Compressive Sensing Based Robust Signal Sampling


  •  Lianlin Li    
  •  Fang Li    

Abstract

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.


This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1916-9639
  • ISSN(Online): 1916-9647
  • Started: 2009
  • Frequency: semiannual

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