A low-complexity sub-Nyquist sampling system for wideband Radar ESM receivers

The problem of efficient sampling of wideband Radar signals for Electronic Support Measures (ESM) is investigated in this paper. Wideband radio frequency sampling generally needs a sampling rate at least twice the maximum frequency of the signal, i.e. Nyquist rate, which is generally very high. However, when the signal is highly structured, like wideband Radar signals, we can use the fact that signals do not occupy the whole spectrum and instead, there exists a parsimonious structure in the time-frequency domain. Here, we use this fact and introduce a novel low complexity sampling system, which has a recovery guarantee, assuming that received RF signals follow a particular structure. The proposed technique is inspired by the compressive sampling of sparse signals and it uses a multi-coset sampling setting, however it does not involve a computationally expensive reconstruction step. We call this here Low-Complexity Multi-Coset (LoCoMC) sampling technique. Simulation results, show that the proposed sub-Nyquist sampling technique works well in simulated ES scenarios.

[1]  D. Slepian,et al.  Prolate spheroidal wave functions, fourier analysis and uncertainty — II , 1961 .

[2]  H. Pollak,et al.  Prolate spheroidal wave functions, fourier analysis and uncertainty — III: The dimension of the space of essentially time- and band-limited signals , 1962 .

[3]  Lloyd R. Welch,et al.  Lower bounds on the maximum cross correlation of signals (Corresp.) , 1974, IEEE Trans. Inf. Theory.

[4]  F. Harris On the use of windows for harmonic analysis with the discrete Fourier transform , 1978, Proceedings of the IEEE.

[5]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

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

[7]  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.

[8]  S. Mallat A wavelet tour of signal processing , 1998 .

[9]  Özgür Yilmaz,et al.  Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[10]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[11]  Thomas Strohmer,et al.  GRASSMANNIAN FRAMES WITH APPLICATIONS TO CODING AND COMMUNICATION , 2003, math/0301135.

[12]  Richard Baraniuk,et al.  APPLICATION OF COMPRESSIVE SENSING TO THE DESIGN OF WIDEBAND SIGNAL ACQUISITION RECEIVERS , 2009 .

[13]  Yonina C. Eldar,et al.  Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals , 2007, IEEE Transactions on Signal Processing.

[14]  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.

[15]  Justin K. Romberg,et al.  Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals , 2009, IEEE Transactions on Information Theory.

[16]  Michael Lexa,et al.  Multi-coset Sampling and Recovery of Sparse Multiband Signals , 2011 .

[17]  Bernie Mulgrew,et al.  Spatially variant apodization for conventional and sparse spectral sensing systems , 2011 .

[18]  Fabien Millioz,et al.  Sparse Detection in the Chirplet Transform: Application to FMCW Radar Signals , 2012, IEEE Transactions on Signal Processing.

[19]  P. W. East Fifty years of instantaneous frequency measurement , 2012 .

[20]  Yuan Xu,et al.  Cubature Formulas on Spheres , 2013 .

[21]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.