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This paper investigates the problem of recovering the support of structuredsignals via adaptive compressive sensing. We examine several classes ofstructured support sets, and characterize the fundamental limits of accuratelyrecovering such sets through compressive measurements, while simultaneouslyproviding adaptive support recovery protocols that perform near optimally forthese classes. We show that by adaptively designing the sensing matrix we canattain significant performance gains over non-adaptive protocols. These gainsarise from the fact that adaptive sensing can: (i) better mitigate the effectsof noise, and (ii) better capitalize on the structure of the support sets.

Adaptive Compressed Sensing for Support Recovery of Structured Sparse Sets