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Proceedings Paper

A Factorized Extended Kalman Filter
Author(s): James L. Fisher; David P. Casasent; Charles P. Neuman
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Paper Abstract

Kalman filtering represents formidable computational linear algebra requirements for each new input measurement vector. The air-to-air missile guidance problem is addressed for which an extended Kalman filter (EKF) is required because the measurements are nonlinear in Cartesian coordinates. An explicit formulation is used. At the outset, we discretize the system dynamics and measurement model and incorporate a discrete-time EKF. A factorized L D LT algorithm is used to propagate the covariance matrices between sample times. A simulation analysis of the number of data bits required in the computations is provided. Comparison with other EKF algorithms shows that this method requires only 18 bit accuracy (compared to 32-40 bits for other methods). Quantitative position, velocity and acceleration estimates obtained for a highly maneuverable target are presented. A high-accuracy floating point optical processor is presented that is capable of computing the full EKF to allow a new measurement update each msec.

Paper Details

Date Published: 4 January 1986
PDF: 12 pages
Proc. SPIE 0564, Real-Time Signal Processing VIII, (4 January 1986); doi: 10.1117/12.949713
Show Author Affiliations
James L. Fisher, Carnegie-Mellon University (United States)
David P. Casasent, Carnegie-Mellon University (United States)
Charles P. Neuman, Carnegie-Mellon University (United States)


Published in SPIE Proceedings Vol. 0564:
Real-Time Signal Processing VIII
Keith Bromley; William J. Miceli, Editor(s)

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