A Study on Satellite Attitude Estimation Method Using Weighted Adaptive Extended Kalman Filter for Blind Zone Compensation

In this paper, a fusion method based on the Extended Kalman Filter (EKF) is proposed to overcome the limitation of star sensor outages, where valid star images cannot be acquired. The proposed framework integrates GNSS, IMU, and multiple star sensors while dynamically adjusting sensor weights in real time based on anomaly detection. The algorithm evaluates the reliability of each star sensor and automatically reduces its influence in the presence of observation outages or detected anomalies, thereby compensating with GNSS/IMU-based attitude estimation. Simulation results under various scenarios demonstrate that the proposed framework outperforms conventional single star sensor EKF and fixed-weight approaches in terms of attitude estimation accuracy. Owing to its real-time applicability, the proposed method is expected to enhance the reliability of next-generation high-precision satellite navigation systems.