存在数据包丢失的大规模系统的分布式状态估计

Xiao Fu, Xinmin Song
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摘要

本文主要研究大规模系统中存在数据包丢失的分布式状态估计问题。大系统在物理空间中由多个相互关联的子系统构成,每个子系统只与相邻子系统通信。特别地,不同子系统的状态由不同的传感器测量,传感器通过有损通信信道将测量信息广播给下估计器及其邻居。因此,在存在数据包丢失的情况下,次估计器获得不同的局部信息。本文设计了分布式估计器,利用局部信息集在最小均方误差(MMSE)估计准则下获得最优增益。最后,通过仿真实验验证了分布式估计器的有效性。
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Distributed State Estimation for Large-Scale Systems in the Presence of Data Packet Drops
This article focuses on the distributed state estimation problem for large-scale systems in the presence of data packet drops. The large-scale system is structured in several correlated subsystems in the physical space, and each subsystem only communicates with its neighbors. In particular, the states of different subsystems are measured by different sensors, and the sensor broadcasts measurement information to the subestimator and its neighbors through the lossy communication channel. Thus, subestimators obtain different local information in the presence of data packet drops. In this article, the distributed estimator is designed and the optimal gain is obtained under the minimum mean square error (MMSE) estimation criterion by using local information set. Finally, the effectiveness of the distributed estimator is illustrated by a simulation experiment.
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