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Compressive Measurement for Target Tracking in Persistent, Pervasive Surveillance Applications
September 2009
Michael E. Gehm, University of Arizona
Michael D. Stenner, The MITRE Corporation
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 di erences are sparse. The second methodology is a 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.
Additional Search Keywords
motion tracking, group testing, compressive sensing
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