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Next-gen satellites: A leap in autonomous timekeeping with LSTM algorithm

Next-gen satellites: a leap in autonomous timekeeping with LSTM algorithm
Clock error forecast flow chart. Credit: Satellite Navigation (2024). DOI: 10.1186/s43020-024-00137-6

Accurate time-keeping is essential for satellite navigation, as even minor time deviations can result in significant positioning errors. Traditional systems rely heavily on ground-based atomic clocks, which pose risks of service interruptions. Due to these challenges, advanced research is necessary to develop more reliable and autonomous satellite time-keeping systems.

Researchers from the University of Chinese Academy of Sciences and the Innovation Academy for Microsatellites have published a study in Satellite Navigation presenting a novel, two-level timing system using a sparse sampling Long Short-Term Memory (LSTM) algorithm to enhance the precision and stability of satellite navigation time-keeping.

The study proposes a two-level satellite timing system to improve the high-precision autonomous timing capability of navigation satellites. The first level uses multiple on each satellite to form a stable time scale, while the second level integrates these individual time scales into a constellation-level time scale using inter-satellite links (ISLs).

A key innovation is the use of a sparse sampling LSTM algorithm, which significantly enhances the accuracy of long-term clock error predictions. Simulation results show that the frequency stability of the spaceborne time scale improves markedly with this approach.

For instance, at a sampling time of 300 seconds, the frequency stability reaches 1.35 × 10-15, and the 10-day prediction error is reduced to 3.16 × 10-10 seconds. This represents a substantial improvement over existing prediction models.

Dr. Wenbin Gong, the lead researcher, stated, "Our two-level timing system and the application of the sparse sampling LSTM represent significant advancements in satellite time-keeping. These innovations not only enhance the precision of satellite navigation but also improve the robustness and reliability of space-based time scales."

The advancements presented in this study have far-reaching implications for the future of and related technologies. Enhanced time-keeping accuracy will lead to improved positioning services, benefiting various applications such as autonomous vehicles, global communication systems, and .

Moreover, the methodology and findings of this research can be applied to other areas requiring precise time synchronization and prediction, further broadening its impact.

More information: Shitao Yang et al, Long-term autonomous time-keeping of navigation constellations based on sparse sampling LSTM algorithm, Satellite Navigation (2024). DOI: 10.1186/s43020-024-00137-6

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