Hostname: page-component-5b777bbd6c-gcwzt Total loading time: 0 Render date: 2025-06-18T23:36:28.881Z Has data issue: false hasContentIssue false
  • English
  • Français

Implementation of an intelligent solution for acquiring telemetry from Alsat-2 satellites: leveraging machine learning for polarisation drive

Published online by Cambridge University Press:  14 May 2025

E.H. Bensikaddour Open the ORCID record for E.H. Bensikaddour [Opens in a new window] ,
B. Nasri ,
F. Bouchiba ,
D.E. Baba Hamed  and
S. Bekkar Djelloul Saiah Open the ORCID record for S. Bekkar Djelloul Saiah [Opens in a new window]
E.H. Bensikaddour*
Affiliation:
Satellite Development Center, ASAL, Oran, Algeria
B. Nasri
Affiliation:
Satellite Development Center, ASAL, Oran, Algeria
F. Bouchiba
Affiliation:
Satellite Development Center, ASAL, Oran, Algeria
D.E. Baba Hamed
Affiliation:
Satellite Development Center, ASAL, Oran, Algeria
S. Bekkar Djelloul Saiah
Affiliation:
Satellite Development Center, ASAL, Oran, Algeria
*
Corresponding author: E.H. Bensikaddour; Email: hbensikadour@cds.asal.dz
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents the development and implementation of a comprehensive system for acquiring telemetry from Alsat-2A and Alsat-2B satellites, whose orbits are phased 180 degrees apart, utilising the CDM600 demodulator. Integral to this system is an automatic learning module leveraging machine learning algorithms to optimise circular polarisation selection based on reception conditions. The software segment manages the demodulator, user interface, and coordinates the machine learning algorithm, drawing insights from historical polarisation data to construct predictive models for optimal polarisation selection. Through the integration of machine learning, this system aims to enhance telemetry signal reception quality, contributing to the success (Alsat-2A was launched on 12 July 2010, and Alsat-2B on 26 September 2016) of satellite missions.

Keywords

satellite telemetriespolarisation diversitymachine learningtracking parameters
Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 17
Check for updates
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Chen, M., Mu, Q., Ma, J., et al. (2020). A design of satellite telemetry acquisition system. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M. and Zhang, B. (eds), Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol. 571, Springer, Singapore, 2020, pp 1003–1011. doi:10.1007/978-981-13-9409-6_95.CrossRefGoogle Scholar
Uhlig, T., Sellmaier, F. and Schmidhuber, M. Spacecraft Operations. Berlin: Springer, 2015.CrossRefGoogle Scholar
Weiss, B.M., Clarke, B., Elnourani, M., et al. (2024). Leveraging smart maintenance for satellite health preservation. Paper presented at the 75th International Astronautical Congress, Milan, Italy, 14–18 October 2024. International Astronautical Federation (IAF). Published 31 October 2024. Available at: https://strathprints.strath.ac.uk/id/eprint/91045.Google Scholar
T.W.Future Autonomous Ground Stations. Future Autonomous Ground Stations, n.d. Available at https://telecomworld101.com/future-autonomous-ground-stations/ (accessed March 13, 2025).Google Scholar
Marczewski, T.J. Machine Learning in Mobile Cubesat Command and Control (MC3) Ground Stations. Monterey, CA: Naval Postgraduate School, 2021.Google Scholar
Comtech *CDM-600 Open Network Satellite Modem Installation and Operation Manual*, 2024. Available at https://ai-sat.com/wp-content/uploads/2015/01/Comtech-cdm600L-manual.pdf Google Scholar
Antenna, e. 2024. Available at https://anywaves.com/resources/blog/definition-whats-polarization-for-space-antennas-and-why-is-it-important/#:~:text=Polarization%20diversity%20in%20space%20antennas,polarization%20to%20improve%20communication%20performance Google Scholar
Hassanien, A.E., Darwish, A., and Abdelghafar, S., Machine learning in telemetry data mining of space mission: basics, challenging and future directions, Artif. Intell. Rev., 2020. 53, (5), pp 3201–3230.CrossRefGoogle Scholar
Russo, A. and Lax, G. Using artificial intelligence for space challenges: a survey, Appl. Sci., 2022, 12, (10), p 5106.CrossRefGoogle Scholar
Murphy, J., Ward, J.E., & Mac Namee, B. (2021). Machine learning in space: a review of machine learning algorithms and hardware for space applications. In A. Pakrashi, E. Rushe, M. H. Z. Bazargani, & B. Mac Namee (Eds.), Proceedings of the 29th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2021) (pp. 72–83). CEUR-WS.org, Dublin, Ireland.Google Scholar
Mohamed, M.I., Badran, K.M. and Hussien, A.E. Anomaly detection for agile satellite attitude control system using hybrid deep-learning technique, J. Aerosp. Inf. Syst., 2023, 20, (12): pp 890–904.Google Scholar
Xu, H., et al. Anomaly traffic detection based on communication-efficient federated learning in space-air-ground integration network, IEEE Trans. Wireless Commun., 2023, 22, (12), pp 9346–9360.CrossRefGoogle Scholar
Hussein, M. A real-time anomaly detection in satellite telemetry data using artificial intelligence techniques depending on time-series analysis, J. ACS Adv. Comput. Sci., 2023, 14, (1), 21–45. https://doi.org/10.21608/asc.2023.171575.1011.CrossRefGoogle Scholar
Bua, R. et al. Machine learning radio-frequency-based anomaly detection for ground station and satellite telecommunication. In: Proceedings of the 37th Annual AIAA/USU Conference on Small Satellites, Utah State University, Logan, UT, 2023.Google Scholar
Han, X., Zhang, Y., Li, J., et al. (2023). Anomaly Detection in Time Series Satellite Data Using a Deep Learning Method. In Proceedings of the 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), pp. 123–130. IEEE, Yibin, China.CrossRefGoogle Scholar
Ma, K.Y. (2021). Utilization of Machine Learning Techniques for Managing the Tracking and Data Relay Satellite Constellation. In Proceedings of the 16th International Conference on Space Operations (SpaceOps 2021). International Astronautical Federation (IAF), Virtual Conference.Google Scholar
Bensikaddour, E.H. and Nasri, B. Detection and Prediction of Satellite Telemetry Anomalies. In: Proceedings of the 2024 4th International Conference on Embedded & Distributed Systems (EDiS), Béchar, Algeria. IEEE, Piscataway, NJ, USA, 2024, pp. 310–313. CrossRefGoogle Scholar
Wang, Y., et al. A deep learning anomaly detection framework for satellite telemetry with fake anomalies, Int. J. Aerosp. Eng., 2022, 2022, (1), p 1676933.Google Scholar
Nalepa, J., et al. Evaluating algorithms for anomaly detection in satellite telemetry data, Acta Astronaut., 2022, 198, pp 689–701.CrossRefGoogle Scholar
Thangavel, K., et al. Artificial intelligence for trusted autonomous satellite operations, Prog. Aerosp. Sci., 2024, 144, p 100960.CrossRefGoogle Scholar
Caldas, F., & Soares, C. (2024). Machine learning in orbit estimation: A survey. Acta Astronautica, 220, 97–107. https://doi.org/10.1016/j.actaastro.2024.03.072.Google Scholar
Rösch, C. and Gou, J. (2022). Machine learning for orbit determination. ETH Zurich Research Collection, ETH Library. Available at: https://doi.org/10.3929/ethz-b-000617317.CrossRefGoogle Scholar
LEE, Byoung-sun, KIM, Won-Gil, LEE, Junho, et al. (2018). Machine learning approach to initial orbit determination of unknown LEO satellites. In: 2018 SpaceOps Conference, 28 May – 1 June 2018, Marseille, France. American Institute of Aeronautics and Astronautics (AIAA). p. 2566. https://doi.org/10.2514/6.2018-2566.CrossRefGoogle Scholar
Mortlock, T. and Kassas, Z. M., ÉC;Assessing machine learning for LEO satellite orbit determination in simultaneous tracking and navigation,ÉC; in 2021 IEEE Aerospace Conference (50100), Big Sky, MT, USA, 6–13 March 2021, pp. 1–8, doi: 10.1109/AERO50100.2021.9438144.CrossRefGoogle Scholar
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B.A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166–177. https://doi.org/10.1016/j.isprsjprs.2019.04.015.CrossRefGoogle Scholar
Miralles, P., et al. A critical review on the state-of-the-art and future prospects of machine learning for earth observation operations, Adv. Space Res., 2023, 71, (12), pp 4959–4986.CrossRefGoogle Scholar
Mini, P. and Bhomini, P.A. A study in enhancement of satellite images, Eng. Appl. Artif. Intell., 2024, 138, p 109259.CrossRefGoogle Scholar
Bhattacharyya, A., Kumar, N., Sahu, P., Roy, R., Chatterjee, M., & Paul, S. ÉC;Machine learning and deep learning powered satellite communications: enabling technologies, applications, open challenges, and future research directions, ÉC; International Journal of Satellite Communications and Networking, vol. 41, no. 6, pp. 539–588, 2023.CrossRefGoogle Scholar
Bekkar Djelloul Saiah, S., et al. Analysis of the communication links between the AlSat-1b satellite and the ground station: the impact of the auto tracking system on antenna pointing accuracy, Int. J. Satellite Commun. Netwk., 2021, 39, (5), pp 486–499.CrossRefGoogle Scholar
GALLARDO, MARQUINA, Sergi, Study and simulation of communication links in a LEO satellite constellation based on Link Budget calculations, Thèse de baccalauréat, Universitat Politècnica de Catalunya, 2022, Barcelone, Espagne.Google Scholar
Bensikaddour, E., Nasri, B., Hamed, D.E., et al. Azimuth, elevation, and Doppler shift estimation for LEO satellites based on an open source python package, Automat. Control Comput. Sci., 2023, 57, (2), pp 203–212.CrossRefGoogle Scholar
Rhodes, B.C. PyEphem: Astronomical Ephemeris for Python. Astrophysics Source Code Library, 2011, ascl:1112.014, College Park, MD, USA.Google Scholar
Lighari, R.U.R., Berg, M., Kallankari, J., Parssinen, A., and Salonen, E.T., Analysis of the measured RHCP and LHCP GNSS signals in multipath environment, in Proceedings of the 2016 International Conference on Localization and GNSS (ICL-GNSS), IEEE, 2016, pp. 1–6, Barcelona, Spain.CrossRefGoogle Scholar
Rhys, H., Machine Learning with R, the Tidyverse, and mlr, Manning Publications, 2020, Shelter Island, NY, USA.Google Scholar
Random Forest, S. 2024. Available at https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html Google Scholar
KNN. 2024, Available at https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html Google Scholar