REAL-TIME IMPLEMENTATION OF THE KALMAN FILTER FOR TRAJECTORY ESTIMATION
STANFORD RESEARCH INST MENLO PARK CA
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The study addressed itself to the problem of real-time implementation of the Kalman filter for estimating ballistic trajectories. The Kalman filter is an extremely effective algorithm for estimation of ballistic trajectories, although the computational requirements of ballistic trajectories, although the computational requirements of the fully implemented Kalman filter are quite severe. In this report, several approaches that may be used to modify the Kalman filtering algorithm in order to reduce the computational requirements are described. The most promising approach of those considered is the piecewise- recursive Kalman filter. As shown by the numerical results obtained from extensive computer simulations, the piecewise-recursive Kalman filter can process measurements of a single target at a computational speed on the Univac 1108 that is about 20 to 25 times faster than real time for the endoatmospheric cases and about 500 times faster than real time for the exoatmospheric cases, and yet obtain estimation accuracy approaching that of the fully implemented Kalman filter. This increased filter capability is invaluable for the real-time estimation of multiple targets.
- Guided Missile Trajectories, Accuracy and Ballistics