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This paper presents a decentralized Gaussian Process (GP) learning, fusion,and planning (RESIN) formalism for mobile sensor networks to actively learntarget motion models. RESIN is characterized by both computational andcommunication efficiency, and the robustness to rumor propagation in sensornetworks. By using the weighted exponential product rule and the Chernoffinformation, a rumor-robust decentralized GP fusion approach is developed togenerate a globally consistent target trajectory prediction from local GPmodels. A decentralized information-driven path planning approach is thenproposed for mobile sensors to generate informative sensing paths. A novel,constant-sized information sharing strategy is developed for path coordinationbetween sensors, and an analytical objective function is derived thatsignificantly reduces the computational complexity of the path planning. Theeffectiveness of RESIN is demonstrated in various numerical simulations.

Rumor-robust Decentralized Gaussian Process Learning, Fusion, and Planning for Modeling Multiple Moving Targets