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OAFuser: Online Adaptive Extended Object Tracking and Fusion using automotive Radar Detections | IEEE Conference Publication | IEEE Xplore

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


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 f...Show More

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.
Date of Conference: 14-16 September 2020
Date Added to IEEE Xplore: 26 October 2020
ISBN Information:
Conference Location: Karlsruhe, Germany

Funding Agency:


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