H∞information fusion filtering for discrete-time systems with time-delay sensors

Authors:
Jie Yan;Xiao Lu
Affiliations:
Key Laboratory for Robot & Intelligent Technology of Shandong Province, Shandong University of Science and Technology, Qingdao, P. R. China;Key Laboratory for Robot & Intelligent Technology of Shandong Province, Shandong University of Science and Technology, Qingdao, P. R. China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 6
Cited 0

Indefinite-quadratic estimation and control: a unified approach to H2 and H∞ theories

Indefinite-quadratic estimation and control: a unified approach to H2 and H∞ theories
Sensor and Data Fusion Concepts and Applications

Sensor and Data Fusion Concepts and Applications
H∞ filtering for multiple-time-delay measurements

IEEE Transactions on Signal Processing
Aggregation operators for linguistic weighted information

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Technical communique: Distributed optimal component fusion weighted by scalars for fixed-lag Kalman smoother

Automatica (Journal of IFAC)
Multi-sensor optimal information fusion Kalman filter

Automatica (Journal of IFAC)

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper is concerned with H∞ information fusion filtering for discrete-time systems with time-delay sensors based on re-organization of innovation. The key technique applied for deriving the filter is the ro-organized innovation analysis approach in Krein space. The H∞ filter is given in terms of solutions of Riccati matrix differential equations. It is the first time to present the result of H∞ information fusion for linear discrete-time systems with delayed sensors. In the fusion center, It only requires computing scalar weights, so the calculation burden can be reduced. while this method does not requires that the parameters of the system and the statistics of the noises are assumed to be known exactly. Thus the proposed approach is much more computational attractive.