Process noise is often used in estimation filters to account for unmodeled and mismodeled accelerations in the dynamics. The process noise covariance acts to inflate the state covariance over propagation intervals, increasing the uncertainty in the state. In scenarios where the acceleration errors change significantly over time, the standard process noise covariance approach can fail to provide effective representation of the state and its uncertainty. Consider covariance analysis techniques provide a method to precompute a process noise covariance profile along a reference trajectory using known model parameter uncertainties. The process noise covariance profile allows significantly improved state estimation and uncertainty representation over the traditional formulation. As a result, estimation performance on par with the consider filter is achieved for trajectories near the reference trajectory without the additional computational cost of the consider filter. The new formulation also has the potential to significantly reduce the trial-and-error tuning currently required of navigation analysts. A linear estimation problem as described in several previous consider covariance analysis studies is used to demonstrate the effectiveness of the precomputed process noise covariance, as well as a nonlinear descent scenario at the asteroid Bennu with optical navigation.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Precomputing Process Noise Covariance for Onboard Sequential Filters


    Contributors:


    Publication date :

    2017-04-24



    Type of media :

    Miscellaneous


    Type of material :

    No indication


    Language :

    English







    Precomputing Process Noise Covariance for Onboard Sequential Filters

    Olson, Corwin G. / Russell, Ryan P. / Carpenter, J. Russell | AIAA | 2017


    Utilizing determined optimized time windows for precomputing optimal path matrices to reduce computer resource usage

    STEWART JOHN BRIAN / WEERAPURAGE DINESH PREMALAL / CARNES TIMOTHY ALAN et al. | European Patent Office | 2019

    Free access