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Proceedings Paper

Systolic array for Kalman filtering with algorithm-based fault tolerance
Author(s): William F. Mitchell
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Paper Abstract

The Kalman filter is an optimal recursive estimator for linear dynamic systems with white noise. Many digital signal processing applications require the real time computation of the Kalman filter e. g. tracking an aircraft with radar. Consequently many papers have been published on the use of parallel processing technology in the form of systolic arrays for fast computation of the Kalman filter. This paper presents a new systolic array implementation of the Kalman filter that is not excessive in either hardware or computation steps. For a dynamic system with N states and M observation components the array uses N(N-i-1) processors and about 4N+6M computation steps. In some applications it is also required that the processing system continue to function even after some of the components of the system fail. The Kalman filter systolic array is extended to one that is tolerant of faults in the processing elements of the array by using techniques of algorithm-based fault tolerance. Overhead for fault tolerance is about 47 additional hardware and 17 additional computational steps in the example of radar tracking.

Paper Details

Date Published: 1 November 1990
PDF: 12 pages
Proc. SPIE 1348, Advanced Signal Processing Algorithms, Architectures, and Implementations, (1 November 1990); doi: 10.1117/12.23498
Show Author Affiliations
William F. Mitchell, General Electric Co. (United States)


Published in SPIE Proceedings Vol. 1348:
Advanced Signal Processing Algorithms, Architectures, and Implementations
Franklin T. Luk, Editor(s)

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