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Proceedings Paper

Unification of detection, tracking, and recognition for millimeter wave and infrared sensors
Author(s): Aaron D. Lanterman; Michael I. Miller; Donald L. Snyder
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Paper Abstract

Our pattern theoretic approach to the automated understanding of complex scenes brings the traditionally separate endeavors of detection, tracking, and recongition together into a unified jump-diffusion process. Concentrating on an air-to-ground scenario, we postulate data likelihood models for a low-resolution, wide field-of-view millimeter wave radar (for detection) and a high-resolution, narrow field-of-view forward-looking infrared sensor (for recognition). The interaction between the sensors is governed by a jump-diffusion process which provides a mathematical foundation for saccadic detection and computationally efficient target hypothesis during recognition. New objects are detected and object types are recognition through discrete jump moves. Between jumps, the location and orientation of objects are estimated via continuous diffusions. The methodology outlined may be applied to any scenario involving the fusion of low-resolution and high-resolution sensor data.

Paper Details

Date Published: 18 August 1995
PDF: 12 pages
Proc. SPIE 2562, Radar/Ladar Processing and Applications, (18 August 1995); doi: 10.1117/12.216951
Show Author Affiliations
Aaron D. Lanterman, Washington Univ. (United States)
Michael I. Miller, Washington Univ. (United States)
Donald L. Snyder, Washington Univ. (United States)


Published in SPIE Proceedings Vol. 2562:
Radar/Ladar Processing and Applications
William J. Miceli, Editor(s)

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