US 12,468,331 B2
Real-time sliding ultrashort-term forecast model algorithm based on frequency data and phase data
Yaping Gao, Chengdu (CN); Wenju Fu, Chengdu (CN); Guo Chen, Chengdu (CN); Weige Zi, Chengdu (CN); Jiapeng Li, Chengdu (CN); and Pengcheng Zhao, Chengdu (CN)
Assigned to Chengdu University of Technology, Chengdu (CN); and Wuhan University, Wuhan (CN)
Filed by Chengdu University of Technology, Chengdu (CN); and Wuhan University, Wuhan (CN)
Filed on Jan. 24, 2024, as Appl. No. 18/421,435.
Application 18/421,435 is a continuation of application No. PCT/CN2024/071286, filed on Jan. 9, 2024.
Prior Publication US 2025/0224759 A1, Jul. 10, 2025
Int. Cl. G06F 1/08 (2006.01)
CPC G06F 1/08 (2013.01) 6 Claims
OG exemplary drawing
 
1. A real-time sliding ultrashort-term forecast model algorithm based on frequency data and phase data, executed by a processor of a computer apparatus, and the real-time sliding ultrashort-term forecast model algorithm based on frequency data and phase data comprising:
converting clock error phase data from a satellite navigation system into the frequency data;
iteratively processing the frequency data to eliminate frequency outliers from the frequency data, comprising:
calculating a standard deviation value of the frequency data, wherein a calculation formula for calculating the standard deviation value of the frequency data is as follows:

OG Complex Work Unit Math
wherein sigma represents the standard deviation value of the frequency data, ave represents an average of the frequency data, fi represents one of the frequency data, and n represents a total number of the frequency data;
eliminating a target frequency data fm0 farthest from the average ave of the frequency data from the frequency data to satisfy a condition fm0=MAX(fabs(fn−ave)) to thereby obtain updated frequency data, and updating the sigma based on the updated frequency data to obtain an updated sigma;
designing different thresholds according to the updated sigma and different satellite orbit types;
determining whether fabs(fm0−ave) is greater than a corresponding one threshold of the different thresholds;
in response to fabs (fm0−ave) being greater than the corresponding one threshold of the different thresholds, considering that fm0 is a frequency outlier; and
continuing iterating a next frequency maximum point fm1 until no epoch is beyond a corresponding threshold;
performing a real-time sliding clock error forecast to update a forecast epoch, comprising:
calculating a root mean square error of fitting residuals as a basis for setting a threshold range; and
determining whether a fitting residual of the forecast epoch is beyond the threshold range;
in response to the fitting residual of the forecast epoch being beyond the threshold range, eliminating data of the forecast epoch; and
in response to the fitting residual of the forecast epoch being not beyond the threshold range, sliding forward by one epoch, and performing fitting and forecast again; and
using a real-time clock error forecast value obtained through performing the forecast to correct a clock frequency deviation, for thereby improving forecast accuracy, real-time performance and data stability of a satellite clock error.