| Paper ID | MLSP-47.4 |
| Paper Title |
NNAKF: A NEURAL NETWORK ADAPTED KALMAN FILTER FOR TARGET TRACKING |
| Authors |
Sami Jouaber, Mines ParisTech/Thales LAS, France; Silvère Bonnabel, Mines ParisTech/UNC, France; Santiago Velasco-Forero, Mines ParisTech, France; Marion Pilté, Thales LAS, France |
| Session | MLSP-47: Applications of Machine Learning |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-APPL] Applications of machine learning |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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 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 (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. |
