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.

中文翻译：

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可靠的去中心化高斯过程学习、融合和规划多运动目标建模

本文提出了一种分散式高斯过程 (GP) 学习、融合和规划 (RESIN) 形式，用于移动传感器网络以主动学习目标运动模型。RESIN 的特点是计算和通信效率，以及传感器网络中谣言传播的鲁棒性。通过使用加权指数乘积规则和切尔诺夫信息，开发了一种可靠的分散 GP 融合方法，以从局部 GP 模型生成全局一致的目标轨迹预测。然后为移动传感器提出了一种分散的信息驱动路径规划方法，以生成信息传感路径。开发了一种新颖的、大小不变的信息共享策略，用于传感器之间的路径协调，并推导出分析目标函数，显着降低路径规划的计算复杂度。RESIN 的有效性在各种数值模拟中得到了证明。