Publications [#355260] of Silvia Ferrari

Papers Published

1. Liu, C; Liao, Z; Ferrari, S, Rumor-robust Decentralized Gaussian Process Learning, Fusion, and Planning for Modeling Multiple Moving Targets, Proceedings of the Ieee Conference on Decision and Control, vol. 2020-December (December, 2020), pp. 3066-3071, ISBN 9781728174471 [doi]
   (last updated on 2021/09/05)

   Abstract:
   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 sensing path coordination, and an analytical objective function is derived that significantly reduces the computational complexity of the path planning. The effectiveness of RESIN is demonstrated in simulations.