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Two-Stage Estimator for Frequency Rate and Initial Frequency in LFM Signal Using Linear Prediction Approach

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Abstract

We propose a two-stage estimator to estimate chirp rate and initial frequency of the chirp signals in the presence of additive white Gaussian noise. In the first stage, the chirp rate estimation problem is reformulated as a single tone frequency estimation problem. Then, the frequency of single tone is estimated through a linear prediction approach. Using the chirp rate estimate in the first stage, we can convert the linear frequency modulated signal to a single tone. Similar to the first stage, the initial frequency is estimated via the linear prediction approach. The performance of the present method is assessed by comparison with Cramer–Rao lower bound and other existing methods through computer simulations. The proposed algorithm estimates well for different values of the chirp rate and initial frequency, as well as for different number of samples. In other words, this algorithm has uniform performance for various values of the signal parameters.

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References

  1. T. Abatzoglou, Fast maximum likelihood joint estimation of frequency and frequency rate. IEEE Trans. Aerosp. Electron. Syst. AES–22(6), 708–715 (1986)

    Article  Google Scholar 

  2. O. Besson, M. Ghogho, A. Swami, Parameter estimation for random amplitude chirp signals. IEEE Trans. Signal Process. 47(12), 3208–3219 (1999)

    Article  Google Scholar 

  3. J. Cao, N. Zhang, L. Song, A fast algorithm for the chirp rate estimation, in IEEE International Symposium on Electronic Design, Test and Applications (2008), p. 45–48

  4. C.E. Davila, A subspace approach to estimation of autoregressive parameters from noisy measurements. IEEE Trans. Signal Process. 46(2), 531–534 (1998)

    Article  Google Scholar 

  5. Z.M. Deng, L.M. Ye, M.Z. Fu, S.J. Lin, Y.X. Zhang, Further investigation on time-domain maximum likelihood estimation of chirp signal parameters. IET Signal Proc. 7(5), 444–449 (2013)

    Article  MathSciNet  Google Scholar 

  6. P.M. Djuric, S.M. Kay, Parameter estimation of chirp signals. IEEE Trans. Acoust. Speech Signal Process. 38(12), 2118–2126 (1990)

    Article  Google Scholar 

  7. I. Djurovic, Viterbi algorithm for chirp-rate and instantaneous frequency estimation. Sig. Process. 91(5), 1308–1314 (2011)

    Article  Google Scholar 

  8. I. Djurovic, A WD-RANSAC instantaneous frequency estimator. IEEE Signal Process. Lett. 23(5), 757–761 (2016)

    Article  Google Scholar 

  9. I. Djurovic, QML-RANSAC: PPS and FM signals estimation in heavy noise environments. Signal Process. 130(1), 142–151 (2017)

    Article  Google Scholar 

  10. I. Djurovic, M. Simeunovic, S. Djukanovic, P. Wang, A hybrid CPF–HAF estimation of polynomial-phase signals: detailed statistical analysis. IEEE Trans. Signal Process. 60(10), 5010–5023 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  11. K. Heydari, P. Azmi, B. Abbasi, A. Heydari, Determining the parameters of chirp signals using cyclostationary method in presence of the interference. J. Fundam. Appl. Sci. 8(4), 478–486 (2016)

    Article  Google Scholar 

  12. M.Z. Ikram, K. Abed-Meraim, Y. Hua, Estimating the parameters of chirp signals: an iterative approach. IEEE Trans. Signal Process. 46(12), 3436–3441 (1998)

    Article  Google Scholar 

  13. M. Jankiraman, Design of Multi-frequency CW Radars (SciTech Publishing, New York, 2007)

    Book  Google Scholar 

  14. Y. Li, H. Fu, P.Y. Kam, Improved, approximate, time-domain ML estimators of chirp signal parameters and their performance analysis. IEEE Trans. Signal Process. 57(4), 1260–1272 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  15. Y. Li, P.Y. Kam, Improved chirp parameter estimation using signal recovery method, in IEEE Vehicular Technology Conference (2010), pp. 1–5

  16. Z. Li, W. Sheng-Li, N. Jin-Lin, L. Guo-Sui, Doppler frequency rate estimation for sar using match Fourier transform. Int. Conf. Neural Netw. Signal Process. 2, 1109–1112 (2003)

    Google Scholar 

  17. R.G. McKilliam, B.G. Quinn, I.V.L. Clarkson, B. Moran, B.N. Vellambi, Polynomial phase estimation by least squares phase unwrapping. IEEE Trans. Signal Process. 62(8), 1962–1975 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  18. A. Papoulis, S.U. Pillai, Probability, Random Variables, and Stochastic Processes (McGraw-Hill Education, New York, 2002)

    Google Scholar 

  19. L. Qi, R. Tao, S. Zhou, Y. Wang, Detection and parameter estimation of multicomponent LFM signal based on the fractional Fourier transform. Proc. Sci. China Ser. F: Inf. Sci. 47(2), 184–198 (2004)

    MathSciNet  MATH  Google Scholar 

  20. A. Springer, M. Huemer, L. Reindl, C.C. Ruppel, A. Pohl, F. Seifert, W. Gugler, R. Weigel, A robust ultra-broad-band wireless communication system using saw chirped delay lines. IEEE Trans. Microw. Theory Tech. 46(12), 2213–2219 (1998)

    Article  Google Scholar 

  21. S. Saha, S.M. Kay, Maximum likelihood parameter estimation of superimposed chirps using Monte Carlo importance sampling. IEEE Trans. Signal Process. 50(2), 224–230 (2002)

    Article  Google Scholar 

  22. A. Serbes, O. Aldimashki, A fast and accurate chirp rate estimation algorithm based on the fractional Fourier transform. EUSIPCO 190(1), 95–101 (2017)

    Google Scholar 

  23. P. Stoica, R.L. Moses, Spectral Analysis of Signals (Prentice Hall, Upper Saddle River, 2005)

    Google Scholar 

  24. P. Wang, P.V. Orlik, K. Sadamoto, W. Tsujita, F. Gini, Parameter estimation of hybrid sinusoidal FM-polynomial phase signal. IEEE Signal Process. Lett. 24(1), 66–70 (2017)

    Article  Google Scholar 

  25. H. Zhang, G. Zhang, J. Wang, \(\cal{H} _ {\infty } \) Observer design for LPV systems with uncertain measurements on scheduling variables: application to an electric ground vehicle. IEEE/ASME Trans. Mechatron. 21(3), 1659–1670 (2016)

    Article  Google Scholar 

  26. E.C. Zaugg, D.G. Long, Theory and application of motion compensation for LFM-CW SAR. IEEE Trans. Geosci. Remote Sens. 46(10), 2990–2998 (2008)

    Article  Google Scholar 

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Correspondence to A. Mahmoudi.

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Gholami, S., Mahmoudi, A. & Farshidi, E. Two-Stage Estimator for Frequency Rate and Initial Frequency in LFM Signal Using Linear Prediction Approach. Circuits Syst Signal Process 38, 105–117 (2019). https://doi.org/10.1007/s00034-018-0843-3

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  • DOI: https://doi.org/10.1007/s00034-018-0843-3

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