ATTITUDE DETERMINATION FOR SMALL SATELLITE CUBESAT IN LOW EARTH ORBIT

Abstract

Accurate attitude determination is essential for the reliable operation of CubeSats in Low Earth Orbit (LEO), where limited onboard resources, sensor uncertainty, magnetic disturbances, and eclipse conditions can significantly affect estimation performance. This study evaluated a sensor-fusion-based Extended Kalman Filter (EKF) for CubeSat attitude determination using measurements from a three-axis gyroscope, magnetometer, and sun sensor. A quantitative simulation-based experimental design was employed to assess estimator performance under nominal conditions, increased sensor noise, magnetic disturbance, and eclipse. The simulation comprised 144,000 attitude-state observations generated across repeated experimental scenarios. Performance was evaluated using attitude root mean square error (RMSE), convergence time, and computational efficiency. The results indicated that the proposed EKF achieved a mean RMSE of 0.18° under nominal conditions, which increased to 0.43° under elevated sensor noise, 0.61° under magnetic disturbance, and 0.74° during eclipse. The EKF also demonstrated shorter convergence time than the benchmark methods, although it required greater computational resources. Overall, the findings indicated that multi-sensor EKF fusion provided accurate and robust attitude estimation while maintaining computational feasibility for CubeSat applications. The study highlighted the importance of adaptive sensor weighting, fault-tolerant estimation, and computational optimization for reliable attitude determination under degraded LEO operating conditions.