Sparse basis expansions for compressive sensing of electromagnetic scattering patterns computed using iterative physical optics

Compressive sensing theory is applied to reduce the amount of data needed for reconstructing an RCS pattern generated using computational electromagnetics software. The data is generated by the iterative physical optics method for a scale-model generic tank at X-band. It is shown that the number of samples needed is considerably lower than the Nyquist requirement if the RCS pattern may be represented in a sparse basis. Here, the sparse basis is the set of point scattering functions that represent the dominant scattering centers of the target.