Analysis of the Neural Extended Kalman Filter for Target Tracking using Different Neural Network Functions

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


Neural Networks, Kalman Filter, Target Tracking, Hidden Layer, Function Approximation


The neural extended Kalman filter is an adaptive estimation technique that has been shown to improve target-tracking performance when the target is maneuvering. The technique relies upon a neural network which is trained on-line to modify the target motion model. Different mathematical functions have been proposed and implemented as the hidden-layer squashing function of the neural network. For a general tracking application where a wide variety of targets with different maneuver specifications are present, the performance of these different hidden layer functions is analyzed to provide a baseline metric for meaningful comparison and evaluation. Using these results, the neural extended Kalman filter tracking system with the overall best tracking performance for manoeuvring targets can be selected.

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