Calculating the Spectral Entropy of a Stationary Random Process

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Abstract

The problem of calculating the spectral entropy of a stationary random process is solved. The spectral entropy (σ-entropy) of a signal is understood as a scalar value characterizing the noise color; it describes the class of signals affecting a system depending on the band under study. By assumption, the random process is defined by a shaping filter, with the Gaussian white noise with a unit covariance matrix supplied at its input, or by an autocorrelation function. The spectral entropy of the stationary random process is analytically derived using a known mathematical model of the shaping filter in the form of a log-determinant function that depends on the transfer matrix and the observability Gramian of the filter. An algorithm for calculating the σ-entropy of stationary random processes with a known autocorrelation function is proposed. The method reduces to reconstructing the mathematical model of the shaping filter using its spectral density factorization. A numerical example is provided: spectral entropy is calculated for a disturbance describing the velocity of wind gusts that affect an aircraft.

About the authors

A. A Belov

Trapeznikov Institute of Control Sciences, Russian Academy of Sciences

Email: a.a.belov@inbox.ru
Moscow, Russia

O. G Andrianova

Trapeznikov Institute of Control Sciences, Russian Academy of Sciences

Email: andrianovaog@gmail.com
Moscow, Russia

References

  1. Методы классической и современной теории автоматического управления: Учебник в 5-и тт.; 2-е изд. перераб и доп. Т. 2: Статистическая динамика и идентификация систем автоматического управления / Под ред. К.А. Пупкова, Н.Д. Егупова. – М.: Издательство МГТУ им. Н.Э. Баумана, 2004. – 640 с.
  2. Wang, S., Wu, Z., Wu, Z.-G. Trajectory Tracking and Disturbance Rejection Control of Random Linear Systems // Journal of the Franklin Institute. – 2022. – Vol. 359, no. 9. – P. 4433–4448.
  3. Кочетков В.Т., Половко А.М., Пономарев В.М. Теория систем управления и самонаведения ракет. – М.: Наука, 1964. – 536 с.
  4. Burlibaşa, A., Ceangă, E. Rotationally Sampled Spectrum Approach for Simulation of Wind Speed Turbulence in Large Wind Turbines // Applied Energy. – 2013. – Vol. 111. – P. 624–635.
  5. Wang, C., Wang, X., Ju, P., et al. Survey on Stochastic Analysis Methods for Power Systems // Autom. Electr. Power Syst. – 2022. – Vol. 46. – P. 184–199.
  6. Boichenko, V.A., Belov, A.A., Andrianova, O.G. State-Space Solution to Spectral Entropy Analysis and Optimal State-Feedback Control for Continuous-Time Linear Systems // Mathematics. – 2024. – Vol. 22, no. 12. – Art. no. 3604. – DOI: https://doi.org/10.3390/math12223604
  7. Boichenko, V., Belov, A. On σ-entropy Analysis of Linear Stochastic Systems in State Space // Syst. Theor. Control Comput. J. – 2021. – Vol. 1, no. 1. – P. 30–35.
  8. Rudin, W. Real and Complex Analysis. – New York: McGraw-Hill, 1986. – 416 p.
  9. Mustafa, D., Glover, K. Minimum Entropy H∞ Control. – Heidelberg–Berlin: Springer, 1990. – 144 p.

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