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

  • 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.

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Metadaten
Author:Stefan Haag, Bharanidhar Duraisamy, Constantin Blessing, Reiner Marchthaler, Wolfgang Koch, Martin Fritzsche, Jürgen Dickmann
DOI:https://doi.org/10.1109/MFI49285.2020.9235222
ISBN:978-1-7281-6422-9
Parent Title (English):2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
Publisher:IEEE
Place of publication:Karlsruhe
Document Type:Conference Proceeding
Language:English
Year of Completion:2020
Release Date:2021/02/04
Page Number:7
First Page:303
Last Page:309
Institutes:Fakultäten / Informationstechnik
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
Open Access?:nur im Hochschulnetz
Relevance:Peer reviewed nach anderen Listungen (mit Nachweis zum Peer Review Verfahren)
Licence (German):License LogoVeröffentlichungsvertrag ohne Print-on-Demand