Passive Multi-Sensor Fusion for Tracking Underwater Targets: Field Experience from REPMUS22

• Published in: OCEANS 2024 - Singapore pp. 1 - 7
• Main Authors: Soldi, Giovanni, Ferri, Gabriele, Faggiani, Alessandro, Stinco, Pietro, Tesei, Alessandra, Been, Robert
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15.04.2024
• Subjects: Atmospheric measurements; autonomous underwater vehicles (AUVs); data fusion; Fuses; Noise; passive sonars; Robot sensing systems; Robotic network; Sea measurements; Sensor fusion; surveillance; Target tracking
• DOI: 10.1109/OCEANS51537.2024.10682307

Robotic Networks equipped with passive sensors, which can detect the noise emitted by targets moving in the ocean, represent a covert and effective solution for the surveillance of large underwater areas. In this context, advanced information fusion methodologies, which are able to coherently fuse the generated bearing-only measurements and track existing underwater targets, are required to achieve a comprehensive underwater survelliance capability in anti-submarine warfare (ASW) scenarios. This paper will focus on the performance of the delta-generalized labelled multi-Bernoulli (\delta-\mathbf{GLMB}) tracking filter, an information fusion methodology which was employed during the Robotics Experimentation and Prototyping MUS (REPMUS22) and Dynamic Messenger (DYMS22) trials, held in Portugal in September 2022. In particular, we will show that the 6-GLMB tracking filter is able to effectively fuse passive measurements generated by a hybrid robotic network composed of two bottom sensing nodes and two autonomous underwater vehicles (AUVs), thus enabling near real-time monitoring of large underwater areas.