Real-Time Estimation of LEO Satellite Clocks Using a Combination of Low- and High-Frequency Clock Solutions

作者
Jiawei Liu,Jinqian Wang,Kan Wang,Wei Xie,Meifang Wu,Ahmed El‐Mowafy,Xuhai Yang
出处
期刊:Navigation: journal of the Institute of Navigation [Wiley]
卷期号:73 (1): navi.739-navi.739 被引量:2
标识
DOI:10.33012/navi.739
摘要

Abstract Low Earth orbit (LEO) satellites are considered as an augmentation to traditional global navigation satellite systems for positioning, navigation, and timing (PNT). To enable real-time LEO-augmented PNT services, particularly for stand-alone positioning, high-precision LEO satellite clock products are required in real time, which is a challenge owing to the high correlation between the clocks and orbits. This contribution introduces an approach for real-time LEO satellite clock estimation using a combination of low- and high-frequency clock solutions. This work introduces predicted clocks based on low-frequency batch least-squares (BLS) clock determination and properly constrains them to improve the next step of Kalman-filter-based high-frequency clock estimation. By using 6-day real onboard Global Positioning System observations of Sentinel-3B, the performance of the predicted clocks from the BLS adjustment is evaluated. These clocks are then introduced into the high-frequency clocks solutions, and the resulting real-time clock estimates are analyzed. The potential of introducing filter-based predicted clocks is also discussed. Results show that the predicted LEO satellite clocks based on the BLS adjustment exhibit a precision of 0.15, 0.22, 0.33, 0.44, and 0.56 ns for prediction windows of 0–3, 3–6, 6–9, 9–12, and 12–15 min, respectively. After properly constraining the introduced predicted clocks within the above-mentioned predicted windows, improvements of 46.38%, 39.33%, 31.77%, 25.62%, and 18.35% were observed in the high-frequency precision, respectively, reducing the clock precision from 0.24 to 0.13 ns for the short prediction window of 0–3 min. For the signal-in-space ranging error (SISRE), corresponding improvements at 34.73%, 26.48%, 18.58%, 13.55%, and 8.93% were achieved, reducing the SISRE from 0.09 to 0.06 m for a prediction window of 0–3 min.

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