Preprocessing passive sonar signals for neural classification

The noise radiated from ships in the ocean contains information about their machinery and can be used for detection and identification purposes. Here, a preprocessing method is developed in order to improve the performance of a feedforward neural network, which is used to classify four classes of ships. The entire system operates in the frequency domain over the information collected by the sensors of a passive sonar system. The effect of spectra averaging, resolution and background noise normalisation in the classifier performance is evaluated. Using preprocessed data to feed the input nodes of the classifier, a classification efficiency of about 97% has been achieved.