Adaptive Kalman Filter with Probabilistic Gain Adjustment for Estimation of Satellite Position in the Presence of Measurement Faults

In this study, a probabilistic adaptive filtering technique is described for the Extended Kalman filter (EKF) algorithm, which is used to estimate Low Earth Orbit (LEO) satellite position, velocity, and clock bias using Global Navigation Satellite System (GNSS) distance measurements. The proposed probabilistic adaptive extended Kalman filter (pAEKF) algorithm is based on tracking normalized innovation sequences in the filter and calculating the probability of normal operation of the estimation system. The filter gain is adjusted based on this probability to maintain the filter's tracking performance despite inaccurate measurements.