DocumentCode :
844006
Title :
An efficient algorithm and systolic architecture for multiple channel adaptive filtering
Author :
Yuen, Stanley M. ; Abend, Kenneth ; Berkowitz, Raymond S.
Author_Institution :
RCAS Gov. Electron. Syst. Div., Moorestown, NJ, USA
Volume :
36
Issue :
5
fYear :
1988
fDate :
5/1/1988 12:00:00 AM
Firstpage :
629
Lastpage :
635
Abstract :
A multiple-input-multiple-output orthogonalization algorithm and its efficient systolic implementation are presented. The processing architecture is developed using a basic two-input-two-output decorrelation processing element as the primitive building block. Its features are discussed and compared to the approach of K. Gerlach and F.A. Studer (see ibid., vol.AP-34, no.3, p.458-462, 1986) which is based on the modified Gram-Schmidt (MGS) orthogonalization procedure. For simplicity of illustration in the development, batch processing is emphasized. The main features of the newly developed multiple-channel orthogonalization architecture are: (1) it requires no broadcasting of data and any given processing node in the structure only communicates with its neighboring nodes in pipelining fashion; (2) in terms of the total number of arithmetic operations, it is at least as efficient as the MGS approach; (3) the new architecture is developed in a systematic and bottom-up fashion; (4) it is an extremely regular and compact processing structure; (5) no unscrambling of the output channels is needed; and (6) the architecture presented places no restriction on the number of input channels
Keywords :
batch processing (computers); cellular arrays; correlation theory; digital filters; filtering and prediction theory; pipeline processing; batch processing; decorrelation processing element; multiple channel adaptive filtering; multiple-input-multiple-output orthogonalisation algorithm; pipelining; processing architecture; processing node; systolic architecture; systolic implementation; Adaptive filters; Convergence; Covariance matrix; Decorrelation; Filtering algorithms; Kalman filters; Least squares approximation; Least squares methods; Resonance light scattering; Signal processing algorithms;
fLanguage :
English
Journal_Title :
Antennas and Propagation, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-926X
Type :
jour
DOI :
10.1109/8.192139
Filename :
192139
Link To Document :
بازگشت