Signal Detection and Modulation Classification for Satellite Communications

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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

121 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning
JulkaisupaikkaNew York, NY, USA
KustantajaAssociation for Computing Machinery (ACM)
Sivut114–118
Sivumäärä5
ISBN (elektroninen)9781450375733
DOI - pysyväislinkit
TilaJulkaistu - 22 lokakuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Signal Processing and Machine Learning - Online, Beijing, Kiina
Kesto: 22 lokakuuta 202024 lokakuuta 2020
Konferenssinumero: 3
http://www.spml.net/2020.html

Conference

ConferenceInternational Conference on Signal Processing and Machine Learning
LyhennettäSPML
MaaKiina
KaupunkiBeijing
Ajanjakso22/10/202024/10/2020
www-osoite

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