Quadrature compressive sampling of multiband radar signals at sub-Landau rate

Sampling multiband radar signals is an essential issue of multiband/multifunction radar. This paper proposes a multiband quadrature compressive sampling (MQCS) system to perform the sampling at sub-Landau rate. The MQCS system randomly projects the multiband signal into a compressive multiband one by modulating each subband signal with a low-pass signal and then samples the compressive multiband signal at Landau-rate with output of compressive measurements. The compressive inphase and quadrature (I/Q) components of each subband are extracted from the compressive measurements respectively and are exploited to recover the baseband I/Q components. As effective bandwidth of the compressive multiband signal is much less than that of the received multiband one, the sampling rate is much less than Landau rate of the received signal. Simulation results validate that the proposed MQCS system can effectively acquire and reconstruct the baseband I/Q components of the multiband signals.

[1]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[2]  P. Vaidyanathan,et al.  Periodically nonuniform sampling of bandpass signals , 1998 .

[3]  Ping Feng,et al.  Spectrum-blind minimum-rate sampling and reconstruction of multiband signals , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[4]  Zhong Liu,et al.  Quadrature Compressive Sampling for Radar Signals , 2014, IEEE Transactions on Signal Processing.

[5]  A. Feuer,et al.  Robust Patterns in Recurrent Sampling of Multiband Signals , 2008, IEEE Transactions on Signal Processing.

[6]  James B. Y. Tsui,et al.  Direct bandpass sampling of multiple distinct RF signals , 1999, IEEE Trans. Commun..

[7]  J. T. Mayhan,et al.  Ultrawide-band coherent processing , 1999 .

[8]  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.

[9]  Cormac Herley,et al.  Minimum rate sampling and reconstruction of signals with arbitrary frequency support , 1999, IEEE Trans. Inf. Theory.

[10]  P. Lombardo,et al.  Multiband coherent radar detection against compound-Gaussian clutter , 1999 .

[11]  Zhong Liu,et al.  Quadrature compressive sampling for radar echo signals , 2011, 2011 International Conference on Wireless Communications and Signal Processing (WCSP).

[12]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[13]  Michael B. Wakin,et al.  Compressive Sensing of Analog Signals Using Discrete Prolate Spheroidal Sequences , 2011, ArXiv.

[14]  James B. Y. Tsui,et al.  Digital Techniques for Wideband Receivers , 1995 .

[15]  Ching-Hsiang Tseng,et al.  Direct downconversion of multiband RF signals using bandpass sampling , 2006, IEEE Transactions on Wireless Communications.

[16]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[17]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[18]  H. Landau.  Necessary density conditions for sampling and interpolation of certain entire functions , 1967 .

[19]  Roberto Gómez-García,et al.  Multi-Band Radar Receiver Design Approach for Minimum Bandpass Sampling , 2013, IEEE Transactions on Aerospace and Electronic Systems.