The Sparsity and Incoherence in Compressive Sensing as Applied to Field Reconstruction

Compressive Sensing (CS) opens up new perspectives for field reconstruction. Electromagnetic far field can be reconstructed by CS from a reduced number of samples when no prior knowledge of the radiating source is available with large probability as long as two constraints are satisfied, that is sparsity and incoherence. A set of representative numerical examples considering different sampling strategy and different recover algorithms is reported and discussed to demonstrate that when CS condition holds true, a perfect reconstruction with large probability is guaranteed.