Multiple Model Adaptive Estimation of Satellite Attitude using MEMS Gyros

Recently, MEMS gyroscopes are receiving attention for future use attitude determination systems in micro-satellites, because of advantages of light weight, low cost, and low power consumption. However, the high noise levels and bias drifts of these sensors currently limits use for high-precision applications. A multiple model adaptive estimation algorithm is employed to estimate the magnitudes of noise variances in a gyroscope model. The estimated values are used in an extended Kalman filter for attitude estimation. It is demonstrated through simulations and experiments that the adaptive estimation of noise parameters improves attitude estimation performance.