Compressive Sensing Based Robust Signal Sampling

Signal processing methods have been changed substantially over the last several decades. Traditional sampling theorem of Shannon-Nyquist states that the sampling rate must be at least twice the maximum frequency presented in the signal; however, sampling at the Nyquist rate is inefficient because the signals of interest contain only a small number of significant frequencies relative to the band limit, although the locations of the frequencies may not be known a priori. Recently, compressive sensing (CS) has made a paradigmatic step in the way information is presented, stored, transmitted and recovered, by which we can acquire and reconstruct sparse signals from sub-Nyquist incoherent measurements. Three key ingredients of successfully implementing compressed sampling technique are sparsible/compressible probed signal, reliable hardware design, and low-cost computational algorithm. In this paper, we focused on two aspects about the robust sampling of sparsible/compressible signal, in particular, the design of compressed sampling hardware and the robust reconstruction via sparse Bayesian analysis. Primary results showed the high performance of proposed strategies.