Research Article
LOW COMPLEXITY ADAPTIVE ALGORITHM FOR GENERALIZED EIGENVALUE DECOMPOSITION
@INPROCEEDINGS{10.1109/ChinaCom.2013.6694681, author={Feifei Gao and Rong Wang and Minli Yao and Hongxing Zou}, title={LOW COMPLEXITY ADAPTIVE ALGORITHM FOR GENERALIZED EIGENVALUE DECOMPOSITION}, proceedings={8th International Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2013}, month={11}, keywords={generalized eigenvalue decomposition colored noise weighted nonquadratic criterion}, doi={10.1109/ChinaCom.2013.6694681} }- Feifei Gao
Rong Wang
Minli Yao
Hongxing Zou
Year: 2013
LOW COMPLEXITY ADAPTIVE ALGORITHM FOR GENERALIZED EIGENVALUE DECOMPOSITION
CHINACOM
IEEE
DOI: 10.1109/ChinaCom.2013.6694681
Abstract
It is well known that the generalized eigenvalue decomposition (GEVD) can be used in a number of signal processing applications, for example, subspace tracking and estimation in the presence of colored noise. In this paper, we propose a new approach to extract the principle generalized eigenvectors (PGEs) for GEVD. Resorting to a weighted nonquadratic criterion (WNQC), the designed algorithm has a steep landscape, such that the desired point can be obtained from fast gradient-based method. Applying the projection approximation and recursive least squares (RLS) technique, we develop an adaptive algorithm with low computational complexity to parallelly estimate the PGEs. Finally, numerical results are provided to demonstrate the effectiveness of the proposed studies.