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14 November 1989 A Linear Array For Covariance Differencing Via Hyperbolic SVD
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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.
© (1989) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
A. Bojanczyk and A. Steinhardt "A Linear Array For Covariance Differencing Via Hyperbolic SVD", Proc. SPIE 1152, Advanced Algorithms and Architectures for Signal Processing IV, (14 November 1989); https://doi.org/10.1117/12.962269
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