Please use this identifier to cite or link to this item:
|Publication type:||Conference paper|
|Type of review:||Editorial review|
|Title:||Drone radio signal detection with multi-timescale deep neural networks|
|Proceedings:||Proceedings of the 4th International Conference on Advances in Signal Processing and Artificial Intelligence|
|Editors of the parent work:||Yurish, Sergey Y.|
|Conference details:||4th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI), Corfu, Greece, 19-21 October 2022|
|Publisher / Ed. Institution:||IFSA Publishing|
|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.|
|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:
|2022_Horn-etal_Drone-radio-signal-detection_ASPAI-fullpaper.pdf||Accepted Version||303.23 kB||Adobe PDF|
Show full item record
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, 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.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.