Title :
James-Stein state space filter
Author :
Manton, Jonathan H. ; Krishmamurthy, V. ; Poor, H. Vincent
Author_Institution :
Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
Abstract :
In 1961, James and Stein discovered a remarkable estimator which dominates the maximum-likelihood estimate of the mean of a p-variate normal distribution, provided the dimension p is greater than two. This paper, by applying “James-Stein estimation theory”, derives the James-Stein state filter (JSSF), which is a robust version of the Kalman filter. The JSSF is designed for situations where the parameters of the state-space evolution model are not known with any certainty
Keywords :
Kalman filters; filtering theory; maximum likelihood estimation; normal distribution; parameter estimation; state estimation; state-space methods; James-Stein estimation theory; James-Stein state space filter; maximum-likelihood estimate; p-variate normal distribution; state-space evolution model; Estimation theory; Filtering theory; Filters; Gaussian distribution; Mathematical model; Maximum likelihood estimation; Robustness; Signal processing; State estimation; State-space methods;
Conference_Titel :
Decision and Control, 1997., Proceedings of the 36th IEEE Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-4187-2
DOI :
10.1109/CDC.1997.652382
