Multiple Mobile Target Detection and Tracking in Active Sonar Array Using a Track-Before-Detect Approach

We present an algorithm for detecting and tracking underwater mobile objects using active acoustic transmission of broadband chirp signals whose reflections are received by a hydrophone array. The method overcomes the problem of high false alarm rate by applying a track-before-detect approach to the sequence of received reflections. A 2D time-space matrix is created for the reverberations received from each transmitted probe signal by performing delay and sum beamforming and pulse compression. The result is filtered by a 2D constant false alarm rate (CFAR) detector to identify reflection patterns corresponding to potential targets. Closely spaced signals for multiple probe transmissions are combined into blobs to avoid multiple detections of a single object. A track-before-detect method using a Nearly Constant Velocity (NCV) model is employed to track multiple objects. The position and velocity is estimated by the debiased converted measurement Kalman filter. Results are analyzed for simulated scenarios and for experiments at sea, where GPS tagged gilt-head seabream fish were tracked. Compared to two benchmark schemes, the results show a favorable track continuity and accuracy that is robust to the choice of detection threshold.

Underwater object classification combining SAS and transferred optical-to-SAS Imagery

Combining synthetic aperture sonar (SAS) imagery with optical images for underwater object classification has the potential to overcome challenges such as water clarity, the stability of the optical image analysis platform, and strong reflections from the seabed for sonar-based classification. In this work, we propose this type of multi-modal combination to discriminate between man-made targets and objects such as rocks or litter. We offer a novel classification algorithm that overcomes the problem of intensity and object formation differences between the two modalities. To this end, we develop a novel set of geometrical shape descriptors that takes into account the geometrical relation between the objects shadow and highlight. Results from 7,052 pairs of SAS and optical images collected during several sea experiments show improved classification performance compared to the state-of-the-art for better discrimination between different types of underwater objects. For reproducibility, we share our database.