ISEE.U: Distributed online active target localization with unpredictable targets

Real-world applications such as logistics, security, minerals and oil exploration, personal and vehicle navigation, wireless communications, and surveillance, just to mention a few, struggle for achieving a solution for medium-high accuracy localization of some non cooperative targets. Most approaches for range-based localization do not assume agents can control the network motion to improve localization accuracy. Thus, a passive localization algorithm solely relies on a stream of sensor data. One of the first approaches for active localization is [1], where one robot attempts to self-localize with a Markovian approach: computing a belief for a discretized map of the region of interest, given sensor measurements, and maximizing the entropy of its next movement. However, when we envision large teams of moving artificial agents, with high-level tasks, like intercepting an intruder, the computational paradigm should accommodate scalability concerns.