Optimal induced-norm and set membership state smoothing and filtering for linear systems with bounded disturbances

被引：6

作者：

Garulli, A

Vicino, A

Zappa, G

机构：

[1] Univ Siena, Dipartimento Ingn Informaz, I-53100 Siena, Italy

[2] Univ Florence, Dipartimento Sistemi & Informat, I-50139 Florence, Italy

来源：

AUTOMATICA | 1999年 / 35卷 / 05期

关键词：

state estimation; set membership; smoothing; filtering; bounded disturbances;

D O I：

10.1016/S0005-1098(98)00212-X

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper a unified framework founded on Information-Based Complexity is introduced, to study set membership and optimal induced-norm state estimation problems, for linear systems subject to norm bounded process noise and measurement errors. The proposed approach leads to a clean geometric picture of the problem, allowing for a straightforward derivation of several existing results. Moreover, it permits to tackle new estimation problems in which both induced-norm optimization and consistency of the estimate with the noise bound are required. (C) 1999 Elsevier Science Ltd. All rights reserved.

引用

页码：767 / 776

页数：10

共 13 条

[1] OPTIMUM PERFORMANCE LEVELS FOR MINIMAX FILTERS, PREDICTORS AND SMOOTHERS [J].