Estimation for Networked Random Sampling Systems With Packet Losses

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-09-01 DOI:10.1109/TSMC.2019.2956156

Honglei Lin, Shuli Sun

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引用次数: 2

Abstract

The state estimation problem is investigated in this article for networked random sampling linear stochastic systems. In the system, the system state uniformly updates and the measurement is randomly sampled. Packet losses induced by unreliable networks from a controller to an actuator and from a sensor to an estimator under the TCP protocol are tackled by employing two independent Bernoulli distributed stochastic variables. A state space model (SSM) at successfully received measurement sampling (SRMS) points is developed under the condition of known sampling time. Using an innovation analysis approach, a recursive nonaugmented optimal estimator is proposed in the linear minimum variance (LMV) sense. It can obtain state estimates at state update (SU) points and SRMS points. In addition, for multisensor systems, a centralized fusion estimator by reordering measurement data from sensors and a suboptimal distributed covariance intersection fusion estimator are proposed, respectively. The effectiveness of the proposed algorithms is verified through an example.

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具有丢包的网络随机抽样系统的估计

研究了网络随机抽样线性随机系统的状态估计问题。在系统中，系统状态均匀更新，测量结果随机采样。在TCP协议下，采用两个独立的伯努利分布随机变量来解决从控制器到执行器和从传感器到估计器的不可靠网络引起的丢包问题。在采样时间已知的条件下，建立了成功接收测量采样点的状态空间模型。利用一种创新的分析方法，提出了线性最小方差(LMV)意义下的递归非增广最优估计。它可以获得状态更新(SU)点和SRMS点的状态估计。此外，针对多传感器系统，分别提出了一种传感器测量数据重排序的集中式融合估计器和一种次优分布协方差交叉融合估计器。通过算例验证了所提算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans 工程技术-计算机：控制论

自引率

0.00%

发文量

审稿时长

6.0 months

期刊介绍： The scope of the IEEE Transactions on Systems, Man, and Cybernetics: Systems includes the fields of systems engineering. It includes issue formulation, analysis and modeling, decision making, and issue interpretation for any of the systems engineering lifecycle phases associated with the definition, development, and deployment of large systems. In addition, it includes systems management, systems engineering processes, and a variety of systems engineering methods such as optimization, modeling and simulation.