Distributed fusion receding horizon filtering for uncertain linear stochastic systems with time-delay sensors

A new distributed fusion receding horizon filtering problem is investigated for uncertain linear stochastic systems with time-delay sensors. First, we construct a local receding horizon Kalman filter having time delays (LRHKFTDs) in both the system and measurement models. The key technique is the de...

Full description

Saved in:
Bibliographic Details
Published in:Journal of the Franklin Institute 2012-04, Vol.349 (3), p.928-946
Main Authors: Young Song, Il, Shin, Vladimir, Jeon, Moongu
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Control theory. Systems
Derivation
Exact sciences and technology
Filtering
Filtration
Horizon
Mathematical analysis
Mathematical models
Modelling and identification
Sensors
Stochastic systems
System theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A new distributed fusion receding horizon filtering problem is investigated for uncertain linear stochastic systems with time-delay sensors. First, we construct a local receding horizon Kalman filter having time delays (LRHKFTDs) in both the system and measurement models. The key technique is the derivation of recursive error cross-covariance equations between LRHKFTDs in order to compute the optimal matrix fusion weights. It is the first time to present distributed fusion receding horizon filter for linear discrete-time systems with delayed sensors. It has a parallel structure that enables processing of multisensory time-delay measurements, so the calculation burden can be reduced and it is more reliable than the centralized version if some sensors turn faulty. Simulations for a multiple time-delays system show the effectiveness of the proposed filter in comparison with centralized receding horizon filter and non-receding versions. ► Deriving a new distributed fusion receding horizon filtering for uncertain linear stochastic systems with time-delay sensors. ► Derivation of recursive error cross-covariance equations between local receding horizon Kalman filters. ► It has a parallel structure that enables processing of multisensory time-delay measurements. ► Reducing calculation burden. ► It is more reliable than the centralized version if some sensors turn faulty.
ISSN:0016-0032
1879-2693
DOI:10.1016/j.jfranklin.2011.10.022