Classification of Automotive Targets Using Inverse Synthetic Aperture Radar Images
نویسندگان
چکیده
Traditionally, point cloud representations or Doppler spectrograms have been generated from short-range automotive radars for dynamic object detection and classification. In this work, we propose using inverse synthetic aperture radar (ISAR) images obtained range compensated turning targets the classification of different types vehicles. We experimentally demonstrate that ISAR provide rich features such as dimensions, trajectory, number wheels vehicles Additionally, present a simulation framework generating large volumes realistic at millimeter-wave frequencies training classifiers. The model incorporates scattering phenomenology commonly found along with range-Doppler-based clutter receiver noise. is validated measurement data gathered an radar. database are subsequently classified traditional machine learning techniques deep neural networks based on transfer learning. show offer accuracy above 90% robust to both noise clutter.
منابع مشابه
Inverse Synthetic Aperture Radar
1The school of ITEF, The University of Queensland, Brisbane 4072, Australia 2Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy 3 School of Information Technology & Electrical Engineering, University of Queensland, Brisbane 4072, Australia 4Radar Modelling & Analysis Group, Electronic Warfare & Radar Division, Defence Science & Technology Organisation...
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ژورنال
عنوان ژورنال: IEEE transactions on intelligent vehicles
سال: 2022
ISSN: ['2379-8904', '2379-8858']
DOI: https://doi.org/10.1109/tiv.2022.3146639