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.
Author: | Stefan Haag, Bharanidhar Duraisamy, Constantin Blessing, Reiner Marchthaler, Wolfgang Koch, Martin Fritzsche, Jürgen Dickmann |
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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 |
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): | ![]() |