A method for improving the accuracy and reliability of navigation and measurement systems based on complex optimal invariant filtering of arbitrary signals in conditions of redundancy of information processing devices


Аuthors

Fedorinov A. Y.*, Ivanov Y. P.

Saint Petersburg State University of Aerospace Instrumentation, 67, Bolshaya Morskaya str., Saint Petersburg, 190000, Russia

*e-mail: fedorinov_asperant_accaunt@mail.ru

Abstract

The application of signal filtering is a necessary and important process in modern technology and science, as it helps to improve the quality and reliability of the data that we receive from various sources. The paper presents a method for improving the accuracy and reliability of an integrated navigation signal filtering system based on the use of redundancy of optimal finite-time information processing methods without feedback and with feedback in conditions of complete a priori certainty and parametric a priori uncertainty using optimal automatic identification of meter states according to the criterion of V.A. Kotelnikov. An important advantage of using an integrated system is to improve flight safety and the accuracy of the measured parameters. The information and measurement system under study contains a two-channel measurement system and two finite-time information processing algorithms. The method of complex optimal invariant linear filtering of discrete signals of information and measurement systems based on the use of a difference signal filter (FRS) is considered in conditions of complete a priori certainty of measurement interference and in conditions of a priori parametric uncertainty regarding the type of models of low-frequency measurement interference. Measurement interference models can be, in general, arbitrary random fluctuation processes or an additive combination of arbitrary fluctuations and a regular type of quasi-deterministic non-stationary random process. It is assumed that the difference signal filter implements an algorithm for filtering signals in accordance with optimally identified states by the meters of the complex system according to the criterion of V.A. Kotelnikov. The simulation was carried out using Mathcad.

Keywords:

Optimal filtering, finite-time signal estimation methods, redundancy of filtering algorithms of a complex system

References

  1. Tyapkin P.S. Hardware and software complex for testing methods of blind signal processing in radio systems. Trudy MAI. 2023. No. 129. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=173029. DOI: 10.34759/trd-2023-129-17
  2. Seidzh E., Mels Dzh. Teoriya otsenivaniya i ee primenenie v svyazi i upravleniya (The theory of evaluation and its application in communication and management). Moscow: Svyaz' Publ., 1976. 495 p.
  3. Medich Dzh. Statisticheski optimal'nye lineinye otsenki i upravlenie (Statistically optimal linear estimates and control). Moscow: Energiya Publ., 1973. 440 p.
  4. Shakhtarin B.I. Fil'try Vinera i Kalmana (Wiener and Kalman filters). Moscow: Gelios ARV Publ., 2008. 408 p.
  5. Ovakimyan D.N., Zelenskii V.A., Kapalin M.V., Ereskin I.S. Research of methods and development of algorithms for integrating navigation information. Trudy MAI. 2023. No. 132. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=176849
  6. Detkov A.N. Optimal discrete filtering of samples of a continuous random process against the background of correlated Markov noise. Trudy MAI. 2022. No. 126. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=169002. DOI: 10.34759/trd-2022-126-16
  7. Ivanov Yu.P., Sinyakov A.N., Filatov I.V. Kompleksirovanie informatsionno-izmeritel'nykh ustroistv letatel'nykh apparatov (Integration of information and measuring devices of aircraft). Ltningrad: Mashinostroenie Publ., 1984. 208 p.
  8. Ivanov Yu.P., Nikitin V.G. Informatsionno-statisticheskaya teoriya izmerenii. Metody optimal'nogo sinteza informatsionno-izmeritel'nykh, kriterii optimizatsii i svoistva otsenok (Information and statistical theory of measurements. Methods of optimal synthesis of information and measurement, optimization criteria and evaluation properties). Saint Petersburg: GUAP Publ., 2011. 102 p.
  9. Pugachev V.S. Teoriya sluchainykh funktsii (Theory of random functions). Moscow: Fizmatgiz Publ., 1962. 882 p.
  10. Ivanov Yu.P. The finite-time method of optimal filtering of discrete signals. Pribory i Sistemy. Upravlenie, Kontrol', Diagnostika. 2018. No. 5. P. 23-28. (In Russ.)
  11. Frenks L. Teoriya signalov (The theory of signals). Moscow: Sovetskoe radio Publ., 1974. 344 p.
  12. Bukhalev V.A., Boldinov V.A. Signal filtering with low-frequency interference in measuring and information systems of unmanned aerial vehicles. Trudy MAI. 2017. No. 97. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=87283
  13. Tang. Pham Van, Thang Nguyen Van, Duc Anh Nguyen, Trinh Chu Duc. 15-State Extended Kalman Filter Design for INS/GPS Navigation System. Journal of Automation and Control Engineering. 2015. V. 3 (2), P. 109-114. DOI: 10.12720/joace.3.2.109-114
  14. Awasthi V., Krishna R. A Comparison of Kalman Filter and Extended Kalman Filter in State Estimation. International Journal of Electronics Engineering. 2011. V. 3, No. 1, P. 67-71.
  15. Glushkov A.N., Moiseev S.N., Ispulov A.A., Filippov A.V., Nikolaev S.V. A method for evaluating the accuracy of alignment of on-board aircraft location systems. Trudy MAI. 2022. No. 127. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=170346. DOI: 10.34759/trd-2022-127-16
  16. Gorbunov S.A., Nenashev V.A., Mazhitov M.V., Khadur A.A. Algorithm for estimating the coordinates of the helicopter state in an on-board radar station. Trudy MAI. 2022. No. 127. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=170348. DOI: 10.34759/trd-2022-127-18
  17. Andria Gregorio, Mario Savino, Amerigo Trotta. Windows and interpolation algorithms to improve electrical measurement accuracy. IEEE Transactions on Instrumentation and Measurement. 1989. V. 38 (4), P. 856-863. DOI: 10.1109/19.31004
  18. Bukirev A.S. A method for diagnosing a complex of aircraft avionics based on machine learning. Trudy MAI. 2023. No. 133. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=177672
  19. Ovakimyan D.N., Zelenskii V.A., Kapalin M.V., Ereskin I.S. Research of methods and development of algorithms for integrating navigation information. Trudy MAI. 2023. No. 132. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=176849
  20. Chernodarov A.V., Ivanov S.A. Model identification and adaptive noise filtering of inertial meters. Trudy MAI. 2018. No. 99. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=91962
  21. Vovasov V.E., Betanov V.V., Turlykov P.Yu. Integration of a navigation receiver and accelerometers for estimating coordinates and orientation of highly dynamic objects. Trudy MAI. 2017. No. 96. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=85834
  22. Lebedev G.N., Mikhailin D.A., Rumakina A.V. Multistage identification of immeasurable flight parameters when combining signals of on-board measuring instruments Trudy MAI. 2016. No. 91. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=75637


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