Abstract: A Huber-based Kalman Filter (HKF) is presented to non-Gaussian random measurement errors. The measurement noise uncertainty is tackled at each filter step by minimizing a criterion function that is original from Huber technique. A recursive algorithm is also provided to solve the criterion function. The proposed HKF algorithm has been tested in attitude estimation using gyroscope and star tracker sensors for a single spacecraft in flight simulations. Simulation results demonstrate the superior performance of the proposed filter as compared to the standard Kalman Filter (KF) in the presence of non-Gaussian measurement noise.