Abstract
In view of the increasing on-board processing capacity, this paper investigates the possibility of the communication reconnaissance on LEO constellation satellite platforms, and proposes a wireless signal recognition algorithm based on deep learning. The proposed algorithm visualizes the wireless signal as a picture based on the basic digital signal processing, as a result, the signal recognition problem is subtly transferred to an object detection problem recurring in the field of Computer Vision (CV). Then, it co-opts deep learning models in CV field in order to realize the end-to-end signal recognition and improve the performance. Validating results on the field-collected signal dataset with 12 types and 4740 samples show that, the algorithm can effectively identify the waveform types and time/frequency coordinates of communication signals with the precision 89%, which is 40% higher than traditional algorithms.
This work is supported by the National Key R&D Program (2016YFB0501104) and Special Fund for National Defense Technology Innovation (18-163-11-ZT-003-027-01).
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Zhou, X., Xiao, Y., Hu, M., Liu, L. (2020). Wireless Signal Recognition Based on Deep Learning for LEO Constellation Satellite. In: Yu, Q. (eds) Space Information Networks. SINC 2019. Communications in Computer and Information Science, vol 1169. Springer, Singapore. https://doi.org/10.1007/978-981-15-3442-3_23
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DOI: https://doi.org/10.1007/978-981-15-3442-3_23
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