Title :
Inverse Scattering by Compressive Sensing and Signal Subspace Methods
Author :
Marengo, Edwin A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Northeastern Univ., Boston, MA
Abstract :
This work, composed of the present conference paper plus the associated talk at the conference, explores new paradigms for both active and passive target localization, imaging and inverse scattering that are based on both signal subspace and compressive sensing methods (being of particular interest the basis pursuit problem). The signal subspace component provides signal-subspace-based imaging methods applicable to spatially extended targets. The compressive sensing approach is developed as a recent alternative to the solution of a broad class of target parameter estimation problems. Our research program emphasizes certain inverse source and scattering problems, for which one has a priori knowledge on sparsity of the sources, scatterers and their fields, in physically- derived representational dictionaries for those signals. The derived theory and algorithms are illustrated with computer simulations (the full account of which is left for the talk).
Keywords :
image processing; pulse compression; scattering; active target localization; compressive sensing method; inverse scattering; passive target localization; signal subspace method; signal-subspace-based imaging methods; target parameter estimation problems; Computer simulation; Dictionaries; Frequency; Green function; Inverse problems; Multiple signal classification; Parameter estimation; Scattering; Sensor arrays; Waves; Compressive sensing; compressive imaging; imaging; localization; multiple signal classification; signal subspace; time reversal;
Conference_Titel :
Computational Advances in Multi-Sensor Adaptive Processing, 2007. CAMPSAP 2007. 2nd IEEE International Workshop on
Conference_Location :
St. Thomas, VI
Print_ISBN :
978-1-4244-1713-1
Electronic_ISBN :
978-1-4244-1714-8
DOI :
10.1109/CAMSAP.2007.4497977