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Vectorization of linear discrete filtering algorithmsLinear filters, including the conventional Kalman filter and versions of square root filters devised by Potter and Carlson, are studied for potential application on streaming computers. The square root filters are known to maintain a positive definite covariance matrix in cases in which the Kalman filter diverges due to ill-conditioning of the matrix. Vectorization of the filters is discussed, and comparisons are made of the number of operations and storage locations required by each filter. The Carlson filter is shown to be the most efficient of the filters on the Control Data STAR-100 computer.
Document ID
19770021849
Acquisition Source
Legacy CDMS
Document Type
Technical Memorandum (TM)
Authors
Schiess, J. R.
(NASA Langley Research Center Hampton, VA, United States)
Date Acquired
September 3, 2013
Publication Date
July 1, 1977
Subject Category
Mathematical And Computer Sciences (General)
Report/Patent Number
L-11448
NASA-TM-X-3527
Accession Number
77N28793
Funding Number(s)
PROJECT: RTOP 505-15-37-01
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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