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Spectrum Analyzer Based on a Dynamic Filter

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Abstract

A method for the design of a second-order recursive filter, optimal in terms of the bias error, and a method for the synthesis of the parameters of correlation filter spectrum analyzers based on it are presented. The improvement of technical characteristics of spectrum analyzers based on dynamic filters is obtained by the transition from analog to digital filtering. The structure of the recursive narrowband digital dynamic filter of the second order is optimized by the minimum relative dispersion of the power spectral density estimation. It is justified that the correlation-filter spectrum analyzer has two components of methodological error: approximation error and statistical error. Expressions for the relative approximation error of the power spectral density estimate for two versions of optimization respectively with the constant and piecewise-step law of change of the relative attenuation coefficient of the proposed second-order digital dynamic filter are derived. The results of experimental studies suggest that the spectrum analyzer based on the proposed recursive filter of the second order provides considerably higher accuracy (of the order of several percent) in comparison with classical filter analyzers with stationary non-reconfigurable filters of even higher-order (third and fourth). The advantages in the accuracy of digital correlation-filter spectrum analyzers with the use of a narrow-band digital dynamic filter of the second order are demonstrated. The theoretical and practical principles of their design are presented.

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Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Bhanu SJ, Baswaraj D, Bigul SD, Sastry JKR (2019) Generating test cases for testing embedded systems using combinatorial techniques and neural networks based learning model. Int J Emerg Trends Eng Res 7(7):417–429. https://doi.org/10.30534/ijeter/2019/047112019

    Article  Google Scholar 

  2. Bonavolontà F, D’Apuzzo M, Liccardo A, Mieleb G (2016) Harmonic and interharmonic measurements through a compressed sampling approach. Measurement 77:1–15. https://doi.org/10.1016/j.measurement.2015.08.022

    Article  Google Scholar 

  3. Chinkov VM, Gerasimov SV (2013) Study and justification of the criteria of optimization of measurement signals for automated control systems. Ukrainian Metrol J 4:43–47. http://www.metrology.kharkov.ua/fileadmin/user_upload/data_gc/umj/2013/umj_2013_4/JRN/PDF/2.pdf

  4. Dudnik PI, Ilchuk AR, Tatarskii BG (2007) Multifunctional radar systems: textbook for universities. Ed. B.G. Tatarskii. Moscow. Drofa

  5. Hajimolahoseini H, Taban MR, Soltanian-Zadeh H (2012) Extended Kalman Filter frequency tracker for nonstationary harmonic signals. Measurement 45:126–132. https://doi.org/10.1016/j.measurement.2011.09.008

    Article  Google Scholar 

  6. Helstrom CW (1995) Elements of signal detection and estimation. Prentice Hall, Englewood Cliffs. NJ. USA

    MATH  Google Scholar 

  7. Herasimov S, Belevshchuk Y, Ryapolov I, Tymochko O, Pavlenko M, Dmitriiev O, Zhyvytskyi M, Goncharenko N (2018) Characteristics of radiolocation scattering of the SU-25T attack aircraft model at different wavelength ranges. Inf Control Syst. East Euro J Entrep Technol. 6(96):22–29

    Google Scholar 

  8. Herasimov S, Pavlii V, Tymoshchuk O et al (2019) Testing signals for electronics: Criteria for synthesis. J Electron Test 35(148):1–9. https://doi.org/10.1007/s10836-019-05798-9

    Article  Google Scholar 

  9. Herasimov S, Roshchupkin E, Kutsenko V, Riazantsev S, Nastishin Yu (2020) Statistical analysis of harmonic signals for testing of Electronic Devices. Int J Emerg Trends Eng Res. 8(7):3791–3798. https://doi.org/10.30534/ijeter/2020/143872020

    Article  Google Scholar 

  10. Herasimov S, Tymochko O, Kolomiitsev O, Aloshin G, Kriukov O, Morozov O, Aleksiyev V (2019) Formation analysis of multi-frequency signals of laser information measuring system. EUREKA Phys Eng 5:19–28. https://doi.org/10.21303/2461-4262.2019.00984

    Article  Google Scholar 

  11. Karimian-Azari S, Jensen JR, Christensen MG (2016) Computationally efficient and noise robust DOA and pitch estimation. Proc IEEE/ACM Trans Audio Lang Process 24:1613–1625. https://doi.org/10.1109/TASLP.2016.2577501

    Article  Google Scholar 

  12. Kihong S (2019) On the selection of sensor locations for the fictitious FRF based fault detection method. Int J Emerg Trends Eng Res 7(7):569–575. https://doi.org/10.30534/ijeter/2019/277112019

    Article  Google Scholar 

  13. Levanon N, Mozeson E (2004) Radar signals. John Wiley & Sons Inc, Hoboken NJ

    Book  Google Scholar 

  14. Murphy E, Colm S (2004) All about direct digital synthesis. Analog Dialogue 8(3):1–5. https://www.analog.com/en/analog-dialogue/articles/all-about-direct-digital-synthesis.html

  15. Rybin Yu (2014) Measuring signal generators. Springer, Theory and Design. New York

    Book  Google Scholar 

  16. Spatial-temporal Signal Processing (1984) Ed. I. Kremer. Moscow. Radio and Communication

  17. Wu X, Tian Z, Davidson T, Giannakis G (2006) Optimal waveform design for UWB radios. IEEE Trans Signal Process 54(6):2009-2021. https://www.ece.mcmaster.ca/~davidson/pubs/Wu_etal_UWB_waveform_design.pdf

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Correspondence to Yu. A. Nastishin.

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Communicated by M. Margala

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Herasimov, S., Borysenko, M., Roshchupkin, E. et al. Spectrum Analyzer Based on a Dynamic Filter. J Electron Test 37, 357–368 (2021). https://doi.org/10.1007/s10836-021-05954-0

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