  This paper presents a decentralized Gaussian Process (GP) learning, fusion,
and planning (RESIN) formalism for mobile sensor networks to actively learn
target motion models. RESIN is characterized by both computational and
communication efficiency, and the robustness to rumor propagation in sensor
networks. By using the weighted exponential product rule and the Chernoff
information, a rumor-robust decentralized GP fusion approach is developed to
generate a globally consistent target trajectory prediction from local GP
models. A decentralized information-driven path planning approach is then
proposed for mobile sensors to generate informative sensing paths. A novel,
constant-sized information sharing strategy is developed for path coordination
between sensors, and an analytical objective function is derived that
significantly reduces the computational complexity of the path planning. The
effectiveness of RESIN is demonstrated in various numerical simulations.
