KALMAN FILTER EMPIRICAL OPTIMIZATION

المؤلفون

  • Izziddien Alsogkier Elmergib University

الكلمات المفتاحية:

Kalman Filter، Luen-berger State Observer، Optimal State، Estima-tion، Empirical Optimi-zation

الملخص

In this paper, in order to simplify the understanding of the Kalman filter and its applications in practice, the optimization of Kalman filter is done empirically, and compared with the Luenberger observer.  Therefore, a series of experiments are done for the range of current and delayed Kalman filter innovation gains with specific process dynamic and stochastic parameters. Also, for the sake of comparison, the same is applied on the observer as well. Where, the stochastic process parameters are the stochastic disturbance or the modeling error covariance (Q), and the measurement error covariance (R).  Consequently, the tests of the current Kalman filter showed that the empirically measured optimal Kalman innovation gains are identical to the computations of the Kalman filter algorithms.  On the other hand, the tests for the delayed version showed that empirical optimal innovation gains slightly diverge from computations of the algorithms. 

المراجع

R. Kalman, “A new approach to linear filtering and prediction problems,” ASME Journal of Basic Engineering, 82, pp. 35-45, March 1960.

R. Kalman and R. Bucy, “New results in linear filtering and prediction theory,” ASME Journal of Basic Engineering, 83, pp. 95-108, March 1961.

M. Grewal and A. Andrews, Kalman Filtering Theory and Practice Using MATLAB, John Wiley & Sons, 2008.

Dan Simon, Optimal State Estimation, John Wiley & Sons, 2006.

R. G. Brown and P. Y. C. Hwang. Introduction to Random Signals and Applied Kalman Filtering, Second Edition, John Wiley & Sons, 1992.

A. Gelb, Applied Optimal Estimation, MIT Press, Cambridge, Massachusetts, 1974.

P. Maybeck, Stochastic Models, Estimation, and Control - Volume 1, Academic Press, New York, 1979.

P. Maybeck, Stochastic Models, Estimation, and Control - Volume 2, Academic Press, New York, 1982.

P. Maybeck, Stochastic Models, Estimation, and Control - Volume 3, Academic Press, New York, 1982.

H. Sorenson, Kalman Filtering: Theory and Application, IEEE Press, New York, 1985.

Pereira, Ronaldo FR, et al. "Estimation of the electrical parameters of overhead transmission lines using Kalman Filtering with particle swarm optimization." IET Generation, Transmission & Distribution, 17.1, pp. 27-38, 2023.

Graybill, Philip P., Bruce J. Gluckman, and Mehdi Kiani. "Optimization of an unscented Kalman filter for an embedded platform." Computers in biology and medicine, Volum 146, 105557, July 2022.

Zhang, Alan, and Mohamed Maher Atia. "An efficient tuning framework for Kalman filter parameter optimization using design of experiments and genetic algorithms." NAVIGATION: Journal of the Institute of Navigation, 67. pp. 775-793, 2020.

Eswehli, Asma, and Izziddien Alsogkier. "Observer Empirical Optimization in Open and Closed Loop." 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA. IEEE, 2021.‏

Izziddien Alsogkier. “Empirical Optimization of State Space Controller and Observer Parameters for a Linear Motion Servo Control System.” Libyan International Conference on Electrical Engineering and Technologies (LICEET2018) 3 – 7 March 2018, Tripoli – Libya.

Luenberger, D. G. "Observing the state of a linear system." IEEE Transactions on Military Electronics, pp. 74-80, 1964.

التنزيلات

منشور

2025-07-01

كيفية الاقتباس

Alsogkier, I. (2025). KALMAN FILTER EMPIRICAL OPTIMIZATION. مجلة البحوث الأكاديمية, 29(2), 78–91. استرجع في من https://lam-journal.ly/index.php/jar/article/view/1243

إصدار

القسم

العلوم الهندسية والتطبيقية