Compressive Sensing for Inverse Scattering
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Abstract
Compressive sensing is a new field in signal processing and applied mathematics. It allows one to simultaneously sample and compress signals which are known to have a sparse representation in a known basis or dictionary along with the subsequent recovery by linear programming (requiring polynomial (P) time) of the original signals with low or no error [1–3]. Compressive measurements or samples are non-adaptive, possibly random linear projections
Description
Compressive sensing is a new field in signal processing and applied mathematics. It allows one to simultaneously sample and compress signals which are known to have a sparse representation in a known basis or dictionary along with the subsequent recovery by linear programming (requiring polynomial (P) time) of the original signals with low or no error [1–3]. Compressive measurements or samples are non-adaptive, possibly random linear projections
Keywords
inverse scattering, signal processing, random linear projection, applied mathematics, compressive measurement, sparse representation, new field, known basis, compressive sensing, original signal, linear programming, subsequent recovery, compress signal , inverse scattering, signal processing, random linear projection, applied mathematics, compressive measurement, sparse representation, new field, known basis, compressive sensing, original signal, linear programming, subsequent recovery, compress signal