State and Parameter Estimation of Nonlinear Systems Using Extended Kalman Filter Based Sensor Fusion Approach

The primary objective of this paper is to devise and execute sensor fusion employing the Extended Kalman Filter (EKF) for parameter estimation in nonlinear systems. Sensor fusion tackles a data processing challenge where individual measurements from diverse sensors are conducted independently, yet the measured values exhibit correlation due to the system dynamics. Furthermore, these measurements are affected by various types of noise. Leveraging the Extended Kalman Filter (EKF) for sensor fusion enables the extraction of accurate information from imprecise measurements. The study employs the Extended Kalman Filter (EKF) algorithm for sensor fusion in the context of the Lorenz system and the Wilberforce Pendulum. Numerical simulations are conducted to validate the efficacy of the proposed methodology.