论文标题
谣言分散的高斯流程学习，融合和计划对多个移动目标进行建模
Rumor-robust Decentralized Gaussian Process Learning, Fusion, and Planning for Modeling Multiple Moving Targets
论文作者
论文摘要
本文为移动传感器网络提供了分散的高斯流程（GP）学习，融合和计划（树脂）形式，以积极学习目标运动模型。树脂的特征是计算和通信效率，以及传感器网络中谣言传播的鲁棒性。通过使用加权指数产品规则和Chernoff信息，开发了一种谣言分散的GP融合方法，以从本地GP模型中产生全球一致的目标轨迹预测。然后，提出了一种分散信息驱动的路径计划方法，以使移动传感器生成信息性的传感路径。为传感器之间的路径协调开发了一种新颖的恒定信息共享策略，并得出了一个分析目标函数，可显着降低路径计划的计算复杂性。在各种数值模拟中证明了树脂的有效性。
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.
