Title :
A note on minimax estimation and Kalman filtering
Author_Institution :
Yale University, New Haven, CN, USA
fDate :
10/1/1969 12:00:00 AM
Abstract :
A minimax estimator for the state of a discrete time-varying linear plant with an a priori unknown control sequence is developed. No statistics for the sequence of control vectors are assumed to exist. It is assumed, however, that the a priori unknown control sequence can be measured in additive Gaussian noise with zero mean and known variance.
Keywords :
Kalman filtering; Linear systems, time-varying discrete-time; Minimax estimation; State estimation; Additive noise; Covariance matrix; Filtering; Kalman filters; Minimax techniques; Noise measurement; Nonlinear filters; State estimation; Statistics; Vectors;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.1969.1099239
