  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.
