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Proceedings Paper

Compressive measurement for target tracking in persistent, pervasive surveillance applications
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Paper Abstract

Motion tracking in persistent surveillance applications enters an interesting regime when the movers are of a size on the order of the image resolution elements or smaller. In this case, for reasonable scenes, information about the movers is a natively sparse signal - in an observation of a scene at two closely separated time-steps, only a small number of locations (those associated with the movers) will have changed dramatically. Thus, this particular application is well-suited for compressive sensing techniques that attempt to efficiently measure sparse signals. Recently, we have been investigating two different approaches to compressive measurement for this application. The first, differential Combinatorial Group Testing (dCGT), is a natural extension of group testing ideas to situations where signal differences are sparse. The second methodology is an ℓ-1-minimization based recovery approach centered on recent work in random (and designed) multiplex sensing. In this manuscript we will discuss these methods as they apply to the motion tracking problem, discuss various performance limits, present early simulation results, and discuss notional optical architectures for implementing a compressive measurement scheme.

Paper Details

Date Published: 24 August 2009
PDF: 8 pages
Proc. SPIE 7468, Adaptive Coded Aperture Imaging, Non-Imaging, and Unconventional Imaging Sensor Systems, 746808 (24 August 2009); doi: 10.1117/12.827048
Show Author Affiliations
Michael E. Gehm, The Univ. of Arizona (United States)
College of Optical Sciences, The Univ. of Arizona (United States)
Michael D. Stenner, MITRE Corp. (United States)

Published in SPIE Proceedings Vol. 7468:
Adaptive Coded Aperture Imaging, Non-Imaging, and Unconventional Imaging Sensor Systems
David P. Casasent; Jean J. Dolne; Stanley Rogers; Thomas J. Karr; Victor L. Gamiz, Editor(s)

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