Sciweavers

Share
TSP
2010

Efficient recursive estimators for a linear, time-varying Gaussian model with general constraints

11 years 6 days ago
Efficient recursive estimators for a linear, time-varying Gaussian model with general constraints
The adaptive estimation of a time-varying parameter vector in a linear Gaussian model is considered where we a priori know that the parameter vector belongs to a known arbitrary subset. We consider a family of efficient recursive estimators for this problem: the recursive constrained maximum likelihood (ML) estimator, the recursive affine minimax, and the recursive minimum mean squared error (MMSE) estimator. We show that all three estimators can be substantially simplified by using the recursive weighted least squares (RWLS) algorithm in a first step as the RWLS computes the sufficient statistic for this estimation problem. The recursive constrained ML needs to solve an optimization problem in the second step for the case that the RWLS solution does not fulfill the constraint. In case of affine minimax, we have to solve an optimization problem and to perform an affine transform. The MMSE estimator needs to calculate the mean of a truncated Gaussian density in the second step which is...
Stefan Uhlich, Bin Yang
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TSP
Authors Stefan Uhlich, Bin Yang
Comments (0)
books