A state-space approach to adaptive filtering

Authors:
Ali H. Sayed;Thomas Kailath
Affiliations:
Information Systems Laboratory, Stanford University, Stanford, CA;Information Systems Laboratory, Stanford University, Stanford, CA
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: digital speech processing - Volume III
Year:
1993

Citing 3
Cited 0

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Fast algorithms for fixed-order recursive least squares parameter estimation

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Abstract

We describe a unified square-root-based derivation of adaptive filtering schemes that is based on reformulating the original problem as a state-space linear least-squares estimation problem. In this process we encounter rich connections with algorithms that have been long established in linear least-squares estimation theory such as the Kalman filter, the Chandrasekhar filter, and the information forms of the Kalman and Chandrasekhar algorithms. The approach also suggests some generalizations and extensions of classical results.