Derivation of fixed-interval smoothing algorithm using covariance information in distributed parameter systems

This paper proposes a recursive least mean squared error fixed-interval smoothing algorithm in distributed parameter systems. It is assumed that the state-space model of the signal to be estimated is unknown, and the algorithm only requires the second-order moments of the signal and the white noise...

Full description

Saved in:
Bibliographic Details
Published in:Applied mathematics and computation 2006-05, Vol.176 (2), p.662-672
Main Authors: Nakamori, S., García-Ligero, M.J., Hermoso-Carazo, A., Linares-Pérez, J.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Covariance information
Distributed parameter systems
Exact sciences and technology
Fixed-interval smoothing
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical approximation
Pattern recognition. Digital image processing. Computational geometry
Probability and statistics
Probability theory and stochastic processes
Recursive estimation
Sciences and techniques of general use
Special processes (renewal theory, markov renewal processes, semi-markov processes, statistical mechanics type models, applications)
Wiener–Hopf equation
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:This paper proposes a recursive least mean squared error fixed-interval smoothing algorithm in distributed parameter systems. It is assumed that the state-space model of the signal to be estimated is unknown, and the algorithm only requires the second-order moments of the signal and the white noise perturbing its observations. Practical application of the proposed algorithm is illustrated with a restoration image problem.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2005.10.012