Extended Chandrasekhar recursions
- 1 March 1994
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Automatic Control
- Vol. 39 (3) , 619-623
- https://doi.org/10.1109/9.280773
Abstract
We extend the discrete-time Chandrasekhar recursions for least-squares estimation in constant parameter state-space models to a class of structured time-variant state-space models, special cases of which often arise in adaptive filtering. It can be shown that the much studied exponentially weighted recursive least-squares filtering problem can be reformulated as an estimation problem for a state-space model having this special time-variant structure. Other applications arise in the multichannel ...Keywords
This publication has 13 references indexed in Scilit:
- Chandrasekhar adaptive regularizer for adaptive filteringPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Lattice filter interpretations of the Chandrasekhar recursions for estimation and spectral factorizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1992
- Regularized fast recursive least squares algorithms for adaptive filteringIEEE Transactions on Signal Processing, 1991
- An efficient two-dimensional Chandrasekhar filter for restoration of images degraded by spatial blur and noiseIEEE Transactions on Acoustics, Speech, and Signal Processing, 1987
- Fast, recursive-least-squares transversal filters for adaptive filteringIEEE Transactions on Acoustics, Speech, and Signal Processing, 1984
- A fast sequential algorithm for least-squares filtering and predictionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1983
- Square-root algorithms for least-squares estimationIEEE Transactions on Automatic Control, 1975
- Dynamic estimation of air pollutionIEEE Transactions on Automatic Control, 1974
- Some new algorithms for recursive estimation in constant, linear, discrete-time systemsIEEE Transactions on Automatic Control, 1974
- A New Approach to Linear Filtering and Prediction ProblemsJournal of Basic Engineering, 1960