Adaptive filter theory
A modular prewindowing framework for covariance FTF RLS algorithms
Signal Processing
Advanced Digital Signal Processing: Theory and Applications
Advanced Digital Signal Processing: Theory and Applications
Dynamically regularized fast RLS with application to echo cancellation
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
A fast least-squares algorithm for linearly constrained adaptivefiltering
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Sliding window adaptive fast QR and QR-lattice algorithms
IEEE Transactions on Signal Processing
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This paper presents new sliding window (SW) recursive least squares (RLS) and fast RLS algorithms for adaptive filtering with linear constraints. The algorithms are formulated for the general case of multichannel adaptive filters with complex-valued weights, and are based on Papaodysseus's matrix inversion lemma. The lemma is used for SW correlation matrix inversion and for the inversion of some other matrices that appear in constrained SW RLS algorithms. The algorithms have a form that can be implemented by means of at least two parallel processors. The proposed algorithms can be used for time-domain adaptive filtering of non-stationary signals, whilst restricting the frequency response of the filter to specific values at particular frequencies. In addition, the algorithms can be used in linearly constrained adaptive beamforming, dealing with non-stationary interference.