论文标题
传感器网络中数据驱动的分布式状态估计和行为建模
Data-Driven Distributed State Estimation and Behavior Modeling in Sensor Networks
论文作者
论文摘要
如今，传感器网络的流行率已能够跟踪动态对象状态，从而为从自主驾驶到环境监测和城市规划的广泛应用。但是，跟踪现实世界对象通常面临两个关键挑战：首先，由于单个传感器的限制，需要以协作和分布式的方式解决状态估计。其次，对象的运动行为是未知的，需要使用传感器观察来学习。在这项工作中，我们首次正式提出了传感器网络中同时进行状态估计和行为学习的问题。然后，我们通过将基于高斯流程的贝叶斯过滤器（GP-bayesfilters）扩展到在线分布式设置，为这个新问题提出了一个简单而有效的解决方案。使用合成数据和从多机器人平台收集的综合数据和数据来评估所提出方法的有效性。
Nowadays, the prevalence of sensor networks has enabled tracking of the states of dynamic objects for a wide spectrum of applications from autonomous driving to environmental monitoring and urban planning. However, tracking real-world objects often faces two key challenges: First, due to the limitation of individual sensors, state estimation needs to be solved in a collaborative and distributed manner. Second, the objects' movement behavior is unknown, and needs to be learned using sensor observations. In this work, for the first time, we formally formulate the problem of simultaneous state estimation and behavior learning in a sensor network. We then propose a simple yet effective solution to this new problem by extending the Gaussian process-based Bayes filters (GP-BayesFilters) to an online, distributed setting. The effectiveness of the proposed method is evaluated on tracking objects with unknown movement behaviors using both synthetic data and data collected from a multi-robot platform.
