, considering four classes of ships.