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F. Meyer:
"Navigation and Tracking in Networks: Distributed Algorithms for Cooperative Estimation and Information-Seeking Control";
Talk: JKU Linz, Linz, Austria; 08-05-2015.

Navigating unmanned autonomous vehicles (UAVs) and tracking objects are key prerequisites for an effective and safe operation of UAVs in real-world scenarios. To be able to perform complicated tasks autonomously, UAVs are often organized in networked teams. Due to the decentralized structure of the network, the potentially high mobility of the UAVs, and the high accuracy and robustness required for a fully autonomous operation, navigation and tracking in such mobile agent networks are difficult tasks. In large agent networks, centralized algorithms for solving these tasks are impractical since they are typically not scalable and not robust to agent failure. Therefore, to leverage the full potential of agent networks, there is a need for efficient distributed (decentralized) algorithms for navigation and tracking.

We present a new class of low-complex cooperative navigation algorithms, which are based on belief propagation (BP) message passing. These algorithms are then extended to the case that some agents in the network are noncooperative in that they do not communicate and perform computations. Furthermore, a distributed information-seeking control scheme that aims to move the agents in such a way that their measurements are maximally informative about the parameters (states) to be estimated is introduced. Simulation results demonstrate intelligent behavior of the agents and excellent estimation performance of cooperative and noncooperative agent states.