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Adaptive Kalman Filtering for Spacecraft Attitude Estimation

並列摘要


An adaptive Kalman filter (AKF) algorithm is proposed to model and approximation errors in this work. The adaptive algorithm for model and approximation errors is attained by using an upper bound for the state error prediction covariance matrix. The proposed adaptive filter algorithm has been tested in attitude estimation using gyroscope and star tracker sensors for single spacecraft in flight simulations. Simulation results demonstrate the superior performance of the proposed filter as compared to the standard Kalman filter.

參考文獻


Fu, M., de Souza, C. E., and Luo, Z. Q., “Finite-horizon robust Kalman filter design,” IEEE Transactions on Signal Processing, Vol. 49, No. 9, 2001, pp. 2103-2112.
Mahmoud, M. S., Xie, L. H., and Soh, Y. C., “Robust Kalman filtering for discrete state-delay systems,” IEE Proceedings Control Theory and Application, Vol. 147, 2000, pp. 613-618.
Geromel, J. C. and Oliveira, M. C., “ 2 H and H∞ robust filtering for convex bounded uncertain systems,” IEEE Transactions on Automatic Control, Vol. 46, No. 1, 2001, pp. 100-107.
Subrahmanya, N. and Shin, Y. C., “Adaptive divided difference filtering for simultaneous state and parameter estimation,” Automatica, Vol. 45, 2009, pp. 1686-1693.
Farrenkopf, R. L., “Analytic Steady-State Accuracy Solutions for Two Common Spacecraft Attitude Estimators,” Journal of Guidance and Control, Vol. 1, No. 4, 1978, pp. 282-284.

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