@inproceedings{HaagDuraisamyBlessingetal.2020,
  author    = {Haag, Stefan and Duraisamy, Bharanidhar and Blessing, Constantin and Marchthaler, Reiner and Koch, Wolfgang and Fritzsche, Martin and Dickmann, J{\"u}rgen},
  title     = {OAFuser: Online Adaptive Extended Object Tracking and Fusion using automotive Radar Detections},
  series = {2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)},
  booktitle = {2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)},
  publisher = {IEEE},
  address   = {Karlsruhe},
  isbn      = {978-1-7281-6422-9},
  doi       = {10.1109/MFI49285.2020.9235222},
  pages     = {303 -- 309},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}