Instantaneous Ghost Detection Identification in Automotive Scenarios

Prophet R, Martinez Garcia J, Fuentes MJC, Ebelt R, Vossiek M (2019)

Publication Language: English

Publication Status: Accepted

Publication Type: Conference contribution, Conference Contribution

Future Publication Type: Conference contribution

Publication year: 2019

Event location: Boston, USA

DOI: 10.1109/RADAR.2019.8835603

Abstract

This paper addresses one of the most serious drawbacks of automotive radar sensors: The frequent occurrence of ghost detections. As such detections may cause undesired behavior, they need to be partially or completely eliminated. We present an algorithm, which uses a machine-learning-based classifier to distinguish between real and ghost detections. In contrast to other papers, this approach addresses all causes of ghost detections and not only simple multipath scenarios. Real world experiments – also in challenging situations – show success rates of 91%.

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How to cite

APA:

Prophet, R., Martinez Garcia, J., Fuentes, M.J.C., Ebelt, R., & Vossiek, M. (2019). Instantaneous Ghost Detection Identification in Automotive Scenarios. In Proceedings of the Radar Conference 2019, Boston (MA), USA, April 2019. Boston, USA, US.

MLA:

Prophet, Robert, et al. "Instantaneous Ghost Detection Identification in Automotive Scenarios." Proceedings of the Radar Conference 2019, Boston (MA), USA, April 2019, Boston, USA 2019.

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