Clustering of Multiple Ships Underwater Radiated Noise

The increase in shipborne underwater radiated noise (URN) is considered a source of pollution, and should be monitored regularly. We identify knowledge gap in the URN monitoring of vessels of opportunity, where the recording may include URN from multiple vessels simultaneously. To that end, we proposed a method to distinguish, by clustering, between narrow-band tonal lines originated from multiple vessels as received by a single omnidirectional hydrophone. Our clustering is based on feature extraction to classify the tonal lines into vessels by clustering the tonal lines to groups based on their likelihood to be originated from the same source. We present proof-of-concept based on data collected from the "ShipsEar"database and a test-case that shows more than 75% in the true negative and true positive for vessel association.