Unbiased Minimum Variance Estimation for Discrete-Time Systems with Measurement Delay and Unknown Measurement Disturbance

被引：0
|
作者：
Guan, Yu [1 ]
Song, Xinmin [1 ,2 ]
机构：
[1] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Shandong, Peoples R China
[2] Shandong Normal Univ, Inst Data Sci & Technol, Jinan 250014, Shandong, Peoples R China
来源：
MATHEMATICAL PROBLEMS IN ENGINEERING | 2018年 / 2018卷
基金：
美国国家科学基金会;
关键词：
OUTPUT-FEEDBACK STABILIZATION; ORDER NONLINEAR-SYSTEMS; STATE ESTIMATION; ASYMPTOTIC STABILITY; MULTIAGENT SYSTEMS; OPTIMALITY; CONSENSUS; FILTER; INPUTS;
D O I：
10.1155/2018/2831561
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This paper addresses the state estimation problem for stochastic systems with unknown measurement disturbances whose any prior information is unknown and measurement delay resulting from the inherent limited bandwidth. For such complex systems, the Kalman-like one-step predictor independent of unknown measurement disturbances is designed based on the linear unbiased minimum variance criterion and the reorganized innovation analysis approach. One simulation example shows the effectiveness of the proposed algorithms.
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页数：7
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