Abstract—A sequential adaptive compressed sensing procedure for signal support recovery is proposed and analyzed. The procedure is based.
Mar 1, 2010 · Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of random measurements.
It is shown that adaptive compressed sensing enables recovery of weaker sparse signals than those that can be recovered using traditional non-adaptive ...
It is shown that adaptive compressed sensing enables recovery of weaker sparse signals than those that can be recovered using traditional non-adaptive ...
Abstract—Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of random measurements.
A sequential adaptive compressed sensing procedure for signal support recovery is proposed and analyzed. The procedure is based on the principle of ...
Jul 2, 2014 · We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the ...
Abstract—Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of random measurements.
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We propose a methodology for sequential sensor placement inspired from the Bayesian compressive sensing framework introduced by Ji et al.
It is shown that adaptive compressed sensing enables recovery of weaker sparse signals than those that can be recovered using traditional non-adaptive ...