Signal Detection and Modulation Classification for Satellite Communications

Veronica Toro Betancur, Augusto Carmona Valencia, José Ignacio Marulanda Bernal

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Amateur ground stations are gaining increasing importance as both academic and hobby activities. However, due to the limited energy resources available in amateur satellites, ground stations need to be located in isolated places in order to establish a reliable communication. This usually implies limited Internet access. Hence, ground stations need to be able to recognize incoming signal without completely relying on an Internet connection. For this reason, we propose an algorithm to estimate parameters such as amplitude, center frequency, bandwidth and modulation type for amateur radio applications. For signal detection, we use an absolute-valued sinc approximation which estimates the center frequency and bandwidth of signals with signal-to-noise ratios over -6 dB with a precision of 5% and 2% respectively. In addition, Support Vector Machines (SVM) binary classifiers are used in series to classify the four most common modulation types used in amateur satellites. With accuracies over 90%, SVM outperforms solutions based on Artificial Neural Networks.
Original languageEnglish
Title of host publicationProceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages5
ISBN (Electronic)9781450375733
Publication statusPublished - 22 Oct 2020
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Signal Processing and Machine Learning - Online, Beijing, China
Duration: 22 Oct 202024 Oct 2020
Conference number: 3


ConferenceInternational Conference on Signal Processing and Machine Learning
Abbreviated titleSPML
Internet address


  • Modulation classification
  • signal detection
  • Spectrum sensing
  • Support vector machines
  • Artificial neural networks
  • Amateur Satellites

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