Segmentation of Range-Azimuth Maps of FMCW Radars with a Deep Convolutional Neural Network
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-45382-3_12
Reference12 articles.
1. Bilik, I., Longman, O., Villeval, S., Tabrikian, J.: The rise of radar for autonomous vehicles: signal processing solutions and future research directions. IEEE Signal Process. Mag. 36(5), 20–31 (2019). https://doi.org/10.1109/MSP.2019.2926573
2. Capon, J.: High-resolution frequency-wavenumber spectrum analysis. Proc. IEEE 57(8), 1408–1418 (1969). https://doi.org/10.1109/PROC.1969.7278
3. Dickmann, J., et al.: Automotive radar the key technology for autonomous driving: from detection and ranging to environmental understanding. In: 2016 IEEE Radar Conference (RadarConf), pp. 1–6 (2016). https://doi.org/10.1109/RADAR.2016.7485214,ISSN: 2375-5318
4. Dimitrievski, M., Shopovska, I., Hamme, D.V., Veelaert, P., Philips, W.: Weakly supervised deep learning method for vulnerable road user detection in FMCW radar. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1–8 (2020). https://doi.org/10.1109/ITSC45102.2020.9294399
5. Kaul, P., de Martini, D., Gadd, M., Newman, P.: RSS-net: weakly-supervised multi-class semantic segmentation with FMCW radar. In: 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 431–436 (2020). https://doi.org/10.1109/IV47402.2020.9304674. ISSN 2642-7214
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