A hardware implementation of Compressive Sensing Theory

In this paper, the new theory of compressive sensing (CS) that unifies signal sensing and compression into a single task is implemented on a Digital Signal Processing (DSP) board. An iterative algorithm for signal reconstruction known as Matching Pursuit is implemented on the DSP and used to the reconstruction of real signals from a reduced set of random projections. Two kinds of validation procedures are used to test the reconstruction algorithm implemented. More precisely, sparse signals synthesized on the DSP and sparse signals generated by a special-purpose generator are used to experimentally test the compressive sensing theory verifying in this way its potential. It is shown that the CS theory is able to recover the most significant values of the underlying signal, while yielding negligible differences between the original signals and the reconstructed ones.