Research Article
Estimation of state of nonlinear stochastic dynamic systems with optimized extended Kalman filter
Author(s): Dejan Stošović, Elvir Čajić*, Maid Omerović and Sead Rešić
In this paper, we critically investigate the application of the Extended Kalman Filter (EKF) in the estimation of states of nonlinear stochastic dynamical systems. We apply an algorithmic approach to EKF and investigate its efficiency in state estimation under stochastic conditions. Through simulation example analysis, we provide detailed insights into the performance and advantages of the EKF compared to other national accounting methods. This paper contributes to the understanding and application of EKF in complex nonlinear systems and lays the foundation for further research in state estimation in dynamic systems using adaptive learning methods, algorithm is adjusted to dynamic changes in the system to ensure the best estimate of the situation. In addition, we develop and implement advanced fault detection and correction methods that ensure O-EKF stability and reliability even unde.. Read More»
DOI:
10.37532/2752- 8081.24.8(4).01-06
+447723598358
