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Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?

, , , , , and . IEEE International Conference on Information Fusion (FUSION), page 1--8. IEEE, (2020)
DOI: 10.23919/fusion45008.2020.9190338

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Polarimetric Information Representation for Radar based Road User Detection with Deep Learning., , , , and . FUSION, page 1-6. IEEE, (2021)RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications, , , , , , and . CoRR, (2021)Detection and Tracking on Automotive Radar Data with Deep Learning., , , , , , and . FUSION, page 1-7. IEEE, (2020)Comparison of random forest and long short-term memory network performances in classification tasks using radar., , , and . SDF, page 1-6. IEEE, (2017)Road User Detection on Polarimetric Pre-CFAR Radar Data Level., , , , and . IEEE Robotics Autom. Lett., 8 (6): 3558-3565 (June 2023)Machine learning applied to radar data: classification and semantic instance segmentation of moving road users. Dortmund University, Germany, (2021)base-search.net (ftunivdortmund:oai:eldorado.tu-dortmund.de:2003/40162).Motion Classification and Height Estimation of Pedestrians Using Sparse Radar Data., , , , and . SDF, page 1-6. IEEE, (2018)Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?, , , , , and . FUSION, page 1-8. IEEE, (2020)RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications., , , , , , and . FUSION, page 1-8. IEEE, (2021)