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Communication dans un congrès
NNAKF: A NEURAL NETWORK ADAPTED KALMAN FILTER FOR TARGET TRACKING
Abstract : An adaptive three-dimensional Kalman filter for the tracking of maneuvering targets in three dimensions is proposed. In the radar industry, numerous trackers are based on a constant velocity model, with a process noise covariance matrix Q which is adapted in real time to enhance tracking: it is kept at moderate values during straight lines where the constant velocity assumption applies and is increased during maneuvers. In the present paper we advocate a novel method to increase Q during maneuvers (and hence the Kalman gains) based on a recurrent neural network (RNN). The difficulty and the interest of our approach lies in the fact the neural network is trained together with the filter, by backpropagation through the filter, and hence learns the covariance matrix such as to directly maximize the accuracy of the final output.
https://hal-unc.archives-ouvertes.fr/hal-03463046
Contributeur : Silvere Bonnabel Connectez-vous pour contacter le contributeur
Soumis le : jeudi 2 décembre 2021 - 11:18:40
Dernière modification le : dimanche 5 décembre 2021 - 03:13:27
Contributeur : Silvere Bonnabel Connectez-vous pour contacter le contributeur
Soumis le : jeudi 2 décembre 2021 - 11:18:40
Dernière modification le : dimanche 5 décembre 2021 - 03:13:27
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ICASSP_modified.pdf
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