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Proceedings Paper

Systolic Kalman Filtering Based On QR Decomposition
Author(s): M. J. Chen; K. Yao
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Paper Abstract

In this paper, by using the matrix decomposition method, the Kalman filter can be formulated as a modified SRIF data processing problem followed by a QR operation. Compared with the conventional SRIF method, this approach simplifies the computational structure, and is more reliable when the system has a singular(or near singular) coefficient matrix. By skewing the order of input matrices, fully pipelined systolic2Kalman filtering operation can be achieved. With the number of processing units of the 0(n ), the system throughput rate is of the 0(n). The numerical properties of the systolic Kalman filtering algorithm under finite word length effect are studied via analysis and computer simulations, and are compared with those of conventional approaches.

Paper Details

Date Published: 21 January 1988
PDF: 8 pages
Proc. SPIE 0826, Advanced Algorithms and Architectures for Signal Processing II, (21 January 1988); doi: 10.1117/12.942011
Show Author Affiliations
M. J. Chen, University of California (United States)
K. Yao, University of California (United States)

Published in SPIE Proceedings Vol. 0826:
Advanced Algorithms and Architectures for Signal Processing II
Franklin T. Luk, Editor(s)

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