
Accession Number : ADA191756
Title : Signal Processing Computational Needs.
Descriptive Note : Professional paper for period ending Aug 86,
Corporate Author : NAVAL OCEAN SYSTEMS CENTER SAN DIEGO CA
Personal Author(s) : Speiser, Jeffrey M
PDF Url : ADA191756
Report Date : Nov 1987
Pagination or Media Count : 6
Abstract : Previous reviews of signal processing computational needs and their systolic implementation have emphasized the need for a small set of matrix operations, primarily matrix multiplication, orthogonal triangularization, triangular backsolve, singular value decomposition, and the generalized singular value decomposition. Algorithms and architectures for these tasks are sufficiently well understood to begin transitioning from search to exploratory development. Substantial progress has also been reported on parallel algorithms for updating symmetric eigensystems and the singular value decomposition. Another problem which has proved to be easier than expected is inner product computation for highspeed high resolution predictive analogtodigital conversion. Although inner product computation in a general setting will require O(log n) time via a tree, the special structure of the prediction permits the use of a systolic transversal filter, producing a new predicted value in time O(1). Problem areas which are still in an early of study include parallel algorithms for the WignerVille Distribution function, L1 norm approximation, inequality constrained least squares, and the total least squares problem. Keywords: Covariance matrix.
Descriptors : *ALGORITHMS, *COMPUTATIONS, *LEAST SQUARES METHOD, *SIGNAL PROCESSING, DECOMPOSITION, PARALLEL PROCESSING, SEARCHING
Subject Categories : Computer Programming and Software
Numerical Mathematics
Distribution Statement : APPROVED FOR PUBLIC RELEASE