论文标题
使用平均模型的大型系统的分布式密度过滤
Distributed Density Filtering for Large-Scale Systems Using Mean-Filed Models
论文作者
论文摘要
这项研究分布了大规模系统的（概率）密度估计。这些问题是由许多基于密度的分布式控制任务激发的，在这些任务中，群体的实时密度被用作反馈信息，例如传感器部署和城市交通计划。这项工作是基于我们以前的工作[1]构建的，该工作提出了一个（集中式的）密度过滤器，以通过均值场模型，内核密度估计（KDE）和Infinite-Dimensional Kalman滤波器的新型整合来估算大型系统的动态密度。在这项工作中，我们进一步研究了如何分散密度过滤器，以便仅根据其局部观察和与邻居的沟通来估算全球密度。这是通过指出KDE构建的全球观察是当地内核的平均值来实现的。因此，动态平均共识算法用于每个代理以分布式方式跟踪全局观察。我们提出了一个分布式密度过滤器，该滤波器几乎不需要信息交换，并使用输入到国家稳定性的概念来研究其稳定性和最优性。仿真结果表明，分布式过滤器能够收敛到集中级过滤器并保持靠近其。
This work studies distributed (probability) density estimation of large-scale systems. Such problems are motivated by many density-based distributed control tasks in which the real-time density of the swarm is used as feedback information, such as sensor deployment and city traffic scheduling. This work is built upon our previous work [1] which presented a (centralized) density filter to estimate the dynamic density of large-scale systems through a novel integration of mean-field models, kernel density estimation (KDE), and infinite-dimensional Kalman filters. In this work, we further study how to decentralize the density filter such that each agent can estimate the global density only based on its local observation and communication with neighbors. This is achieved by noting that the global observation constructed by KDE is an average of the local kernels. Hence, dynamic average consensus algorithms are used for each agent to track the global observation in a distributed way. We present a distributed density filter which requires very little information exchange, and study its stability and optimality using the notion of input-to-state stability. Simulation results suggest that the distributed filter is able to converge to the centralized filter and remain close to it.
