Unsupervised active sonar contact classification through anomaly detection

Abstract

Target detection and sonar contact classification with active sonar systems are not trivial especially when operating in coastal and shallow water environments with multipath propagation, high reverberation and clutter. It is even more difficult when the sonar receiver is hosted on unmanned platforms with limited maneuvering capabilities unable to perform long-lasting tracking procedures. In such environments with high clutter density, real-time classification algorithms to discriminate target contacts from clutter contacts become crucial. This paper describes a method for active sonar clutter classification that exploits the large number of undesired contacts to learn the “fingerprint” of the environmental clutter and thus to identify the target contacts as anomalies. The paper introduces the method to obtain the features of detected sonar contacts from the beamformed signal of a triplet array of hydrophones that can be towed by an autonomous underwater vehicle. The paper also shows the performance of the proposed unsupervised classification algorithm with real data collected at sea and compares it to what has been achieved by using a convolutional neural network. The results show the capability of the proposed anomaly detection algorithm to properly deal with a variety of clutter contacts without requiring labeling of training data.