Convergence properties of Gram-Schmidt and SMI adaptive algorithms. II

Author

Gerlach, Karl ; Kretschmer, Frank F., Jr.

Author_Institution

US Naval Res. Lab., Washington, DC, USA

Volume

27

Issue

1

fYear

1991

fDate

1/1/1991 12:00:00 AM

Firstpage

83

Lastpage

91

Abstract

For pt.I see ibid., vol.26, no.1, p.44-56, Jan. 1990. Theorems and relationships associated with the convergence rate of the Gram-Schmidt (GS) and sampled matrix inversion (SMI) algorithms are presented. Two forms of the GS canceler are discussed: concurrent block processing and sliding window processing. It is shown (as has been stated by other researchers) that the concurrent block processed GS canceler converges rapidly to its optimal signal-to-noise ratio. However, it is also shown that the result is deceptive in that the output residue samples may be highly correlated, which would significantly degrade postdetection processing. It is demonstrated that a specific form of a sliding window GS canceler has the same convergence properties as the concurrent block processed GS canceler

Keywords

convergence of numerical methods; estimation theory; interference suppression; matrix algebra; signal detection; signal processing; Gram Schmidt algorithm; concurrent block processing; convergence; matrix transform; optimal signal-to-noise ratio; postdetection processing; sampled matrix inversion algorithms; sliding window processing; Adaptive algorithm; Adaptive arrays; Convergence; Covariance matrix; Degradation; Gaussian noise; Laboratories; Noise cancellation; Signal to noise ratio; State estimation;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.68150

Filename

68150