The Identification of Frequency Hopping Signal Using Compressive Sensing
Jia YUAN, Pengwu TIAN, Hongyi YU
DOI: 10.4236/cn.2009.11008   PDF    HTML     5,880 Downloads   11,875 Views   Citations


Compressive sensing (CS) creates a new framework of signal reconstruction or approximation from a smaller set of incoherent projection compared with the traditional Nyquist-rate sampling theory. Recently, it has been shown that CS can solve some signal processing problems given incoherent measurements without ever reconstructing the signals. Moreover, the number of measurements necessary for most compressive signal processing application such as detection, estimation and classification is lower than that necessary for signal reconstruction. Based on CS, this paper presents a novel identification algorithm of frequency hopping (FH) signals. Given the hop interval, the FH signals can be identified and the hopping frequencies can be estimated with a tiny number of measurements. Simulation results demonstrate that the method is effective and efficient.

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J. YUAN, P. TIAN and H. YU, "The Identification of Frequency Hopping Signal Using Compressive Sensing," Communications and Network, Vol. 1 No. 1, 2009, pp. 52-56. doi: 10.4236/cn.2009.11008.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] AYDIN L, POLYDOROS A. Hop-timing estimation for FH signals using a coarsely channelized receiver. IEEE Trans. Communication, Apr. 1996, 44(4): 516-526.
[2] ZHANG X, DU X, ZHU L. Time frequency analysis of frequency hopping signals based on Gabor spectrum method. Journal of Data Acquisition & Processing, Jun. 2007, 22(2): 123-135.
[3] HIPPENSTIEL R, KHALIL N, FARGUES M. The use of wavelets to identify hopped signals. In 1997 Fortieth Asilomar Conf. Signals, System & Computer, 1997, 1: 946-949.
[4] FAN H, GUO Y, XU Y. A novel algorithm of blind detection of frequency hopping signal based on second-order cyclostationarity. Proc. 2008 Image and Signal Processing Congr., 2008, 5: 399-402.
[5] HAUPT J, NOWAK R, YEH G. Compressive sampling for signal classification. In 2006 Asilomar Conf. on Signals, System & Computer, Oct. 2006, 1430-1434.
[6] HAUPT J, NOWAK R. Compressive sampling for signal detection. Conf. Rec. 2007 IEEE Int. Conf. Acoustics Speech and Signal Processing, 2007, 3: 1509-1512.
[7] DUARTE M F, DAVENPORT M A, WAKIN M B. Multiscale random projection for compressive classification. Conf. Rec. 2007 IEEE Int. Conf. Image Processing, 2007, 6: 161-164.
[8] DUARTE M F, DAVENPORT M A, WAKIN M B, BRANIUK R G. Sparse signal detection from incoherent projection. Conf. Rec. 2006 IEEE Int. Conf. Acoustics Speech and Signal Proc-essing, 2006, 3: 305-308.
[9] BRANIUK R. Compressed sensing. IEEE Signal Processing Magazine, Jul. 2007, 24(4): 118-121.
[10] DONOHO D. Compressed sensing. IEEE Trans. Inform. Theory, Apr. 2006, 52(4): 1289-1306.
[11] CANDES E, ROMBERG J, TAO T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory, Feb. 2006, 52(2): 489-509.
[12] DONOHO D, TANNER J. Sparse nonnegative solutions of underdetermined linear equations by linear programming. Proc. National Academy Science, 2005, 102(27): 9446-9451.
[13] TTOPP J A. Greed is good: Algorithmic results for sparse approximation. IEEE Trans. Inform. Theory, Oct. 2004, 50(10): 2231-2242.
[14] HAUPT J, NOWAK R. Signal reconstruction from noisy random projection. IEEE Trans. Inform. Theory, Sep. 2006, 52(9): 4036-4048.
[15] CHEN S, DONOHO D, SAUNDERS M. Atomic decomposition by basis pursuit. SIAM J. Sci. Comput., 1998, 20: 33-61.
[16] LASKA J, KIROLOS S, MASSOUD Y, BARANIUK R. Random sampling for analog-to-informaion conversion of wideband signals. IEEE Dallas/CAS Workshop on Design, Application, Integration and Software, Oct. 2006, 119-122.

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