Parameter estimation of frequency hopping signals based on analogue information converter

Parameter estimation of frequency hopping signals based on analogue information converter

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Aiming at the shortcomings of the existing frequency hopping signal parameter estimation algorithms based on compressed sensing theory, such as weak anti-noise performance, high data transmission and processing, and high hardware implementation cost, this study proposes an improved frequency hopping signal parameter estimation algorithm based on analogue information converter called Baseband Parameter Estimation (BPE) method. BPE does not reconstruct the frequency hopping signal into the Nyquist spectrum, and after obtaining the support set of the signal, the parameter estimation of the frequency hopping signal is performed directly at the baseband. The processing of baseband spectral slices can convert unreconstructed signal parameter estimates into parameter estimates for certain support sets, which makes BPE possible. Simulation experiments show that compared with the existing parameter estimation algorithm, BPE can not only greatly reduce the bandwidth and storage pressure required for subsequent transmission, but also has good anti-noise performance.


    1. 1)
      • 1. Torrieri, D.J.: ‘Mobile frequency-hopping CDMA systems’, IEEE Trans. Commun., 2000, 48, (8), pp. 13181327.
    2. 2)
      • 2. Lee, J., Yoon, D.: ‘Improved FH acquisition scheme in partial-band noise jamming’, IEEE Trans. Aerosp. Electron. Syst., 2016, 52, (6), pp. 30703076.
    3. 3)
      • 3. Liu, F., Marcellin, M.W., Goodman, N.A.: ‘Compressive sampling for detection of frequency-hopping spread spectrum signals’, IEEE Trans. Signal Process., 2016, 64, (21), pp. 55135524.
    4. 4)
      • 4. Li, B., Li, Y., Zhu, Y.: ‘Compressive frequency estimation for frequency hopping signal’. IEEE Int. Conf. of IEEE Region 10 (TENCON 2013), Xi'an, Shaanxi Province, China, January 2014, pp. 14.
    5. 5)
      • 5. Jia, Y., Pengwu, T., Hongyi, Y.U.: ‘The identification of frequency hopping signal using compressive sensing’. 2009 Int. Conf. on Information Engineering and Computer Science, Wuhan, Hubei Province, China, December 2009, pp. 14.
    6. 6)
      • 6. Wu, J., Liu, N., Zhang, Y.: ‘Blind detection of frequency hopping signal based on compressive sensing’. 2012 2nd Int. Conf. on Consumer Electronics, Communications and Networks (CECNet), Yichang City, Hubei Province, China, April 2012, pp. 16911694.
    7. 7)
      • 7. Amin, M.G.: ‘Interference mitigation in spread spectrum communication systems using time-frequency distributions’, IEEE Trans. Signal Process., 1995, 45, (1), pp. 90101.
    8. 8)
      • 8. Zhao, J., Zhang, C., Lai, L.: ‘Blind parameter estimation of frequency-hopping signals based on time-frequency analysis’, J. Circuits Syst., 2003, 8, (3), pp. 4650.
    9. 9)
      • 9. Bsrbarossa, S., Scaglione, A.: ‘Parameter estimation of spread spectrum frequency hopping signals using time-frequency distributions’. First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications, Paris, France, 16–18 April 1997, pp. 213216.
    10. 10)
      • 10. Angelosante, D., Giannakis, G.B.: ‘Estimating multiple frequency-hopping signal parameters via sparse linear regression’, IEEE Trans. Signal Process., 2010, 58, (10), pp. 50445056.
    11. 11)
      • 11. Lv, C., Wang, B., Tang, T.: ‘Blind parameter estimation of frequency hopping signal using local characteristic-scale decomposition’, J. Signal Process., 2015, 31, (3), pp. 308313.
    12. 12)
      • 12. Fan, H., Guo, Y.: ‘A novel blind parameter estimation algorithm of frequency-hopping signals’, J. Signal Process., 2009, 25, (11), pp. 17541758.
    13. 13)
      • 13. Fan, H., Guo, Y., Ai, Y.: ‘Blind detection and parameter estimation algorithm based on atomic decomposition’, J. Signal Process., 2010, 26, (5), pp. 695702.
    14. 14)
      • 14. Sha, Z., Huang, Z., Zhou, Y.: ‘A modification method for time-frequency pattern of frequency-hopping signals based on time frequency sparsity’, J. Astronaut., 2013, 34, (6), pp. 848853.
    15. 15)
      • 15. Fu, W., Li, X., Liu, N., et al: ‘Parameter blind estimation of frequency-hopping signal based on time–frequency diagram modification’, Wirel. Pers. Commun., 2017, 97, (3), pp. 39793992.
    16. 16)
      • 16. Lin, Y.P., Vaidyanathan, P.P.: ‘Periodically nonuniform sampling of bandpass signals’, IEEE Trans. Circuits Syst. II Analog Digital Signal Process., 1998, 45, (3), pp. 340351.
    17. 17)
      • 17. Domínguez-Jiménez, M.E., González-Prelcic, N., Vazquez-Vilar, G., et al: ‘Design of universal multicoset sampling patterns for compressed sensing of multiband sparse signals’. 2012 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, August 2012, pp. 33373340.
    18. 18)
      • 18. Laska, J.N., Kirolos, S., Duarte, M.F., et al: ‘Theory and implementation of an analog-to-information converter using random demodulation’. 2007 IEEE Int. Symp. on Circuits and Systems, New Orleans, Louisiana, USA, June 2007, pp. 19591962.
    19. 19)
      • 19. Mishali, M., Elron, A., Eldar, Y.C.: ‘Sub-Nyquist processing with the modulated wideband converter’. 2010 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Dallas, Texas, USA, June 2010, vol. 130, no. 5, pp. 36263629.
    20. 20)
      • 20. Mishali, M., Eldar, Y.C.: ‘Reduce and boost: recovering arbitrary sets of jointly sparse vectors’, IEEE Trans. Signal Process., 2008, 56, (10), pp. 46924702.

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