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OAFuser: Online Adaptive Extended Object Tracking and Fusion using automotive Radar Detections

: Haag, S.; Duraisamy, B.; Blessing, C.; Koch, W.; Marchthaler, R.; Fritzsche, M.; Dickmann, J.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Robotics and Automation Society; Informationstechnische Gesellschaft -ITG-; Verband der Elektrotechnik, Elektronik, Informationstechnik -VDE-:
IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020 : 14-16 September 2020, virtuell, Karlsruhe, Germany
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-6422-9
ISBN: 978-1-7281-6421-2
ISBN: 978-1-7281-6423-6
International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) <2020, Online>
Fraunhofer FKIE ()

This paper presents the Online Adaptive Fuser: OAFuser, a novel method for online adaptive estimation of motion and measurement uncertainties for efficient tracking and fusion by applying a system of several estimators for ongoing noise along with the conventional state and state covariance estimation. In our system, process and measurement noises are estimated with steady-state filters to obtain combined measurement noise and process noise estimators for all sensors in order to obtain state estimation with a linear Minimum Mean Square Error (MMSE) estimator and accelerating the system's performance. The proposed adaptive tracking and fusion system was tested based on high fidelity simulation data and several real-world scenarios for automotive radar, where ground truth data is available for evaluation. We demonstrate the proposed method's accuracy and efficiency in a challenging, highly dynamic scenario where our system is benchmarked with Multiple Model filter in terms of error statistics and run time performance.