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.


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    Title :

    Machine Learning Applied to Blockage Classification in Automotive Radar


    Contributors:
    Fetterman, Matt (author) / Carlsen, Aret (author) / Ru, Jifeng (author) / Zuo, Yifan (author)


    Publication date :

    2020-11-23


    Size :

    3073154 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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