One of the major challenges for state estimation algorithms, such as the Kalman filter, is the impact of outliers that do not match the assumed process and measurement noise. When these errors occur, they can induce large state estimate errors and even filter divergence. Although there are robust filtering algorithms that can address measurement outliers, in general, they cannot provide robust state estimates when state propagation outliers occur. This paper presents a robust recursive filtering algorithm, the l1-norm filter, which can provide reliable state estimates in the presence of both measurement and state propagation outliers. In addition, Monte Carlo simulations and vision-aided navigation experiments demonstrate that the proposed algorithm can provide improved state estimation performance over existing robust filtering approaches. Presented as Paper 2013-5039 at the AIAA Guidance, Navigation, and Control Conference, Boston, MA, 19-22 August 2013


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    Title :

    Robust State Estimation with Sparse Outliers




    Publication date :

    2015




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    55.54 Flugführung
    Local classification TIB:    770/7040



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