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Proceedings Paper

Architecture for adaptive eigenstructure decomposition based on systolic QRD
Author(s): Simha Erlich; Kung Yao
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Paper Abstract

Eigenstructure decomposition of correlation matrices is an important pre-processing stage in many modern signal processing applications. In an unknown and possibly changing environment, adaptive algorithms that are efficient and numerically stable as well as readily implementable in hardware for eigendecomposition are highly desirable. Most modern real- time signal processing applications involve processing large amounts of input data and require high throughput rates in order to fulfill the needs of tracking and updating. In this paper, we consider the use of a novel systolic array architecture for the high throughput on-line implementation of the adaptive simultaneous iteration method (SIM) algorithm for the estimation of the p largest eigenvalues and associated eigenvectors of quasi-stationary or slowly varying correlation matrices.

Paper Details

Date Published: 1 December 1991
PDF: 10 pages
Proc. SPIE 1565, Adaptive Signal Processing, (1 December 1991); doi: 10.1117/12.49764
Show Author Affiliations
Simha Erlich, Univ. of California/Los Angeles (United States)
Kung Yao, Univ. of California/Los Angeles (United States)

Published in SPIE Proceedings Vol. 1565:
Adaptive Signal Processing
Simon Haykin, Editor(s)

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