Comparison of UKF and EKF Kalman filter performance in satellite
One of the most important issues in the space field of countries is the tracking of low earth orbit (LEO) satellites with high accuracy. Due to the fact that conventional filters are not able to track systems with nonlinear dynamics and their tracking is associated with many errors, the use of nonlinear filters is recommended. To solve nonlinear and noisy filtering problems, the Kalman filter algorithm is one of the most suitable methods. This filter is based on the principle of linearization of measurements and model development using Taylor series expansion. The Kalman filter can change the state variables of the system in non-linear stochastic systems where there is disturbance in the process and noise in sensor measurements. Estimate the optimal form. In this article, the satellite motion equations are applied separately to the developed nonlinear filters (EKF) and the sampler Kalman filter (UKF), and finally, by examining the performance of these two filters, it is observed that the sampler Kalman filter performs better compared to the Kalman filter. has developed The satellite studied in this article is NOAA19 meteorological satellite.
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