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Recursive least squares estimation and Kalman filtering by systolic arraysOne of the most promising new directions for high-throughput-rate problems is that based on systolic arrays. In this paper, using the matrix-decomposition approach, a systolic Kalman filter is formulated as a modified square-root information filter consisting of a whitening filter followed by a simple least-squares operation based on the systolic QR algorithm. By proper skewing of the input data, a fully pipelined time and measurement update systolic Kalman filter can be achieved with O(n squared) processing cells, resulting in a system throughput rate of O (n).
Document ID
19890038498
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Chen, M. J.
(California Univ. Los Angeles, CA, United States)
Yao, K.
(California, University Los Angeles, United States)
Date Acquired
August 14, 2013
Publication Date
January 1, 1988
Subject Category
Cybernetics
Meeting Information
Meeting: International Conference on Advances in Communication and Control Systems
Location: Washington, DC
Country: United States
Start Date: June 18, 1987
End Date: June 20, 1987
Sponsors: NASA, Intl. Federation of Information Processing Societies
Accession Number
89A25869
Funding Number(s)
CONTRACT_GRANT: NAG2-304
Distribution Limits
Public
Copyright
Other

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