Skip to main content
Log in

Promotion of Improved Discrete Polynomial-Phase Transform Method for Phase Parameters Estimation of Linear Frequency Modulation Signal

  • THEORY AND METHODS OF SIGNAL PROCESSING
  • Published:
Journal of Communications Technology and Electronics Aims and scope Submit manuscript

Abstract

The discrete polynomial-phase transform (DPT) method estimate chirp rate and central frequency of LFM signal based on sequential estimation of polynomial phase parameters. DPT method which has been developed by an iterative approach as Improved DPT method uses nonlinear least squares (NLS) technique to estimate phase parameters of the LFM signal. NLS have high computational load. In order to promote the precision of estimation and reduce the computational load in Improved DPT method, combined technique is proposed and used which provides an initial estimation of frequency interval based on NLS criterion in single-exponential mode and using random basis functions method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Similar content being viewed by others

REFERENCES

  1. M. S. Wang, A. K. Chan, and C. K. Chui, “Linear frequency-modulated signal detection using randon-ambiguity tansform,” IEEE Trans. on Signal Process. 46, 571–586 (1998).

    Article  Google Scholar 

  2. J. Zheng et al., “An algorithm for phase parameters estimation of multi-chirp signals,” J. Xi’an Jiaotong Univ. 47 (2), 69–74 (2013).

    Google Scholar 

  3. V. Chen and S. Qian, “Joint time-frequency transform for radar range-Doppler imaging,” IEEE Trans. Aerosp. Electron. Syst. 34, 486–499 (1998).

    Article  Google Scholar 

  4. M. Xing, R. Wu, Y. Li, and Z. Bao, “New ISAR imaging algorithm based on modified Wigner–Ville distribution,” IET Radar Sonar Navig. 3, 70–80 (2009).

    Article  Google Scholar 

  5. J. Zheng, H. Liu, and Q. H. Liu, “Parameterized centroid frequency-chirp rate distribution for LFM signal analysis and mechanisms of constant delay introduction”, IEEE Trans. Signal Process. 65, 6435–6447 (2017).

    Article  MathSciNet  Google Scholar 

  6. P. O’Shea, “A new technique for instantaneous frequency rate estimation,” IEEE Signal Process. Lett. 9, 251–252 (2002).

    Article  Google Scholar 

  7. P. O’Shea, “A fast algorithm for estimating the parameters of a quadratic FM signal,” IEEE Trans. Signal Process. 52, 385–393 (2004).

    Article  MathSciNet  Google Scholar 

  8. P. O. Shea, “A new technique for instantaneous frequency rate estimation,” IEEE Signal Process. Lett. 9, 251–252 (2002).

    Article  Google Scholar 

  9. S. Stein, “Algorithms for ambiguity function processing,” IEEE Trans. Acoust. Speech Signal Process. 29, 588–599 (1981).

    Article  Google Scholar 

  10. P. Wang et al., “Integrated cubic phase function for linear FM signal analysis,” IEEE Trans. Aerosp. Electron. Syst. 46, 963–977 (2010).

    Article  Google Scholar 

  11. M. Wang, A. Chen, and C. Chui, “Linear frequency-modulated signal detection using Radon-ambiguity transform,” IEEE Trans. Signal Process. 46, 571–586 (1998).

    Article  Google Scholar 

  12. X. Lv et al., “Keystone transform of the Wigner-Ville distribution for analysis of multicomponent LFM signals,” Signal Process. 89, 791–806 (2009).

    Article  Google Scholar 

  13. X. Lv et al., “ISAR imaging of maneuvering targets based on the range centroid Doppler technique,” IEEE Trans. Image Process. 47, 141–153 (2010).

    MathSciNet  MATH  Google Scholar 

  14. R. Tao, N. Zhang, and Y. Wang, “Analyzing and compensating effects of range and Doppler frequency migrations in linear frequency modulation pulse compression radar,” IET Radar Sonar Navig. 5, 12–22 (2011).

    Article  Google Scholar 

  15. H. Sun et al., “Application of the fractional Fourier transform to moving target detection in airborne SAR,” IEEE Trans. Aerosp. Electron. Syst. 38, 1416–1424 (2002).

    Article  Google Scholar 

  16. L. B. Almeida, “The fractional fourier transform and time-frequency representations,” IEEE Trans. Signal Process. 42, 3084–3091 (1994).

    Article  Google Scholar 

  17. X. Guo et al., “Comments on discrete chirp-Fourier transform and its application to chirp rate estimation,” IEEE Trans. Signal Process. 50, 3115–3115 (2002).

    Article  MathSciNet  Google Scholar 

  18. X. Xia, “Discrete chirp-Fourier transform and its application to chirp rate estimation,” IEEE Trans. Signal Process. 48, 3122–3133 (2000).

    Article  MathSciNet  Google Scholar 

  19. A. Serbes, “A method for estimating chirp rate of a linear frequency modulated Signal,” Balkan J. Electrical & Computer Eng. 6 (1), (2018).

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

    Article  MathSciNet  Google Scholar 

  21. Jibin Zheng, Tao Su, Qing Huo Liu, Long Zhang, and Wentao Zhu, “Fast parameter estimation algorithm for cubic phase signal based on quantifying effects of doppler frequency shift,” Prog. Electromagn Res. (PIER) 142, 57–74, (2013).

    Article  Google Scholar 

  22. Seo, Woo-Seok, Sang-Hee Kang, and Young-Doo Yoon. “An improved frequency estimation algorithm based on DFT and iterative method,” in Proc. 13th Int. Conf. Development in Power System Protection, Edinburgh, UK, Mar. 7–10, 2016, p. 5.

  23. Xiang-Gen Xia, “Discrete Chirp-Fourier transform and its application to chirp rate estimation,” IEEE Trans. Signal Process. 48, 3122–3133 (2000).

    Article  MathSciNet  Google Scholar 

  24. A. Serbes and L. Durak, “A Centered DFT-Based discrete fractional fourier transform and its application to chirp signal parameter estimation,” in Proc. 17th Eur. Signal Processing Conf. (EUSIPCO-2009), Glasgow, Aug.2009, pp. 1364–1368.

  25. J. Z. Wang, S. Y. Su, and Z. P. Chen, “Accurate parameters estimation of Chirp signal in low SNR,” in Proc. Int. Conf. on Audio, Language and Image Processing (ICALIP 2014), Shanghai, China, Jul. 7–9,2014, pp. 551–555.

  26. L. Zheng and D. Shi, “Maximum amplitude method for estimating the compact fractional Fourier domain,” IEEE Signal Proc. Lett. 17 (3) (2010).

  27. P. O’Shea, “Improving polynomial phase parameter estimation by using nonuniformly spaced signal sample method,” IEEE Trans. Signal Process. 60, 3405 (2012).

    Article  MathSciNet  Google Scholar 

  28. Y. Li, R. Wu, M. Xing, and Z. Bao, “Inverse synthetic aperture radar imaging of ship target with complex motion,” IET Radar, Sonar and Navigation 2, 395–403 (2008).

    Article  Google Scholar 

  29. T. J. Abatzoglou, “Fast Maximum Likelihood Joint Estimation of Frequency and Frequency Rate,” IEEE Trans. Aerosp. Electron. Syst. 22, 708–715 (1986).

    Article  Google Scholar 

  30. S. Peleg and B. Porat, “Estimation and classification of polynomial-phase signals,” IEEE Trans. Inf. Theory 37, 422–430 (1991).

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  32. S. Peleg and B. Friedlander, “The discrete polynomial-phase transform,” IEEE Trans. Signal Process. 43, 1901–1914 (1995). https://doi.org/10.1109/78.403349

    Article  Google Scholar 

  33. S. Peleg, “Estimation and detection with the discrete polynomial phase transform,” Ph. D. Dissertation (Univ. California, Davis, 1993).

  34. S. Kay, “Signal fitting with uncertain basis functions,” IEEE Signal Process. Lett. 6, 383–386 (2011).

    Article  Google Scholar 

  35. S. M. Kay, Fundamentals of Signal Processing-Estimation Theory (Prentice Hall, Englandwood Cliffs, 1993).

  36. S. M. Kay, Fundamentals of Statistical Signal Processing: Practical Algorithm Development (Pearson Education, 2013), Vol. 3.

    Google Scholar 

  37. S. Kay, “A computationally efficient nonlinear least squares method using random basis functions,” IEEE Signal Process. Lett. 20, 721–724 (2013). https://doi.org/10.1109/LSP.2013.2264808

    Article  Google Scholar 

  38. P. Stoica and R. L. Moses, Spectral Analysis of Signals (Pearson/Prentice Hall Upper Saddle River, 2005).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hamid Azad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nooshin Rabiee, Azad, H. & Parhizgar, N. Promotion of Improved Discrete Polynomial-Phase Transform Method for Phase Parameters Estimation of Linear Frequency Modulation Signal. J. Commun. Technol. Electron. 64, 1266–1275 (2019). https://doi.org/10.1134/S1064226919110214

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1064226919110214

Keywords:

Navigation