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• W2000931029 abstract "The neural extended Kalman filter is an adaptive state estimation routine that can be used in target-tracking systems to aid in the tracking through maneuvers without prior knowledge of the targets' dynamics. Within the neural extended Kalman filter, a neural network is trained using a Kalman filter training paradigm that is driven by the same residual as the state estimator. The difference between the a priori model used in the prediction steps of the estimator and the actual target dynamics is approximated. An important benefit of the technique is its versatility because little if any a priori knowledge of the target dynamics is needed. This allows the technique to be used in a generic tracking system that will encounter various classes of targets. In this paper, the neural extended Kalman filter is applied simultaneously to three separate classes of targets, each with different maneuver capabilities. The results show that the approach is well suited for use within a tracking system with multiple possible or unknown target characteristics. © 2010 Wiley Periodicals, Inc." @default.
• W2000931029 created "2016-06-24" @default.
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• W2000931029 date "2010-05-01" @default.
• W2000931029 modified "2023-09-23" @default.
• W2000931029 title "Tracking of multiple target types with a single neural extended Kalman filter" @default.
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