Detection of radar blockage is a critical safety function for automotive radar. In this paper, we report on a machine-learning approach to classify the blockage condition in automotive radar, using detection data. We consider logistic regression, tree-bagging, and neural network approaches. We used pruning to reduce the size of the neural network to make it a viable option for embedded processors with limited memory. The results show that the classifiers, especially the neural network, can achieve high accuracy with a low false-alarm rate.
Machine Learning Applied to Blockage Classification in Automotive Radar
2020-11-23
3073154 byte
Conference paper
Electronic Resource
English
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