Adaptive Kalman Filtering in Networked Systems With Random Sensor Delays, Multiple Packet Dropouts and Missing Measurements

Author

Moayedi, Maryam ; Foo, Yung K. ; Soh, Yeng C.

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore

Volume

58

Issue

3

fYear

2010

fDate

3/1/2010 12:00:00 AM

Firstpage

1577

Lastpage

1588

Abstract

In this paper, adaptive filtering schemes are proposed for state estimation in sensor networks and/or networked control systems with mixed uncertainties of random measurement delays, packet dropouts and missing measurements. That is, all three uncertainties in the measurement have certain probability of occurrence in the network. The filter gains can be derived by solving a set of recursive discrete-time Riccati equations. Examples are presented to demonstrate the applicability and performances of the proposed schemes.

Keywords

Riccati equations; adaptive Kalman filters; delays; discrete time systems; distributed control; distributed sensors; state estimation; telecommunication control; adaptive Kalman filtering; missing measurements; multiple packet dropouts; networked control systems; random measurement delays; random sensor delays; recursive discrete-time Riccati equations; sensor networks; state estimation; Kalman filtering; minimum mean-square error estimation; missing measurements; networked control systems (NCSs); packet dropouts; sensor delays; sensor networks (SNs);

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/TSP.2009.2037853

Filename

5345802