Bespoke Simulator for Human Activity Classification with Bistatic Radar-Reference-Cited by-同舟云学术

Bespoke Simulator for Human Activity Classification with Bistatic Radar

Published:2022 Issue: Volume: Page:71-85
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
language:
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Author:

Yang Kai,Abbasi Qammer H.^ORCID,Fioranelli Francesco^ORCID,Romain Olivier^ORCID,Le Kernec Julien^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-95593-9_7

Reference43 articles.

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2. Gurbuz, S.Z., Amin, M.G.: Radar-based human-motion recognition with deep learning: promising applications for indoor monitoring. IEEE Signal Process. Mag. 36(4), 16–28 (2019). https://doi.org/10.1109/MSP.2018.2890128

3. Abdur Rahman, M.: A secure occupational therapy framework for monitoring cancer patients’ quality of life. Sensors 19(23), 5258 (2019)

4. Li, H., Cui, G., Kong, L., Guo, S., Wang, M., Yang, H.: Human target tracking for small aperture through-wall imaging radar. IEEE Radar Conf. (RadarConf) 2019, 1–4 (2019)

5. Cippitelli, E., Fioranelli, F., Gambi, E., Spinsante, S.: Radar and RGB-depth sensors for fall detection: a review. IEEE Sens. J. 17(12), 3585–3604 (2017). https://doi.org/10.1109/JSEN.2017.2697077

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