Machine Learning Applied to Blockage Classification in Automotive Radar
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.
2020 IEEE MTT S International Conference on Microwaves for Intelligent Mobility (ICMIM)