Linear estimation based on covariances for networked systems featuring sensor correlated random delays

R. Caballero-Águila, A. Hermoso-Carazo and J. Linares-Pérez

International Journal of Systems Science, 2013, vol. 44, issue 7, 1233-1244

Abstract: This article is concerned with the least-squares (LS) linear estimation problem of discrete-time signals from noisy measurements coming from multiple randomly delayed sensors with different delay characteristics. It is assumed that the Bernoulli random variables characterising the measurement delays are correlated at consecutive sampling times. Using an innovation approach, recursive linear filtering and smoothing (fixed-point and fixed-interval) algorithms are obtained without requiring the state-space model generating the signal, but only the covariance functions of the signal and the noise, the delay probabilities and the correlation function of the Bernoulli variables. Also, recursive formulas for filtering and fixed-point smoothing error covariance matrices are obtained to measure the goodness of the proposed estimators.

Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2012.659709 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tsysxx:v:44:y:2013:i:7:p:1233-1244

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20

DOI: 10.1080/00207721.2012.659709

Access Statistics for this article

International Journal of Systems Science is currently edited by Visakan Kadirkamanathan

More articles in International Journal of Systems Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().