This paper presents a systematic generalization of the linear update structure associated with the extended Kalman filter for high-order polynomial estimation of nonlinear dynamical systems. The minimum mean-square error criterion is used as the cost function to determine the optimal polynomial update during the estimation process. The high-order series representation is implemented effectively using differential algebra techniques. Numerical examples show that the proposed algorithm, named the high-order differential algebra Kalman filter, provides superior robustness and/or mean-square error performance as compared to linear estimators under the condition considered.
Recursive Polynomial Minimum Mean-Square Error Estimation with Applications to Orbit Determination
Journal of Guidance, Control, and Dynamics ; 43 , 5 ; 939-954
2020-05-01
Article (Journal)
Electronic Resource
English
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