Title :
QR-based algorithms for multichannel adaptive least squares lattice filters
Author_Institution :
Los Alamos Nat. Lab., NM, USA
fDate :
3/1/1990 12:00:00 AM
Abstract :
A pair of multichannel least-squares lattice filter algorithms is presented. Each m-channel filter stage is numerically stable and computationally efficient, with a computational complexity of O (m2). Both algorithms are based on the recursive QR decomposition of the forward and backward error matrices in each filter stage. The first algorithm uses orthogonal Givens rotations to compute the QR decomposition. The second algorithm uses fast Givens rotations for greater efficiency. Simulation results are presented, as well as an example of the algorithms´ application in the enhancement of magnetoencephalographic signals
Keywords :
adaptive filters; computational complexity; digital filters; filtering and prediction theory; least squares approximations; signal processing; adaptive filters; computational complexity; computationally efficient; fast Givens rotations; magnetoencephalographic signals; multichannel least-squares lattice filter; numerically stable; orthogonal Givens rotations; recursive QR decomposition; Adaptive filters; Computational complexity; Computational modeling; Lattices; Least squares approximation; Least squares methods; Matrix decomposition; Nonlinear filters; Resonance light scattering; Signal processing algorithms;
Journal_Title :
Acoustics, Speech and Signal Processing, IEEE Transactions on
