Application of the Savitzky-Golay differentiating filter in restoring the sensor input signal

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Abstract

The problem of correcting the dynamic error by restoring the sensor input signal in the presence of additive noise at its output is considered. A review of publications on the application of the Savitzky-Golay fi lter in dynamic measurements is carried out. A block diagram of an adaptive dynamic measuring system based on the discrete Savitzky-Golay differentiating fi lter is developed. For the possibility of using the differentiating fi lter, a method is proposed for reducing the sensor transfer function to a minimum form of an integrating unit, the order of which is equal to the difference in the orders of the denominator and numerator of the sensor transfer function. Reduction is performed by processing a sequence of discrete samples of the sensor output signal using a reduction unit, the output signal of which is equivalent to the output signal of the reduced sensor transfer function. After analyzing the dynamic error of the sensor input signal restoration, an estimate of the error and its components is proposed, due to the difference between the sensor's transfer function and the ideal one and the additive noise at its output. The noise adaptation of the differentiating fi lter parameter is carried out by minimizing the mean square deviation of the dynamic error estimate. A computer simulation of the proposed measuring system is performed in the presence of additive random noise at the output of a second-order sensor. The effectiveness of estimating the dynamic error of reconstructing the sensor input signal based on the structure of a measuring system with the Savitzky-Golay differentiating fi lter is demonstrated. The application fi eld of the measuring system obtained is the measurement data processing of fast-changing quantities such as temperature, pressure, speed and acceleration, when the dynamic component of the error, caused by inertia of the sensor, as well as additive noises at its output, is dominant.

About the authors

A. S. Volosnikov

South Ural State University (National Research University)

Email: volosnikovas@susu.ru
ORCID iD: 0000-0001-5579-6040
SPIN-code: 1638-8035

References

  1. Волосников А. С. Измерительная система на основе нерекурсивных фильтров с оптимальной коррекцией погрешности динамического измерения. Измерительная техника, (10),19–25 (2022). https://doi.org/10.32446/0368-1025it.2022-10-19-25 ; https://www.elibrary.ru/okkmng.
  2. Волосников А. С. Адаптивное линейное оценивание погрешности динамического измерения. Измерительная техника, (10), 25–31. https://doi.org/10.32446/0368-1025it.2023-10-25-31.
  3. Savitzky A., Golay M. J. E. Smoothing and differentiation of data by simplifi ed least squares procedure. Analytical Chemistry, 36(8), 1627–1639 (1964). https://doi.org/10.1021/ac60214a047
  4. Sadeghi M., Behnia F., Amiri R. Window selection of the Savitzky-Golay fi lters for signal recovery from noisy measurements. IEEE Transactions on Instrumentation and Measurement, 69(8), 5418–5427 (2020). https://doi.org/10.1109/TIM.2020.2966310
  5. Gorry P. A. General least-squares smoothing and differentiation by the convolution (Savitzky-Golay) method. Analytical Chemistry, 62(6), 570–573 (1990). https://doi.org/10.1021/ac00205a007
  6. Madden H. H. Comments on the Savitzky-Golay convolution method for least-squares-fi t smoothing and differentiation of digital data. Analytical Chemistry, 50(9), 1383–1386 (1978). https://doi.org/10.1021%2Fac50031a048
  7. Schafer R. W. What is a Savitzky-Golay Filter?. IEEE Signal Processing Magazine, 28(4), 111–117 (2011). https://doi.org/10.1109/MSP.2011.941097
  8. Candan Ç., Inan H. A unifi ed framework for derivation and implementation of Savitzky-Golay fi lters. Signal Processing, 104, 203–211 (2014). https://doi.org/10.1016/j.sigpro.2014.04.016
  9. Шестаков А. Л. Методы теории автоматического управления в динамических измерениях: монография. Издательский центр ЮУрГУ, Челябинск (2013).
  10. Shestakov A. L., Keller A. V., Zamyshlyaeva A. A., Manakova N. A., Tsyplenkova O. N., Gavrilova O. V., Perevozchikova K. V. Restoration of dynamically distorted signal using the theory of optimal dynamic measurements and digital fi ltering. Measurement: Sensors, 18, 100178 (2021). https://doi.org/10.1016/j.measen.2021.100178
  11. Luo J., Ying K., He P., Bai J. Properties of Savitzky-Golay digital differentiators. Digital Signal Processing, 15(2), 122– 136 (2005). https://doi.org/10.1016/j.dsp.2004.09.008
  12. Nishida E. N., Dutra O. O., Ferreira L. H. C., Colletta G. D. Application of Savitzky-Golay digital differentiator for QRS complex detection in an electrocardiographic monitoring system. 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 233–238, Rochester, MN (2017). https://doi.org/10.1109/MeMeA.2017.7985881
  13. Krishnan S. R., Magimai.-Doss M., Seelamantula C. S. A Savitzky-Golay fi ltering perspective of dynamic feature computation. IEEE Signal Processing Letters, 20(3), 281–284 (2013). https://doi.org/10.1109/LSP.2013.2244593
  14. Rawash Y. Z., Al-Naami B., Alfraihat A., Owida H. A. Advanced low-pass fi lters for signal processing: A comparative study on Gaussian, Mittag-Leffl er, and Savitzky-Golay fi lters. Mathematical Modelling of Engineering Problems, 11(7), 1841– 1850 (2024). https://doi.org/10.18280/mmep.110713
  15. Rivolo S., Patterson T., Asrress K. N., Marber M., Redwood S., Smith N. P. Accurate and standardized coronary wave intensity analysis. IEEE Transactions on Biomedical Engineering, 64(5), 1187–1196 (2017). https://doi.org/10.1109/TBME.2016.2593518
  16. Dombi J., Dineva A. Adaptive Savitzky-Golay fi ltering and its applications. International Journal of Advanced Intelligence Paradigms, 16(2), 145–156 (2020). http://dx.doi.org/10.1504/IJAIP.2020.107011
  17. John A., Sadasivan J., Seelamantula C. S. Adaptive Savitzky-Golay fi ltering in non-Gaussian noise. IEEE Transactions on Signal Processing, 69, 5021–5036 (2021). https://doi.org/10.1109/TSP.2021.3106450
  18. Franklin G. F., Powell J. D., Workman M. L. Digital Control of Dynamic Systems (3rd ed.). Addison Wesley Longman, Menlo Park (1998).
  19. Oppenheim A. V., Schafer R. W. Discrete-time signal processing, 3rd ed. Pearson Education, Harlow (2010).
  20. Lyons R. G. Understanding digital signal processing, 3rd ed. Pearson Education, Boston (2011).
  21. Schmid M., Rath D., Diebold U. Why and how Savitzky-Golay fi lters should be replaced. ACS Measurement Science Au, 2(2), 185–196 (2022). https://doi.org/10.1021/acsmeasuresciau.1c00054
  22. Грановский В. А. Динамические измерения: Основы метрологического обеспечения. Энергоатомиздат, Ленинград (1984).

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