93

A stable iterative algorithm for estimating elements of the matrix gain of the Kalman filter has been developed. The traditional Kalman filter equations are given. Algorithms for autonomous calculation of the stationary Kalman filter gain are presented, which are performed under conditions relating to the system parameters. A non-linear iterative equation is solved for the gain of the Kalman filter. Modeling results are given, these Kalman filtering expressions for a linear discrete system and the actual filtering process is the current process for predicting and correcting recursive and iterative nature.

  • Internet havola
  • DOI
  • UzSCI tizimida yaratilgan sana 07-12-2023
  • O'qishlar soni 93
  • Nashr sanasi 23-10-2023
  • Asosiy tilIngliz
  • Sahifalar99-104
English

A stable iterative algorithm for estimating elements of the matrix gain of the Kalman filter has been developed. The traditional Kalman filter equations are given. Algorithms for autonomous calculation of the stationary Kalman filter gain are presented, which are performed under conditions relating to the system parameters. A non-linear iterative equation is solved for the gain of the Kalman filter. Modeling results are given, these Kalman filtering expressions for a linear discrete system and the actual filtering process is the current process for predicting and correcting recursive and iterative nature.

№ Muallifning F.I.Sh. Lavozimi Tashkilot nomi
1 Zaripov O.O. proff TDTU
2 Sevinov J.U. dotsent TDTU
№ Havola nomi
1 1. A.A. Krasovsky. Handbook on the theory of automatic control. “Science”, Moscow, 1987. 712.
2 2. V.N. Afanasyev. Dynamic control systems with incomplete information: Algorithmic construction. “Publishing House”, 2007.
3 3. S.V. Pervachev., A.I. Perov. Adaptive message filtering. “Radio and Communications”, Moscow, 1991. 160.
4 4. M.A. Ogarkov. Methods for statistical evaluation of random process parameters. “Energo atom publishing”, Moscow, 1990. 208.
5 5. H.Z. Igamberdiyev., A.N. Yusupbekov., O.O. Zaripov., J.U. Sevinov. Algorithms of adaptive identification of uncertain operated objects in dynamical models. “Procedia Computer Science”, 2017. 854.
6 6. J.U. Sevinov., O.O. Zaripov., S.O. Zaripova. The algorithm of adaptive estimation in the synthesis of the dynamic objects control systems. “International journal of advanced science and technology”, 2020. 1096
7 7. N. Assimakis., M. Adam. “Kalman filter Riccati equation for the prediction, estimation and smoothing error covariance matrices. “ISRN Computational Mathematics”, 2013. 7.
8 8. N. Assimakis., M. Adam. Global systems for mobile posi¬tion tracking using Kalman and Lainiotis filters. “The Scientific World Journal”, 2014. 8.
9 9. N. Assimakis., M. Adam. Iterative and algebraic algorithms for the computation of the steady state kalman filter gain. “Hindawi publishing corporation, applied mathematics”, 2014. 10.
10 10. B.D.O. Anderson., J.B. Moore. “Optimal filtering, dover publications”, USA, 2005.
11 11. M.S. Grewal., A.P. Andrews. “Kalman filtering: theory and practice using MATLAB”, USA, 2008.
12 12. R.A. Horn., C.R. Johnson. Matrix analysis, “Cambridge university press”, Cambridge, UK, 2005.
13 13. F.P. Vasiliev. Optimization methods. “Publishing House: Factorial Press”, 2002. 824.
14 14. A.B. Bakushinsky., M.Y. Kokurin. “Iterative methods of solving irregular equations”, Moscow, 2006. 214.
15 15. A.Y. Morozov., D.L. Reviznikov. “Methods of computer simulation of dynamic systems with interval parameters”, Moscow, 2019
Kutilmoqda