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Proceedings Paper

Monopulse radar tracking using an adaptive interacting multiple-model method with extended Kalman filters
Author(s): Jeffery R. Layne
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Paper Abstract

In this paper we investigate an adaptive interacting multiple model (AIMM) tracker using the extended Kalman filter. This adaptive algorithm is based on the interacting multiple model (IMM) tracking technique with the addition of an adaptive acceleration model to track behavior that falls in between the fixed model dynamics. In previous research, we found that the adaptive model matches more closely the true system dynamics when the target kinematics lie in between the fixed models, thus improving the overall performance of the tracking system. We also showed that the AIMM outperforms other existing adaptive approaches while reducing computational complexity. In this paper, we further investigate these superior qualities of the AIMM by considering a more realistic radar-tracking scenario where monopulse radar range, azimuth, and elevation measurements are processed using extended Kalman filters in the AIMM. Here a more complex 3D simulation is implemented instead of the simplified 2D problem considered in our previous research. Again, the result how that the AIMM outperforms the classical IMM when the target is maneuvering.

Paper Details

Date Published: 3 September 1998
PDF: 12 pages
Proc. SPIE 3373, Signal and Data Processing of Small Targets 1998, (3 September 1998); doi: 10.1117/12.324625
Show Author Affiliations
Jeffery R. Layne, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 3373:
Signal and Data Processing of Small Targets 1998
Oliver E. Drummond, Editor(s)

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