Underwater Tracking Based on the Sum-Product Algorithm Enhanced by a Neural Network Detections Classifier

The necessity of long-range underwater surveillance has strongly increased in the last decades, and low-frequency active sonar (LFAS) systems seem to fulfill this need. However, in littoral environments with shallow water LFAS may suffer from an elevate number of false alarms. In this context, the capability to distinguish between object-generated and clutter-generated detections is crucial. This paper describes a multiobject tracking framework based on the sum-product algorithm that exploits the information provided by a convolutional neural network that classifies the LFAS detections. The effectiveness of the proposed approach is demonstrated both in a simulated and in a real underwater scenario.