Title :
Bi-iteration SVD subspace tracking algorithms
Author_Institution :
Fachhochschule Furtwangen, Germany
fDate :
5/1/1997 12:00:00 AM
Abstract :
We present a class of fast subspace tracking algorithms that arise from a straightforward extension of Bauer´s (1957) classical bi-iteration to the sequential processing case. The bi-iteration concept has an unexpected potential in subspace tracking. Our new bi-SVD subspace trackers are well structured and show excellent convergence properties. They outperform the TQR-SVD subspace tracking algorithm. Detailed comparisons confirm our claims. An application to rank and data adaptive signal reconstruction is also discussed
Keywords :
adaptive filters; adaptive signal processing; convergence of numerical methods; filtering theory; iterative methods; signal reconstruction; singular value decomposition; tracking; TQR-SVD subspace tracking algorithm; bi-iteration SVD subspace tracking algorithms; convergence properties; data adaptive signal reconstruction; fast subspace tracking algorithms; rank adaptive signal reconstruction; rank adaptive subspace filtering; sequential processing; Adaptive signal processing; Approximation algorithms; Array signal processing; Convergence; Frequency estimation; Parameter estimation; Signal detection; Signal processing; Signal processing algorithms; Signal reconstruction;
Journal_Title :
Signal Processing, IEEE Transactions on
