Accurate satellite clock error prediction is of vital importance for satellite stable operation when satellites' clocks are not able to compare with those on the ground. Aiming at the problem of intermediate and short-term clock error prediction, a polynomial model is chosen to predict the clock error, and an orthogonal iterative functional networks algorithm based on a sliding window model is designed, which takes advantage of the non-linear learning ability of functional networks to fit and analyze the clock error model. The analysis shows that when the prediction time is less than 12 h, the predicted errors are between 0.2 ns and 0.5 ns, which is equivalent to IGU P. When the prediction time is 24 h, the overall errors are around 1ns, which is slightly less than IGU P. When the prediction time is a satellite week, the maximum error may reach 130 ns, which does not meet the reqirement of the satellites. It is concluded that the algorithm of the paper is suited for the short-term clock error prediction but not the intermediate and longtime prediction.
LIU Qiang
,
CHEN Xihong
,
HU Denghua
,
XUE Lunsheng
,
HAN Beibei
,
ZHANG Qun
. Application of Orthogonal & Iterative Functional Networks to Intermediate and Short-term Clock Error Prediction[J]. Science & Technology Review, 2014
, 32(27)
: 38
-42
.
DOI: 10.3981/j.issn.1000-7857.2014.27.006
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