Fast, rank adaptive subspace tracking and applications

Autor:	Daniel J. Rabideau
Rok vydání:	1996
Předmět:	Recursive least squares filter Adaptive filter Signal processing Theoretical computer science Computational complexity theory Rank (linear algebra) Signal Processing Orthonormal basis Electrical and Electronic Engineering Algorithm Subspace topology Mathematics Signal subspace
Zdroj:	IEEE Transactions on Signal Processing. 44:2229-2244
ISSN:	1941-0476 1053-587X
Popis:	High computational complexity and inadequate parallelism have deterred the use of subspace-based algorithms in real-time systems. We proposed a new class of fast subspace tracking (FST) algorithms that overcome these problems by exploiting the matrix structure inherent in multisensor processing. These algorithms simultaneously track an orthonormal basis for the signal subspace and preserve signal eigenstructure information while requiring only O(Nr) operations per update (where N is the number of channels, and r is the effective rank). Because of their low computational complexity, these algorithms have applications in both recursive and block data processing. Because they preserve the signal eigenstructure as well as compute an orthonormal basis for the signal subspace, these algorithms may be used in a wide range of sensor array applications including bearing estimation, beamforming, and recursive least squares. We present a detailed description of the FST algorithm and its rank adaptive variation (RA-FST) as well as a number of enhancements. We also demonstrate the FST's rapid convergence properties in a number of application scenarios.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::69517f6be49b8c053c466aa182345565 https://doi.org/10.1109/78.536680 Zobrazit plný text záznamu