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- W3176358081 abstract "This paper focuses on the frequently occurring issue of automotive radar sensors: ghost detection. Three data-based approaches, namely random forest, convolutional neural network (CNN), and PointNet++, are adopted to identify ghost detection. Evaluated with the same dataset, random forest and PointNet++, with more than 95% accuracy, are evidently better than CNN in not only city but also motorway scenarios. Furthermore, the influence of various features for each classifier is also analyzed." @default.
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- W3176358081 date "2021-05-07" @default.
- W3176358081 modified "2023-09-26" @default.
- W3176358081 title "Comparison of Different Approaches for Identification of Radar Ghost Detections in Automotive Scenarios" @default.
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- W3176358081 doi "https://doi.org/10.1109/radarconf2147009.2021.9454980" @default.
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