Abstract
This paper focuses on the development of a robust multiple model adaptive estimation (RMMAE) algorithm and its performance analysis. The main goal of this work is to enhance the robustness of the estimator against the model parameter identification error. A proof is provided that shows the convergence property of the proposed algorithm. Further analysis shows that the RMMAE algorithm guarantees a bounded energy gain from the model parameter identification error to the estimation error. The performance of the RMMAE is evaluated via simulations for spacecraft autonomous navigation. Simulation results demonstrate the effectiveness of the new algorithm compared with the extended Kalman filter (EKF), the unscented Kalman filter (UKF), the robust Kalman filter (RKF) and the multiple model adaptive estimation (MMAE).
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