Journal ArticleDOI
Unbiased minimum-variance linear state estimation
TLDR
A method is developed for linear estimation in the presence of unknown or highly non-Gaussian system inputs so that the state update is determined so that it is unaffected by the unknown inputs.About:
This article is published in Automatica.The article was published on 1987-11-01. It has received 433 citations till now. The article focuses on the topics: Mean squared error & Minimum-variance unbiased estimator.read more
Citations
More filters
Journal ArticleDOI
Brief paper: Unbiased minimum-variance input and state estimation for linear discrete-time systems
Steven Gillijns,Bart De Moor +1 more
TL;DR: A recursive filter, optimal in the minimum-variance unbiased sense, is developed where the estimation of the state and the input are interconnected and the state estimation problem is transformed into a standard Kalman filtering problem.
Journal ArticleDOI
Technical communique: Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough
Steven Gillijns,Bart De Moor +1 more
TL;DR: Using linear minimum-variance unbiased estimation, a recursive filter is derived where the estimation of the state and the input are interconnected, based on the assumption that no prior knowledge about the dynamical evolution of the unknown input is available.
Journal ArticleDOI
A dual Kalman filter approach for state estimation via output-only acceleration measurements
TL;DR: In this article, a dual implementation of the Kalman filter for estimating the unknown input and states of a linear state-space model by using sparse noisy acceleration measurements is proposed, which avoids numerical issues attributed to unobservability and rank deficiency of the augmented formulation of the problem.
Journal ArticleDOI
Unbiased minimum variance estimation for systems with unknown exogenous inputs
TL;DR: A new method is developed for the state estimation of linear discrete-time stochastic systems in the presence of an unknown disturbance that is optimal in the unbiased minimum variance sense.
Journal ArticleDOI
Robust two-stage Kalman filters for systems with unknown inputs
TL;DR: A robust two-stage Kalman filter which is unaffected by the unknown inputs can be readily derived and serves as an alternative to the Kitanidis' (1987) unbiased minimum-variance filter.
References
More filters
Journal ArticleDOI
The intrinsic random functions and their applications
TL;DR: The intrinsic random functions (IRF) are a particular case of the Guelfand generalized processes with stationary increments and constitute a much wider class than the stationary RF, and are used in practical applications for representing nonstationary phenomena as discussed by the authors.
Journal ArticleDOI
On the identification of variances and adaptive Kalman filtering
TL;DR: In this paper, it was shown that the steady-state optimal Kalman filter gain depends only on n \times r linear functionals of the covariance matrix and the number of unknown elements in the matrix.
Journal ArticleDOI
A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems
Alan S. Willsky,Harold L. Jones +1 more
TL;DR: In this article, the authors consider a class of stochastic linear systems that are subject to jumps of unknown magnitudes in the state variables occurring at unknown times and devise an adaptive filtering system for the detection and estimation of the jumps.
Journal ArticleDOI
Kriging in the hydrosciences
TL;DR: In this article, the authors present a series of case-studies in automatic contouring, data input for numerical models, estimation of average precipitation over a given catchment area, and measurement network design.
Related Papers (5)
Brief paper: Unbiased minimum-variance input and state estimation for linear discrete-time systems
Steven Gillijns,Bart De Moor +1 more
Unbiased minimum variance estimation for systems with unknown exogenous inputs
Technical communique: Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough
Steven Gillijns,Bart De Moor +1 more