This paper presents novel Gaussian process decentralized data fusion
algorithms exploiting the notion of agent-centric support sets for distributed
cooperative perception of large-scale environmental phenomena. To overcome the
limitations of scale in existing works, our proposed algorithms allow every
mobile sensing agent to choose a different support set and dynamically switch
to another during execution for encapsulating its own data into a local summary
that, perhaps surprisingly, can still be assimilated with the other agents'
local summaries (i.e., based on their current choices of support sets) into a
globally consistent summary to be used for predicting the phenomenon. To
achieve this, we propose a novel transfer learning mechanism for a team of
agents capable of sharing and transferring information encapsulated in a
summary based on a support set to that utilizing a different support set with
some loss that can be theoretically bounded and analyzed. To alleviate the
issue of information loss accumulating over multiple instances of transfer
learning, we propose a new information sharing mechanism to be incorporated
into our algorithms in order to achieve memory-efficient lazy transfer
learning. Empirical evaluation on real-world datasets show that our algorithms
outperform the state-of-the-art methods.

论文提出了利用代理中心支持集的高斯过程分散数据融合算法，允许每个移动感知代理在执行过程中选择不同的支持集并动态切换，以便将其自己的数据封装到本地摘要中，然后可以与其他代理的本地摘要相结合，从而预测环境现象。同时，提出了一种新的信息共享机制，并通过实验验证了该算法的性能优异。