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A class of least-squares filtering and identification algorithms with systolic array architecturesA unified approach is presented for deriving a large class of new and previously known time- and order-recursive least-squares algorithms with systolic array architectures, suitable for high-throughput-rate and VLSI implementations of space-time filtering and system identification problems. The geometrical derivation given is unique in that no assumption is made concerning the rank of the sample data correlation matrix. This method utilizes and extends the concept of oblique projections, as used previously in the derivations of the least-squares lattice algorithms. Exponentially weighted least-squares criteria are considered for both sliding and growing memory.
Document ID
19910040398
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Kalson, Seth Z.
(MIT Lexington, MA, United States)
Yao, Kung
(California, University Los Angeles, United States)
Date Acquired
August 14, 2013
Publication Date
January 1, 1991
Publication Information
Publication: IEEE Transactions on Information Theory
Volume: 37
ISSN: 0018-9448
Subject Category
Cybernetics
Accession Number
91A25021
Funding Number(s)
CONTRACT_GRANT: NSF NCR-88-14407
CONTRACT_GRANT: NAG2-304
Distribution Limits
Public
Copyright
Other

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