Linear recursive discrete-time estimators using covariance information under uncertain observations

This paper, using the covariance information, proposes recursive least-squares (RLS) filtering and fixed-point smoothing algorithms with uncertain observations in linear discrete-time stochastic systems. The observation equation is given by y( k)= γ( k) Hx( k)+ v( k), where { γ( k)} is a binary swit...

Full description

Saved in:

Bibliographic Details
Published in:	Signal processing Vol. 83; no. 7; pp. 1553 - 1559
Main Authors:	Nakamori, Seiichi, Caballero-Águila, Raquel, Hermoso-Carazo, Aurora, Linares-Pérez, Josefa
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.07.2003 Elsevier Science
Subjects:	Applied sciences Covariance information Detection, estimation, filtering, equalization, prediction Exact sciences and technology Information, signal and communications theory Linear discrete-time systems Recursive estimation Services and terminals of telecommunications Signal and communications theory Signal, noise Stochastic process Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Telemetry. Remote supervision. Telewarning. Remote control Transmission and modulation (techniques and equipments) Wiener-Hopf equation Covariance information Wiener-Hopf equation Linear discrete-time systems Stochastic process Recursive estimation Filtering Probabilistic approach Information system Discrete time systems Data transmission Probability distribution Conditional probability Algorithm Modeling Remote sensing Binary sequence Covariance Least squares method Discrete time White noise Recursive method Multiple channel Matrix system Fixed point
ISSN:	0165-1684, 1872-7557
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!