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On Stability and Convergence of Distributed Filters
IEEE Signal Processing Letters ( IF 3.9 ) Pub Date : 2021-02-12 , DOI: 10.1109/lsp.2021.3059207
Sayed Pouria Talebi , Stefan Werner , Vijay Gupta , Yih-Fang Huang

Recent years have bore witness to the proliferation of distributed filtering techniques, where a collection of agents communicating over an ad-hoc network aim to collaboratively estimate and track the state of a system. These techniques form the enabling technology of modern multi-agent systems and have gained great importance in the engineering community. Although most distributed filtering techniques come with a set of stability and convergence criteria, the conditions imposed are found to be unnecessarily restrictive. The paradigm of stability and convergence in distributed filtering is revised in this manuscript. Accordingly, a general distributed filter is constructed and its estimation error dynamics is formulated. The conducted analysis demonstrates that conditions for achieving stable filtering operations are the same as those required in the centralized filtering setting. Finally, the concepts are demonstrated in a Kalman filtering framework and validated using simulation examples.

中文翻译:

分布式滤波器的稳定性和收敛性

近年来见证了分布式过滤技术的激增,在分布式过滤技术中,通过自组织网络进行通信的一组代理旨在协作估计和跟踪系统状态。这些技术构成了现代多代理系统的使能技术,并且在工程界已变得非常重要。尽管大多数分布式过滤技术都带有一组稳定性和收敛标准,但是发现施加的条件受到了不必要的限制。本文修订了分布式过滤中的稳定性和收敛性范式。因此,构造了通用分布式滤波器,并制定了其估计误差动态。进行的分析表明,实现稳定过滤操作的条件与集中式过滤设置中所要求的条件相同。最后,这些概念在卡尔曼滤波框架中进行了演示,并使用仿真示例进行了验证。
更新日期:2021-03-16
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