A 2-D Intercept Problem using a Neural Extended Kalman Filter for Target Tracking

K.A. Kramer and S.C. Stubberud (USA)


Neural Networks, Kalman Filter, Target Tracking, TargetIntercept, Control


The neural extended Kalman filter is a technique that learns unmodeled dynamics while performing state estimation in the feedback loop of a control system. This coupled system performs the standard estimation of the states of the plant while estimating a function to approximate the difference between the given state coupling function model and the behavior of the true plant dynamics. At each sample step, this new model is added to the existing model to improve the state estimate. The neural extended Kalman filter is applied to a two dimensional intercept problem and the results are compared to those obtained from a standard tracking system. Using the intercept control approach of following the trajectory of a target, the neural state estimation is applied when modeling errors or target maneuvers are incorporated into the system. The new trajectory model can be better approximated in flight allowing a closer intercept of the target.

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