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https://doi.org/10.21256/zhaw-27185| Publication type: | Conference paper |
| Type of review: | Editorial review |
| Title: | Drone radio signal detection with multi-timescale deep neural networks |
| Authors: | Horn, Claus Nyfeler, Matthias Müller, Georg Schüpbach, Christof |
| et. al: | No |
| DOI: | 10.21256/zhaw-27185 |
| Proceedings: | Proceedings of the 4th International Conference on Advances in Signal Processing and Artificial Intelligence |
| Editors of the parent work: | Yurish, Sergey Y. |
| Page(s): | 140 |
| Pages to: | 143 |
| Conference details: | 4th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI), Corfu, Greece, 19-21 October 2022 |
| Issue Date: | 19-Oct-2022 |
| Publisher / Ed. Institution: | IFSA Publishing |
| ISBN: | 978-84-09-45050-3 |
| Language: | English |
| Subjects: | Drone signal detection; Deep learning; Multi-timescale modeling |
| Subject (DDC): | 629: Aeronautical, automotive engineering |
| Abstract: | We develop a multi-timescale deep learning algorithm to detect drones from radio signals. While previous approaches focused on the analysis of high-frequency radio data alone we integrate signals from the higher timescale of the drone communication protocol in an end-to-end architecture. To this end, we develop a new meta-CNN layer, which generalizes the idea of the standard CNN (which slides a single, fully connected kernel along a higher-level input) towards arbitrarily complex kernel models. To detect higher timescale patterns our system uses an LSTM layer in the top layers. As a result, our model is able to extend drone identification abilities significantly toward very small SNRs. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/27185 |
| Fulltext version: | Accepted version |
| License (according to publishing contract): | Not specified |
| Departement: | Life Sciences and Facility Management |
| Organisational Unit: | Institute of Computational Life Sciences (ICLS) |
| Published as part of the ZHAW project: | Drohnenalarm |
| Appears in collections: | Publikationen Life Sciences und Facility Management |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2022_Horn-etal_Drone-radio-signal-detection_ASPAI-fullpaper.pdf | Accepted Version | 303.23 kB | Adobe PDF |  View/Open |
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Horn, C., Nyfeler, M., Müller, G., & Schüpbach, C. (2022). Drone radio signal detection with multi-timescale deep neural networks [Conference paper]. In S. Y. Yurish (Ed.), Proceedings of the 4th International Conference on Advances in Signal Processing and Artificial Intelligence (pp. 140–143). IFSA Publishing. https://doi.org/10.21256/zhaw-27185
Horn, C. et al. (2022) ‘Drone radio signal detection with multi-timescale deep neural networks’, in S.Y. Yurish (ed.) Proceedings of the 4th International Conference on Advances in Signal Processing and Artificial Intelligence. IFSA Publishing, pp. 140–143. Available at: https://doi.org/10.21256/zhaw-27185.
C. Horn, M. Nyfeler, G. Müller, and C. Schüpbach, “Drone radio signal detection with multi-timescale deep neural networks,” in Proceedings of the 4th International Conference on Advances in Signal Processing and Artificial Intelligence, Oct. 2022, pp. 140–143. doi: 10.21256/zhaw-27185.
HORN, Claus, Matthias NYFELER, Georg MÜLLER und Christof SCHÜPBACH, 2022. Drone radio signal detection with multi-timescale deep neural networks. In: Sergey Y. YURISH (Hrsg.), Proceedings of the 4th International Conference on Advances in Signal Processing and Artificial Intelligence. Conference paper. IFSA Publishing. 19 Oktober 2022. S. 140–143. ISBN 978-84-09-45050-3
Horn, Claus, Matthias Nyfeler, Georg Müller, and Christof Schüpbach. 2022. “Drone Radio Signal Detection with Multi-Timescale Deep Neural Networks.” Conference paper. In Proceedings of the 4th International Conference on Advances in Signal Processing and Artificial Intelligence, edited by Sergey Y. Yurish, 140–43. IFSA Publishing. https://doi.org/10.21256/zhaw-27185.
Horn, Claus, et al. “Drone Radio Signal Detection with Multi-Timescale Deep Neural Networks.” Proceedings of the 4th International Conference on Advances in Signal Processing and Artificial Intelligence, edited by Sergey Y. Yurish, IFSA Publishing, 2022, pp. 140–43, https://doi.org/10.21256/zhaw-27185.
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