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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.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-03229186
Contributor : Sami Jouaber Connect in order to contact the contributor
Submitted on : Tuesday, June 22, 2021 - 2:52:37 PM
Last modification on : Wednesday, November 17, 2021 - 12:31:12 PM
Long-term archiving on: : Thursday, September 23, 2021 - 6:43:57 PM

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Sami Jouaber, Silvere Bonnabel, Santiago Velasco-Forero, Marion Pilte. NNAKF: A Neural Network Adapted Kalman Filter for Target Tracking. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun 2021, Toronto (virtual), Canada. pp.4075-4079, ⟨10.1109/ICASSP39728.2021.9414681⟩. ⟨hal-03229186⟩

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