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Proceedings Paper

A Linear Array For Covariance Differencing Via Hyperbolic SVD
Author(s): A. Bojanczyk; A. Steinhardt
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Paper Abstract

We consider a problem pertaining to bearing estimation in unknown noise using the covariance differencing approach, and propose a linear array of processors which exhibits a linear speed-up with respect to a uniprocessor system. Our solution hinges on a new canonic matrix factorization which we term the hyperbolic singular value decomposition. The parallel algorithm for hyperbolic SVD based bearing estimation is an adaptation of a well known biorthogonalization technique developed by Hestenes. Parallel implementations of the algorithm are based on earlier works on one-sided Jacobi methods. It turns out that strategies for parallelization of Jacobi methods are equally well applicable for computing the hyperbolic singular value decomposition.

Paper Details

Date Published: 14 November 1989
PDF: 5 pages
Proc. SPIE 1152, Advanced Algorithms and Architectures for Signal Processing IV, (14 November 1989); doi: 10.1117/12.962269
Show Author Affiliations
A. Bojanczyk, Cornell University (United States)
A. Steinhardt, Cornell University (United States)

Published in SPIE Proceedings Vol. 1152:
Advanced Algorithms and Architectures for Signal Processing IV
Franklin T. Luk, Editor(s)

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