Ships Classification Basing On Acoustic Signatures

The paper presents the technique of artificial neural networks used as classifier of hydroacoustic signatures generated by moving ship. The main task of proposed solution is to classify the objects which made the underwater noises. Firstly, the measurements were carried out dynamically by running ship past stationary hydrophones, mounted on tripods 1 m above the sea bottom. Secondly to identify the source of noise the level of vibration were measured on board by accelerometers, which were installed on important components of machinery. On the base of this measurement there was determined the sound pressure level, noise spectra and spectograms, transmission of acoustic energy via the hull into water. More over it was checked by using coherence function that components of underwater noise has its origin in vibrations of ship’s mechanisms. Basing on this research it was possible to create the hydroacoustic signature or so called “acoustic portrait” of moving ship. Next during the complex ships’ measurements on Polish Navy Test and Evaluation Acoustic Range hydroacoustic noises generated by moving ship were acquired. Basing on these results the classifier of acoustic signatures using artificial neural network was worked out. From the technique of artificial neural networks the Kohonen networks which belongs to group of self organizing networks where chosen to solve the research problem of classification. The choice was caused by some advantages of mentioned kind of neural networks like: they are ideal for finding relationships amongst complex sets of data, they have possibility to self expand the set of answers for new input vectors. To check the correctness of classifier work the research in which the number of right classification for presented and not presented before hydroacoustic signatures were made. Some results of research were presented on this paper. Described method actually is extended and its application is provided as assistant subsystem for hydrolocations systems of Polish Naval ships. Key-Words: Self-Organizing Map, Kohonen’s neural networks, Hydroacousitc signatures, Classification.