Ship noise classification using Probabilistic Neural Network and AR model coefficients

The establishment of intelligent systems for classifying marine vessels based on their acoustic radiated noise is of major importance in the sonar systems. This paper presents a new method for classification of marine vessels. In this method, the acoustic radiated noise of ships is modeled by an AR (Autoregressive) model with appropriate order and coefficients of this model are used for classification of ships. A Probabilistic Neural Network (PNN) is used as the classifier and the AR model coefficients are used as the input vector to this classifier. The performance of this method is examined by using a bank of real data files. The results of evaluating the proposed method with real data show that this method is successful in classifying ships into three separate classes (heavy ships, medium ships, and boats).