A Review on Performance Optimization of Remote Sensing CubeSats Using Artificial Intelligence Techniques: Attitude Control, Data Transmission, and Thermal Management

Farhangi, Mahdi; Aghabalaei, Amir; Jamour, Yahya

doi:10.61186/jgst.14.3.115

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Volume 14, Issue 3 (3-2025)

JGST 2025, 14(3): 115-125

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A Review on Performance Optimization of Remote Sensing CubeSats Using Artificial Intelligence Techniques: Attitude Control, Data Transmission, and Thermal Management

Mahdi Farhangi

, Amir Aghabalaei ^*

, Yahya Jamour

Abstract: (140 Views)

Remote sensing CubeSats are valuable tools in space missions. This paper analyses the key challenges and innovations in three essential domains of these satellites: Attitude Determination and Control Systems (ADCS), data transmission to Earth, and thermal management. In the ADCS section, the paper examines challenges related to size and weight limitations, high precision, and the need for rapid responsiveness. The use of artificial intelligence and machine learning algorithms to enhance the performance of these systems—including neural networks and extended Kalman filters for CubeSat attitude control—is explored, with positive impacts on accuracy and error reduction analyzed. The data transmission section reviews issues related to bandwidth, data volume management, and transmission delays. The optimization of the data transmission process through compression algorithms and artificial intelligence techniques for data management and classification is discussed, focusing on reducing unnecessary data and improving communication efficiency. The thermal management section analyses temperature control challenges in CubeSats and proposes solutions such as thermal materials and coatings, along with precise thermal behavior simulation using artificial neural networks. This study evaluates methods for optimizing and predicting thermal conditions to maintain the performance of sensitive systems in space environments, with results indicating reduced processing time and computational costs. The paper also reviews innovative projects such as IPEX and Amazonia-1, analyzing the impact of AI technologies on enhancing CubeSat performance and efficiency. The findings of this review could contribute to advancing space technologies and enhancing remote sensing mission capabilities.

Article number: 8

Keywords: CubeSat, Remote Sensing, Artificial Intelligence, Performance Optimizations, Attitude Control, Thermal Management

Full-Text [PDF 683 kb] (75 Downloads)

Type of Study: Tarviji | Subject: Geo&Hydro
Received: 2024/11/6

References

1. K. Khurshid, R. Mahmood, and Q. Ul Islam, "A survey of camera modules for CubeSats - Design of imaging payload of ICUBE-1," RAST 2013 - Proc. 6th Int. Conf. Recent Adv. Sp. Technol., pp. 875-879, 2013, doi: 10.1109/RAST.2013.6581337. [DOI:10.1109/RAST.2013.6581337]

2. M. Islam Bappy et al., "Advanced payload architecture for a hyperspectral earth imaging CubeSat based on Software Defined Radio and Deep Neural Network Cube Sat Ground Station View project CubeSat View project Advanced payload architecture for a hyperspectral earth imaging CubeS," no. July 2020, 2018, [Online]. Available: https://www.researchgate.net/publication/331534101.

3. N. Ivliev et al., "First Earth-Imaging CubeSat with Harmonic Diffractive Lens," Remote Sens., vol. 14, no. 9, 2022, doi: 10.3390/rs14092230. [DOI:10.3390/rs14092230]

4. L. Feruglio, "Artificial Intelligence for Small Satellites Mission Autonomy," Dr. Diss. Politec. di Torino, 2017.

5. P. A. Oche, G. A. Ewa, and N. Ibekwe, "Applications and Challenges of Artificial Intelligence in Space Missions," IEEE Access, vol. 12, pp. 44481-44509, 2024, doi: 10.1109/ACCESS.2021.3132500. [DOI:10.1109/ACCESS.2021.3132500]

6. E. Allthorpe-Mullis et al., "CubeSat Camera: A Low Cost Imaging System for CubeSat Platforms," 7th Interplanet. CubeSat Work., pp. 1-9, 2018, [Online]. Available: https://icubesat.files.wordpress.com/2018/05/b-35201805251613 paper.pdf

7. M. N. Sarvi and H. Mahdipour, "Performance Evaluation of CubeSats for Remote Sensing Missions: A Review." Journal of Geoinformatics in Civil Engineering (JGCE), 2023.

8. M. Preisinger, "Advancing the Attitude Determination and Control System for the CubeSat MOVE-II," 2019.

9. J. D. Reis Junior, A. M. Ambrosio, F. L. de Sousa, and D. F. Silva, "Spacecraft real-time thermal simulation using artificial neural networks," J. Brazilian Soc. Mech. Sci. Eng., vol. 43, no. 4, p. 198, Apr. 2021, doi: 10.1007/s40430-021-02908-7. [DOI:10.1007/s40430-021-02908-7]

10. L. Feruglio and S. Corpino, "Neural networks to increase the autonomy of interplanetary nanosatellite missions," Rob. Auton. Syst., vol. 93, pp. 52-60, 2017, doi: 10.1016/j.robot.2017.04.005. [DOI:10.1016/j.robot.2017.04.005]

11. J. D. Liddle, A. P. Holt, S. J. Jason, K. A. O'Donnell, and E. J. Stevens, "Space science with CubeSats and nanosatellites," Nat. Astron., vol. 4, no. 11, pp. 1026-1030, 2020, doi: 10.1038/S41550-020-01247-2. [DOI:10.1038/s41550-020-01247-2]

12. J. Goodwill et al., "NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis," 35th Small Satell. Conf., 2021, [Online]. Available: https://ntrs.nasa.gov/api/citations/20210019764/downloads/SSC21-VII-08-TPU_v11.pdf

13. E. E. Secretariat, "Ecss-E-St-50-01C, Space data links - Telemetry synchronization and channel coding," Ecss, no. July, pp. 443-458, 2008.

14. S. Nie, J. M. Jornet, and I. F. Akyildiz, "Deep-learning-based resource allocation for multi-band communications in cubesat networks," 2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings. 2019. doi: 10.1109/ICCW.2019.8757157. [DOI:10.1109/ICCW.2019.8757157]

15. A. Smith, "Space engineering, Communications," Sp. Sci., no. July, pp. 443-458, 2004, doi: 10.1142/9781860944574_0014. [DOI:10.1142/9781860944574_0014]

16. A. Smith, "Space engineering, SpaceWire - Links, nodes, routers and networks," Sp. Sci., no. July, pp. 443-458, 2004, doi: 10.1142/9781860944574_0014. [DOI:10.1142/9781860944574_0014]

17. S. Chien et al., "Onboard autonomy on the intelligent payload experiment CubeSat mission," J. Aerosp. Inf. Syst., vol. 14, no. 6, pp. 307-315, 2017, doi: 10.2514/1.I010386. [DOI:10.2514/1.I010386]

18. J. Castellví Esturi and Jordi, "Feasibility Study of a Multispectral Remote Sensing Mission based on a 6U CubeSat Standard," Oct. 2014, Accessed: Feb. 09, 2024. [Online] Available: http://recercat.cat/handle/2072/242187

19. J. Junior, A. Ambrosio, and F. Sousa, "Real-Time Cubesat Thermal Simulation using Artificial Neural Networks," Journal of Computational Interdisciplinary Sciences, vol. 8, no. 2. 2017. doi: 10.6062/jcis.2017.08.02.0126. [DOI:10.6062/jcis.2017.08.02.0126]

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Farhangi M, Aghabalaei A, Jamour Y. A Review on Performance Optimization of Remote Sensing CubeSats Using Artificial Intelligence Techniques: Attitude Control, Data Transmission, and Thermal Management. JGST 2025; 14 (3) : 8
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