Cognitive multistatic AUV networks

Autonomous underwater vehicle (AUV) platforms are low-cost devices with respect to conventional detection and tracking systems for the purpose of anti-submarine warfare (ASW). Unfortunately, the increased level of manageability is often paid in terms of capabilities, e.g., limited speed and endurance, inferior sensor payloads, and so on. This work exploits two fundamental concepts aimed at filling the consequent performance gap. First, a multistatic network of AUVs is considered, where a smart and collaborative multi-sensor data fusion allows going beyond the individual sensors limitations. Then, we focus on the cognitive paradigm, where the single AUV units optimize their future actions (i.e., their path planning) in view of the final inference purpose of the network, and based on the evidence collected up to the present. A multistatic configuration of the platforms and the corresponding acoustic model are considered, and taken into account in order to derive a proper Bayesian model. Using the information contained in the Bayesian full posterior, cognitive detection and tracking algorithms are designed. Seeing them at work in practical scenarios shows the benefits of the cognitive network paradigm.