Abstract
Abstract. In this work, a synthetic aperture radar setup is used for analyzing the mmWave scattering of road surfaces in the automotive 77 GHz band in the laboratory. With this setup, samples of concrete roads in two different surface conditions are investigated, determining the variances in reflectivity depending on material composition and surface structure. Afterward, the distribution of these variations is fitted using probability density functions, namely normal and rayleigh distribution fits. Consequently, the diffuse scattering behavior of concrete roads can be described mathematically. Additionally, previously presented porous asphalt roads are compared and fitted analogously to get a summary of the scattering for all common road surfaces in Germany. Furthermore, a validation of the measurement and the processing by analyzing particularly generated reference samples is performed.
Reference16 articles.
1. Babu, A. and Baumgartner, S. V.: Road Surface Quality Assessment Using Polarimetric Airborne SAR, 2020 IEEE Radar Conference (RadarConf20), 21–25 September 2020, Florence, Italy, IEEE, https://doi.org/10.1109/RadarConf2043947.2020.9266588, 2020. a
2. Biscoping, M. and Kampen, R.: Zusammensetzung von Normalbeton – Mischungsberechnung, InformationsZentrum Beton GmbH, https://mitglieder.vdz-online.de/fileadmin/gruppen/vdz/3LiteraturRecherche/Zementmerkblaetter/ZM_B20_2017_2.pdf (last access: 21 January 2023), 2017. a
3. DIN 1045-2:2008-08: Tragwerke aus Beton, Stahlbeton und Spannbeton – Teil 2: Beton – Festlegung, Eigenschaften, Herstellung und Konformität – Anwendungsregeln zu DIN EN 206-1, Beuth Verlag GmbH, https://doi.org/10.31030/1453177, 2008. a
4. Forbes, C. S., Evans, M., Hastings, N. A. J., and Peacock, B.: Statistical distributions, 4th edn., Wiley, Hoboken, New Jersey, USA, ISBN 9780470627235, 2011. a, b
5. Henry, C., Azimi, S. M., and Merkle, N.: Road Segmentation in SAR Satellite Images With Deep Fully Convolutional Neural Networks, IEEE Geosci. Remote S., 15, 1867–1871, https://doi.org/10.1109/LGRS.2018.2864342, 2018. a