Key Technologies for Stabilization in Space Laser Communication Tracking Systems
DOI:
https://doi.org/10.61173/b4jqgr95Keywords:
space laser communications, stable tracking, optical phased arrays, deep learningAbstract
Free-space laser communication is poised to revolutionize next-generation space networks by offering unparalleled bandwidth and security. However, its deployment is critically challenged by the demanding requirement for stable pointing and tracking under dynamic disturbances such as satellite platform micro-vibrations and atmospheric turbulence. This review comprehensively surveys the key technologies designed to overcome these challenges, encompassing digital twin-driven modeling, advanced spot localization algorithms, optical phased arrays for nonmechanical beam steering, and deep learning techniques for wavefront prediction and compensation. The comparative analysis reveals that while these approaches have significantly advanced the field—enabling sub-microradian accuracy and enhanced robustness—fundamental bottlenecks persist. These include limited adaptability to extreme environments, high hardware dependency, and challenges in system-level integration. It is conclude that the future trajectory of stable tracking technology lies in the intelligent convergence of these domains, which is essential for developing the robust, autonomous systems required to realize seamless integrated air-space-ground networks.
References
[1] Li Rui, Lin Baojun, Liu Yingchun, et al. A survey on laser space network: terminals, links, and architectures. IEEE Access, 2022, 10: 34815-34834.
[2] Wang Tanshu, Lin Peng, Dong Fang, et al. Current Status and Prospects of Space Laser Communication Technology. Chinese Journal of Engineering Science, 2020, 22(3): 92-100.
[3] Lin Li, Gong Xue, Zhu Feihu, et al. Simulation and Experiment on the Influence of Micro Vibration on Pointing Measurement of High-Performance Spacecraft. Journal of Deep Space Exploration, 2023, 10(3): 277-282.
[4] Yang H, Niu Z K, Fan Q R, et al. The Digital Twin Framework for the Physical Wideband and Long‐Haul Optical Fiber Communication Systems. Laser & Photonics Reviews, 2024, 18(10): 2400234.
[5] Wang D S, Zhang Z G, Zhang M, et al. The role of digital twin in optical communication: fault management, hardware configuration, and transmission simulation. IEEE Communications Magazine, 2021, 59(1): 133-139.
[6] Faruk M S, Savory S J, et al. Measurement informed models and digital twins for optical fiber communication systems. Journal of Lightwave Technology, 2023, 42(3): 1016-1030.
[7] Song Yuchen, Zhang Min, Zhang Yao, et al. Lifecycle management of optical networks with dynamic-updating digital twin: a hybrid data-driven and physics-informed approach. IEEE Journal on Selected Areas in Communications, 2025.
[8] Bao Rui, Yu Deyang, Chen Fei, et al. Research on highprecision position detection based on a driven laser spot in an extreme ultraviolet light source. Photonics, 2024, 11(1): 75.
[9] Fang Fan, Hou Xia, Huang Yingxia, et al. Design and Verification of Small and Light Tracking Turntable for Space Laser Communication. Current Optics and Photonics, 2025, 9(3): 304-316.
[10] Wu Pengfei, Wang Huiliang, Lei Sichen, et al. Research on Spot Center Localization Algorithm in Atmospheric Turbulence Environment. Acta Photonica Sinica, 2022, 51(3): 0301002.
[11] Wang Guanhua, Yang Fang, Song Jian, et al. Free space optical communication for inter-satellite link: Architecture, potentials and trends. IEEE Communications Magazine, 2024, 62(3): 110-116.
[12] Guo Yongjun, Guo Yuhao, Li Chunshu, et al. Integrated optical phased arrays for beam forming and steering. Applied Sciences, 2021, 11(9): 4017.
[13] Liu Qinghui, Di Yihang, Zhang Mengmeng, et al. Research progress on atmospheric turbulence perception and correction based on adaptive optics and deep learning. Advanced Photonics Research, 2025, 6(7): 2400204.
[14] Wang Kaiqiang, Zhang Mengmeng, Tang Ju, et al. Deep learning wavefront sensing and aberration correction in atmospheric turbulence. PhotoniX, 2021, 2(1): 8.
[15] Wang Ning, Zhu Licheng, Ma Shuai, et al. Deep learningbased prediction algorithm on atmospheric turbulence-induced wavefront for adaptive optics. IEEE Photonics Journal, 2022, 14(5): 1-10.
[16] Zhu Jingyang, Shi Yuanming, Zhou Yong, et al. Hierarchical Dean&Francis ISSN 2959-6157 learning and computing over space-ground integrated networks. IEEE Transactions on Mobile Computing, 2025.
[17] Wang Qian, Li Wenfeng, Yu Zheqi, et al. An overview of emergency communication networks. Remote Sensing, 2023, 15(6): 1595.
[18] Qi Fei, Ge Mang, Zhang Shaowei, et al. A multi-layer architecture for space-air-ground network and IoT services. 2021 International Wireless Communications and Mobile Computing, IEEE, 2021: 1809-1813.
[19] Banafaa Mohammed, Shayea Ibraheem, Din Jafri, et al. 6G mobile communication technology: Requirements, targets, applications, challenges, advantages, and opportunities. Alexandria Engineering Journal, 2023, 64: 245-274.
[20] Chowdhury Mostafa Zaman, Shahjalal Md, Ahmed Shakil, et al. 6G wireless communication systems: Applications, requirements, technologies, challenges, and research directions. IEEE Open Journal of the Communications Society, 2020, 1: 957-975.
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