Instantaneous Ghost Detection Identification in Automotive Scenarios

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%.