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Adversarial aircraft diversion and interception using missile herding techniques
Author(s): Ryan A. Licitra; Andrew J. Neale; Emily A. Doucette; Jess W. Curtis
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Paper Abstract

When being pursued by guided munitions, a fixed wing aircraft is likely to attempt to avoid interception. If a team of autonomous missiles can learn how their motion affects the induced motion of their target, the exploitation of this knowledge can facilitate controlled diversion and interception of the target. Motivated by recent advances in the field of herding control, this paper details a novel control and estimation strategy for a team of missiles tasked with diverting a target aircraft from its planned path and intercepting it somewhere on a prescribed “safe" trajectory. A neural network-based estimation scheme is used to approximate the uncertain missile-target interactions online. The missile controllers leverage these estimates to ensure that the diversion and interception objectives are achieved. A rigorous Lyapunov-based analysis examines the stability of the closed loop error system.

Paper Details

Date Published: 13 May 2019
PDF: 9 pages
Proc. SPIE 10982, Micro- and Nanotechnology Sensors, Systems, and Applications XI, 1098229 (13 May 2019); doi: 10.1117/12.2519148
Show Author Affiliations
Ryan A. Licitra, Univ. of Florida (United States)
Andrew J. Neale, Air Force Research Lab. (United States)
Emily A. Doucette, Air Force Research Lab. (United States)
Jess W. Curtis, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 10982:
Micro- and Nanotechnology Sensors, Systems, and Applications XI
Thomas George; M. Saif Islam, Editor(s)

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