The utilization of Low Earth Orbit (LEO) satellites is anticipated to augment various aspects of traditional GNSS-based Positioning, Navigation, and Timing (PNT) services. While the LEO satellite orbital products can nowadays be produced with rather high accuracy in real-time of a few centimeters, the precision of the LEO satellite clock products that can be achieved in real-time is less studied. The latter, however, plays an essential role in the LEO-augmented positioning and timing performances. In real-time, the users eventually use the predicted LEO satellite clocks, with their precision determined by both the near-real-time clock precision and the prediction time needed to match the time window for real-time applications, i.e., the precision loss during the prediction phase. In this study, a real-time LEO satellite clock determination method, consisting of near-real-time clock determination with ultra-short-term clock prediction is proposed and implemented. The principles and strategies of this method are discussed in detail. The proposed method utilized Kalman-filter-based processing, but supports restarts at pre-defined times, thus hampering continuous bias propagation and accumulation from ancient epochs. Based on the method, using Sentinel-3B GNSS observations and the real-time GNSS products from the National Center for Space Studies (CNES) in France, the near-real-time LEO satellite clocks can reach a precision of 0.2 to 0.3 ns, and the precision loss during the prediction phase is within 0.07 ns for a prediction time window from 30 to 90 s. This results in a total error budget in the real-time LEO satellite clocks of about 0.3 ns.

High-precision Low Earth Orbit (LEO) satellite clocks are essential for LEO-augmented Positioning, Navigation, and Timing (PNT) services. Nowadays, high-precision LEO satellite clocks can be determined in real-time using a Kalman filter either onboard or on the ground, as long as the GNSS observations collected onboard LEO satellites can be transmitted to the ground in real-time. While various real-time and high-precision GNSS products are available nowadays in the latter case, their continuity and latencies in engineering reality are not as perfect as expected and will lead to unignorable impacts on the precision of the real-time LEO satellite clocks. In this study, based on real observations of Sentinel-3B, the impacts of different latencies and continuity of the real-time GNSS products on LEO real-time clocks are determined and discussed for two scenarios, namely the “epoch estimation” and “arc estimation” scenarios. The former case refers to the traditional filter-based processing epoch-by-epoch, and the latter case connects LEO satellite clocks from different rounds of filter-based processing under a certain arc length. The two scenarios lead to the “end-loss” and “mid-gap” situations. Latencies of the real-time GNSS products are discussed for the cases of orbit-only latency, clock-only latency, and combined forms, and different handling methods for the missing GNSS satellite clocks are discussed and compared. Results show that the real-time LEO satellite clock precision is very sensitive to the precision of real-time GNSS satellite clocks, and prediction of the latter becomes essential in case of their latencies. For the “end-loss” situation, with a latency of 30 to 120 s for the GNSS real-time clocks, the LEO satellite clock precision is reduced from about 0.2 to 0.28–0.57 ns. Waiting for the GNSS products in case of their short latencies and predicting the LEO satellite clocks instead could be a better option. For “arc-estimation”, when the gap of GNSS real-time products increases from 5 to 60 min, the real-time LEO clock precision decreases from 0.26 to 0.32 ns.

Low Earth Orbit (LEO) satellite augmentation has become a hot research topic for future Positioning, Navigation, and Timing (PNT) applications. To provide real-time LEO-augmented PNT services, high-precision real-time LEO satellite clocks are required. In practice, latencies in the clock determination could range from minutes to hours, depending on the concrete data tracking/transmission strategies and potential operational constraints. Under such circumstances, real-time LEO satellite clocks are basically predicted clocks, whereas the prediction accuracy could play a major role when the prediction time extends over a few minutes. The LEO satellite clock estimates are subject to various systematic effects, resulting in highly nonlinear behavior that is difficult to capture with a single deterministic model. To address this, Deep Learning (DL) models are applied to predict the LEO satellite clocks using real data from the Sentinel-3B satellite. Three novel approaches are proposed in this contribution: 1) an improved Autoformer model enhanced by a pre-training and fine-tuning strategy; 2) an optimized Long Short-Term Memory (LSTM) prediction model; and 3) a Wavelet–LSTM model, where wavelet decomposition enables multi-scale analysis of the clock series to augment the LSTM's predictive capability. The results of the three models are compared with a mathematical polynomial periodic model. Experimental results indicate that while the first two DL models underperform in clock prediction, i.e., often fall short of the polynomial periodic model in accuracy, the Wavelet–LSTM model presents the best performance for all prediction times. Under evaluation mode 1 (average prediction accuracy), it attains a prediction accuracy of approximately 0.05 ns for short-term predictions (within 10 min) and maintains accuracy within about 0.3 ns for long-term predictions (up to 60 min), indicating the potential applicability of the high-precision PNT applications.