TY - CHAP U1 - Konferenzveröffentlichung A1 - Haag, Stefan A1 - Duraisamy, Bharanidhar A1 - Blessing, Constantin A1 - Marchthaler, Reiner A1 - Koch, Wolfgang A1 - Fritzsche, Martin A1 - Dickmann, Jürgen T1 - OAFuser: Online Adaptive Extended Object Tracking and Fusion using automotive Radar Detections T2 - 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) N2 - 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. Y1 - 2020 SN - 978-1-7281-6422-9 SB - 978-1-7281-6422-9 U6 - https://doi.org/10.1109/MFI49285.2020.9235222 DO - https://doi.org/10.1109/MFI49285.2020.9235222 SP - 303 EP - 309 S1 - 7 PB - IEEE CY - Karlsruhe ER -