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A General Sub-Nyquist Sampling System for Pulse Streams

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Abstract

The recent finite rate of innovation (FRI) framework has shown that pulse streams can be sampled at the rate of innovation and recovered from a set of Fourier coefficients. However, due to the individual variation of the pulse frequency spectrum, previous FRI sampling systems vary owing to different pulse shapes. In this paper, we propose a general sub-Nyquist sampling and recovery method for pulse streams that is available for many kinds of pulses. The proposed scheme exploits the spread-spectrum technique from a random demodulator (RD), in which an analog mixing front end aliases the spectrum. Such a technique allows for recovery of an arbitrary pulse with the baseband of the frequency spectrum, which greatly improves the flexibility of the sampling system. But unlike the fast switching rate of the mixing signal in RD, the proposed method has a lower rate requirement and is simpler to implement in practice. To recover the unknown parameters of the pulse streams from the obtained aliased Fourier coefficients, we quantize the analog time axis with a fit number of uniform bins and propose a sparsity-based recovery algorithm. Finally, simulation results demonstrate the effectiveness and robustness of the proposed method.

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References

  1. R.G. Baraniuk, Compressive sensing [lecture notes]. IEEE Signal Process. Mag. 24(4), 118–121 (2007)

    Article  Google Scholar 

  2. E. Baransky, G. Itzhak, N. Wagner, I. Shmuel, E. Shoshan, Y. Eldar, Sub-Nyquist radar prototype: hardware and algorithm. IEEE Trans. Aerosp. Electron. Syst. 50(2), 809–822 (2014)

    Article  Google Scholar 

  3. T. Blu, P.L. Dragotti, M. Vetterli, P. Marziliano, L. Coulot, Sparse sampling of signal innovations: theory, algorithms, and performance bounds. IEEE Signal Process. Mag. 25(2), 31–40 (2008)

    Article  Google Scholar 

  4. H. Chen, C.H. Vun, A novel matrix optimization for compressive sampling-based sub-Nyquist OFDM receiver in cognitive radio. Circuits Syst. Signal Process. 37(11), 5069–5086 (2018)

    Article  MathSciNet  Google Scholar 

  5. P.L. Dragotti, M. Vetterli, T. Blu, Sampling moments and reconstructing signals of finite rate of innovation: shannon meets strang-fix. IEEE Trans. Signal Process. 55(5), 1741–1757 (2007)

    Article  MathSciNet  Google Scholar 

  6. Y.C. Eldar, Sampling Theory: Beyond Bandlimited Systems (Cambridge University Press, Cambridge, 2015)

    MATH  Google Scholar 

  7. N. Fu, G. Huang, L. Qiao, H. Zhao, Sub-Nyquist sampling and recovery of pulse streams with the real parts of fourier coefficients. IEEE Access 5, 22667–22677 (2017)

    Article  Google Scholar 

  8. K. Gedalyahu, R. Tur, Y.C. Eldar, Multichannel sampling of pulse streams at the rate of innovation. IEEE Trans. Signal Process. 59(4), 1491–1504 (2011)

    Article  MathSciNet  Google Scholar 

  9. G. Huang, N. Fu, L. Qiao, J. Cao, C. Fan, A simplified FRI sampling system for pulse streams based on constraint random modulation. IEEE Trans. Circuits Syst. II Express Briefs 65(2), 256–260 (2018)

    Article  Google Scholar 

  10. P.K. Korrai, K. Deergha Rao, C. Gangadhar, FPGA implementation of OFDM-based mmwave indoor sparse channel estimation using OMP. Circuits Syst. Signal Process. 37(5), 2194–2205 (2018)

    Article  MathSciNet  Google Scholar 

  11. J.K. Omura, J.K. Omura, R.A. Scholtz, B.K. Levitt, Spread Spectrum Communications Handbook, revised edn. (McGraw-Hill, New York, 2002)

    Google Scholar 

  12. R. Roy, A. Paulraj, T. Kailath, ESPRIT-a subspace rotation approach to estimation of parameters of cisoids in noise. IEEE Trans. Acoust. Speech Signal Process. 34(5), 1340–1342 (2003)

    Article  Google Scholar 

  13. G. Sun, Z. He, Y. Zhang, Distributed airborne MIMO radar detection in compound-Gaussian clutter without training data. Circuits Syst. Signal Process. 37(10), 4617–4636 (2018)

    Article  Google Scholar 

  14. J.A. Tropp, J.N. Laska, M.F. Duarte, J.K. Romberg, R.G. Baraniuk, Beyond Nyquist: efficient sampling of sparse bandlimited signals. IEEE Trans. Inf. Theory 56(1), 520–544 (2010)

    Article  MathSciNet  Google Scholar 

  15. R. Tur, Y.C. Eldar, Z. Friedman, Innovation rate sampling of pulse streams with application to ultrasound imaging. IEEE Trans. Signal Process. 59(4), 1827–1842 (2011)

    Article  MathSciNet  Google Scholar 

  16. M. Unser, Sampling-50 years after shannon. Proc. IEEE 88(4), 569–587 (2000)

    Article  Google Scholar 

  17. M. Vetterli, P. Marziliano, T. Blu, Sampling signals with finite rate of innovation. IEEE Trans. Signal Process. 50(6), 1417–1428 (2002)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

The paper is supported by National Natural Science Foundation of China (NSFC, No. 61671177).

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Correspondence to Ning Fu.

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Huang, G., Fu, N. & Qiao, L. A General Sub-Nyquist Sampling System for Pulse Streams. Circuits Syst Signal Process 38, 5360–5372 (2019). https://doi.org/10.1007/s00034-019-01101-5

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  • DOI: https://doi.org/10.1007/s00034-019-01101-5

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