CabadaÄŸ, G., Karal, Ã. (2023). A Comparative Study of FFT Based Frequency Estimation Using Different Interpolation Techniques. Alustath, 28(2), 86-93. doi: 10.33899/rengj.2023.139624.1250
Gamze Cabadağ; Ömer Karal. "A Comparative Study of FFT Based Frequency Estimation Using Different Interpolation Techniques". Alustath, 28, 2, 2023, 86-93. doi: 10.33899/rengj.2023.139624.1250
CabadaÄŸ, G., Karal, Ã. (2023). 'A Comparative Study of FFT Based Frequency Estimation Using Different Interpolation Techniques', Alustath, 28(2), pp. 86-93. doi: 10.33899/rengj.2023.139624.1250
CabadaÄŸ, G., Karal, Ã. A Comparative Study of FFT Based Frequency Estimation Using Different Interpolation Techniques. Alustath, 2023; 28(2): 86-93. doi: 10.33899/rengj.2023.139624.1250

A Comparative Study of FFT Based Frequency Estimation Using Different Interpolation Techniques

Al-Rafidain Engineering Journal (AREJ)
Volume 28, Issue 2, September 2023, Pages 86-93    PDF (485.28 K)
Document Type: Research Paper
DOI: 10.33899/rengj.2023.139624.1250
Authors
Gamze Cabadağ* 1; Ömer Karal2
1Electrical and Electronics Engineering Department, Ankara Yıldırım Beyazıt University, Ankara, Turkey
2Electrical and Electronics Engineering Departement, Ankara Yıldırım Beyazıt University, Ankara, Turkey
Abstract
Fast Fourier Transform (FFT) is a commonly used method in electronic support systems for frequency parameter estimation. If the frequency of the radar signal is not an exact multiple of the frequency resolution, the frequency of this signal will usually appear in an inter-line position when FFT is applied. To improve the accuracy of the estimated frequency, interpolation techniques are used to find the peak between two spectral lines. In this study, the frequency of the radar signal is estimated by employing three different interpolation techniques (Ding, Voglewede and Hanning window based interpolation) to the output obtained by applying N-point FFT to the intermediate frequency (IF) signal. In addition, unlike the literature, the behavior of signals contaminated with Laplace noise as well as Gaussian noise were analyzed with these three techniques and their performances were compared. From the analysis results, Ding and Voglewede techniques reduced error rate at all frequency. However, the Hanning window-based interpolation method improved the frequency accuracy values at 500MHz and 750MHz, but it increased the error at 250MHz and 1000MHz frequencies. The error rates of the estimated frequencies can be sorted from the lowest to the highest as follows: Ding, Voglewede and Hanning window based interpolation.
Keywords
FFT; Frequency Estimation; Interpolation; Laplace Noise; Gauss Noise
References
  1. C. Peeters et al., “Review and comparison of tacholess instantaneous speed estimation methods on experimental vibration data,” Mech. Syst. Signal Process., vol. 129, pp. 407–436, 2019, doi: 10.1016/j.ymssp.2019.02.031.
  2. Z. Lai et al., “Application of FFT interpolation correction algorithm based on window function in power harmonic analysis,” in IOP Conference Series: Earth and Environmental Science, 2019, vol. 252, no. 3, p. 32184.
  3. B. Yang, K. Dai, C. Yang, H. Luo, K. He, and Z. Dai, “Improvement of recursive DFT for APF with higher switching frequency to suppress wideband harmonics,” IEEE Access, vol. 9, pp. 144300–144312, 2021.
  4. Ä°. E. Ortatatlı, A. Orduyılmaz, M. Serin, Ö. Özdil, A. Yıldırım, and A. C. Gürbüz, “Real-time frequency parameter extraction for electronic support systems,” in 2016 24th Signal Processing and Communication Application Conference (SIU), 2016, pp. 105–108.
  5. K. H. Sayidmarie and S. E. Abdul-Fatah, “The Effet of the Height and Speed of the Airplane Carrying the Focused Synthetic Aperture Radar on the  Azimuth Resolution,” Al-Rafidain Eng. J., vol. 13, no. 4, 2005.
  6. K. Ibrahim Ali Al-Sharabi and D. Fiz, “Design of wideband radio direction finder based on amplitude comparison,” Al-Rafidain Eng. J., vol. 19, no. 5, pp. 77–86, 2011.
  7. K. Ibrahim Ali Al-Sharabi, “A SimpleMethod to Derive the Bistatic Tracking Radar System Formulas,” Al-Rafidain Eng. J., vol. 20, no. 2, pp. 140–149, 2012.
  8.  J. B. Tsui, Digital techniques for wideband receivers, vol. 2. SciTech Publishing, 2004.
  9. Y. Liu, C. Wang, J. Sun, S. Du, and Q. Hong, “One-step calculation circuit of fft and its application,” IEEE Trans. Circuits Syst. I Regul. Pap., vol. 69, no. 7, pp. 2781–2793, 2022.
  10. E. Jacobsen and P. Kootsookos, “Fast, accurate frequency estimators [DSP Tips & Tricks],” IEEE Signal Process. Mag., vol. 24, no. 3, pp. 123–125, 2007.
  11. K. Duda, “DFT interpolation algorithm for Kaiser–Bessel and Dolph–Chebyshev windows,” IEEE Trans. Instrum. Meas., vol. 60, no. 3, pp. 784–790, 2011.
  12. Ç. Candan, “Fine resolution frequency estimation from three DFT samples: Case of windowed data,” Signal Processing, vol. 114, pp. 245–250, 2015.
  13. M. C. Arva, N. Bizon, and A. Micu, “Proposal of an accurate and low complexity method for frequency estimation using DFT interpolation,” in 2017 40th International Conference on Telecommunications and Signal Processing (TSP), 2017, pp. 474–479.
  14. J. Borkowski, D. Kania, and J. Mroczka, “Comparison of sine-wave frequency estimation methods in respect of speed and accuracy for a few observed cycles distorted by noise and harmonics,” Metrol. Meas. Syst., vol. 25, no. 2, 2018.
  15. M. Gasior and J. L. Gonzalez, “Improving FFT frequency measurement resolution by parabolic and Gaussian spectrum interpolation,” in AIP Conference Proceedings, 2004, vol. 732, no. 1, pp. 276–285.
  16. Ç. Candan, “A method for fine resolution frequency estimation from three DFT samples,” IEEE Signal Process. Lett., vol. 18, no. 6, pp. 351–354, 2011.
  17. L. Fang, D. Duan, and L. Yang, “A new DFT-based frequency estimator for single-tone complex sinusoidal signals,” in MILCOM 2012-2012 IEEE Military Communications Conference, 2012, pp. 1–6.
  18. V. Iglesias, J. Grajal, O. Yeste-Ojeda, M. Garrido, M. A. Sánchez, and M. López-Vallejo, “Real-time radar pulse parameter extractor,” in 2014 IEEE Radar Conference, 2014, pp. 371–375.
  19. B. G. Quinn, “Estimating frequency by interpolation using Fourier coefficients,” IEEE Trans. Signal Process., vol. 42, no. 5, pp. 1264–1268, 1994.
  20. M. Koç, “Bazı Ayrık Fourier DönüÅŸümüne Dayalı Frekans Kestiricilerin KarşılaÅŸtırmalı Performans Analizi.” Bursa Uludag University (Turkey), 2021.
  21. A. A. Minda, D. Lupu, and G.-R. Gillich, “Improvement of Jain’s algorithm for frequency estimation,” Stud. Univ. Babes-Bolyai Eng., vol. 65, no. 1, 2020.
  22. A. A. Minda, C.-I. Barbinita, and G. R. Gillich, “A Review of Interpolation Methods Used for Frequency Estimation,” Rom. J. Acoust. Vib., vol. 17, no. 1, pp. 21–26, 2020.
  23. R. K. Niranjan, C. B. R. Rao, and A. K. Singh, “Performance Comparison of FFT based Frequency Estimation using different Interpolation Techniques for ELINT Systems,” in 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2021, pp. 1–4.
  24. B. K. A. Abdulhamed, “Digital Instantaneous Frequency Measurement Receiver for Fine Frequency and High Sensitivity,” 2019.
  25. Yazıcı, Z.B., Hasimoglu S,. “Comparison of a random model of Hand-Foot-Mouth Disease model with Gaussian and Laplacian parameters,” GümüÅŸhane Üniversitesi Fen Bilim. Derg., vol. 12, no. 1, pp. 9–25, 2019.
  26. Ö. Karal, “Maximum likelihood optimal and robust support vector regression with lncosh loss function,” Neural Networks, 2017.
  27. G. CabadaÄŸ and K. Ömer, “Analysis of Prony’s and Pisarenko Frequency Estimation Methods at Different Bandwidths, Different Noise And Variances,” 30th Signal Processing and Communications Applications Conference (SIU), 2022.
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