Long-term autonomous time-keeping of navigation constellations based on sparse sampling LSTM algorithm

The system time of the four major navigation satellite systems is mainly maintained by multiple high-performance atomic clocks at ground stations. This operational mode relies heavily on the support of ground stations. To enhance the high-precision autonomous timing capability of next-generation navigation satellites, it is necessary to autonomously generate a comprehensive space-based time scale on orbit and make long-term, high-precision predictions for the clock error of this time scale. In order to solve these two problems, this paper proposed a two-level satellite timing system, and used multiple time-keeping node satellites to generate a more stable space-based time scale. Then this paper used the sparse sampling Long Short-Term Memory (LSTM) algorithm to improve the accuracy of clock error long-term prediction on space-based time scale. After simulation, at sampling times of 300 s, 8.64 × 104 s, and 1 × 106 s, the frequency stabilities of the spaceborne timescale reach 1.35 × 10–15, 3.37 × 10–16, and 2.81 × 10–16, respectively. When applying the improved clock error prediction algorithm, the ten-day prediction error is 3.16 × 10–10 s. Compared with those of the continuous sampling LSTM, Kalman filter, polynomial and quadratic polynomial models, the corresponding prediction accuracies are 1.72, 1.56, 1.83 and 1.36 times greater, respectively.