Paper Info
Title
Optimal State Estimation for Discrete-Time Markovian Jump Linear Systems, in the Presence of Delayed Output Observations.
Abstract
In this paper, we investigate an optimal state estimation problem for Markovian Jump Linear Systems. We consider that the state has two components: the first component of the state is finite valued and is denoted as mode, while the second (continuous) component is in a finite dimensional Eu- clidean space. The continuous state is driven by a deterministic control input and a zero mean, white and Gaussian process noise. The observable output has two components: the first is the mode delayed by a fixed amount and the second is a linear combination of the continuous state observed in zero mean white Gaussian noise. Our paradigm is to design optimal estimators for the current state, given the current output observation. We provide a solution to this paradigm by giving a recursive estimator of the continuous state, in the minimum mean square sense, and a finitely parameterized recursive scheme for computing the probability mass function of the current mode conditional on the observed output. We show that the optimal estimator is nonlinear on the observed output and on the control input. In addition, we show that the computation complexity of our recursive schemes is polynomial in the number of modes and exponential in the mode observation delay.
Year
DOI
Venue
2011
10.1109/ITW.2008.4578658
IEEE Trans. Automat. Contr.
Keywords
Field
DocType
Delay,Random processes,Equations,Linear systems,Mathematical model,Kalman filters,Sensors
Linear combination,Discrete mathematics,Applied mathematics,Linear system,Control theory,Mode (statistics),Gaussian process,Discrete time and continuous time,Gaussian noise,Additive white Gaussian noise,Mathematics,Estimator
Journal
Volume
Issue
ISBN
56
9
978-1-4244-2271-5
Citations 
PageRank 
References 
5
0.54
7
Authors
3
Name
Order
Citations
PageRank
Ion Matei114913.66
Nuno C. Martins240836.23
John S. Baras31953257.50