Herein is presented an efficient nonlinear filtering algorithm called the Gaussian-sum cubature Kalman filter (GSCKF) for the bearings-only tracking problem. It is developed based on the recently proposed cubature Kalman filter and is built within a Gaussian-sum framework. The new algorithm consists of a splitting and merging procedure when a high degree of nonlinearity is detected. Simulation results show that the proposed algorithm demonstrates comparable performance to the particle filter (PF) with significantly reduced computational cost.


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    Title :

    A Gaussian-Sum Based Cubature Kalman Filter for Bearings-Only Tracking


    Contributors:


    Publication date :

    2013-04-01


    Size :

    7088502 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English