Compressed sensing based multi-rate sub-Nyquist sampling system

Abstract Signal sampling is a vital component in modern information technology. As the signal bandwidth becomes wider, the sampling rate of analog-to-digital conversion (ADC) based on Shannon-Nyquist theorem is more and more high and may be beyond its capacity. However the analog to information converter (AIC) based on compressed sensing (CS) is designed to sample the analog signals at a sub-Nyquist sampling rate. A new multi-rate sub-Nyquist sampling (MSS) system was proposed in this article, it has one mixer, one integrator and several parallel ADCs with different sampling rates. Simulation shows the signals can be reconstructed in high probability even though the sampling rate is much lower than the Nyquist sampling rate.

[1]  Yonina C. Eldar,et al.  From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals , 2009, IEEE Journal of Selected Topics in Signal Processing.

[2]  Brian M. Sadler,et al.  A Sub-Nyquist Rate Sampling Receiver Exploiting Compressive Sensing , 2011, IEEE Transactions on Circuits and Systems I: Regular Papers.

[3]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[4]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[5]  Yoram Bresler,et al.  Perfect reconstruction formulas and bounds on aliasing error in sub-nyquist nonuniform sampling of multiband signals , 2000, IEEE Trans. Inf. Theory.

[6]  Gonzalo Mateos,et al.  Basis pursuit for spectrum cartography , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  S. Kirolos,et al.  Practical Issues in Implementing Analog-to-Information Converters , 2006, 2006 6th International Workshop on System on Chip for Real Time Applications.

[8]  Houjun Wang,et al.  Model-Based Multichannel Compressive Sampling with Ultra-Low Sampling Rate , 2012, Circuits Syst. Signal Process..

[9]  D. L. Donoho,et al.  Compressed sensing , 2006, IEEE Trans. Inf. Theory.