A new information formulation of the Kalman filter is presented where the information matrix is parameterized as the product of an upper triangular matrix, a diagonal matrix, and the transpose of a triangular matrix (referred as UDU factorization). The UDU factorization of the Kalman filter is known for its numerical stability; this paper extends the technique to the information filter. A distinct characteristic of the new algorithm is that measurements can be processed as vectors, while the classic UDU factorization requires scalar measurement processing, i.e., a diagonal measurement noise covariance matrix.


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    Title :

    Information Formulation of the UDU Kalman Filter


    Contributors:


    Publication date :

    2019-02-01


    Size :

    939755 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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