Compressive Sensing for Inverse Scattering

Authors: Marengo, Edwin A.; Hernández, R. D.; Citron, Y. R.; Gruber, F. K.; Zambrano, M.; Lev-Ari, H.

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
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

Topic: 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