Fixed-point smoothing with non-independent uncertainty using covariance information
DOI10.1080/00207720310001636390zbMath1079.93040OpenAlexW2079398051WikidataQ59552613 ScholiaQ59552613MaRDI QIDQ4653452
Raquel Caballero-Águila, Aurora Hermoso-Carazo, Josefa Linares-Pérez, Seiichi Nakamori
Publication date: 7 March 2005
Published in: International Journal of Systems Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207720310001636390
covariance matrixlinear stochastic systemsorthogonal projection lemmafixed-point smoothinginvariant imbedding.
Filtering in stochastic control theory (93E11) Least squares and related methods for stochastic control systems (93E24) Data smoothing in stochastic control theory (93E14)
Related Items (4)
Cites Work
- Optimal estimation of linear discrete-time systems with stochastic parameters
- Linear recursive state estimators under uncertain observations
- Recursive algorithms for linear LMSE estimators under uncertain observations
- New design of linear least-squares fixed-point smoother using covariance information in continuous systems
- Discrete linear recursive smoothing for systems with uncertain observations
- Design of a fixed-point smoother based on an innovations theory for white gaussian plus coloured observation noise
- Design of recursive fixed-point smoother using covariance information in linear discrete-time systems
- Discrete optimal linear smoothing for systems with uncertain observations
- Linear estimation for discrete-time systems in the presence of time-correlated disturbances and uncertain observations
- Linear minimum mean square error estimation for discrete-time Markovian jump linear systems
- Linear smoothing for discrete-time systems in the presence of correlated disturbances and uncertain observations
- Optimal recursive estimation with uncertain observation
- Recursive Bayesian estimation with uncertain observation (Corresp.)
This page was built for publication: Fixed-point smoothing with non-independent uncertainty using covariance information