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.


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    Title :

    Recursive Polynomial Minimum Mean-Square Error Estimation with Applications to Orbit Determination


    Contributors:

    Published in:

    Publication date :

    2020-01-16


    Size :

    16 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English