Gray particle swarm satellite clock error adaptive prediction method and system

Abstract

The invention provides a gray particle swarm satellite clock error adaptive prediction method and system. Optimizing factors and forgetting factors are introduced into a GM(1,1) model, a gray particle swarm model is established through the least square method, then the optimal optimizing factor is obtained through a particle swarm optimization algorithm, finally, the optimal optimizing factor is substituted into a PGM(1,1) model, clock error prediction is carried out, and a prediction sequence of clock errors is obtained. The prediction precision of the satellite clock errors can be improved, then a precision single-point positioning receiver is assisted in high-precision real-time positioning calculation, the number of training samples needed by the PGM(1,1) prediction model is small, model parameters are adjusted through an adaptive method, the calculation amount is small, and engineering application is convenient; the prediction precision of the satellite clock errors is high, and the errors are controlled within 1 ns.

Description

technical field [0001] The invention relates to a technology in the field of satellite navigation and positioning, in particular to a gray particle swarm satellite clock error self-adaptive prediction method and system. Background technique [0002] The accuracy of satellite clock error prediction directly affects the accuracy of satellite navigation and positioning. The existing gray theory method, that is, the gray system GM(1,1), essentially accumulates the irregular original data to obtain a generated series with strong regularity, and then uses the differential fitting method to re-model, and the GM( 1,1) The data obtained by the model is accumulated and reversed to obtain the predicted value. However, this method has poor model adaptability, flaws in initial value selection, is not suitable for near-exponential sequence fitting, and the background value calculation method is too simple. Contents of the invention [0003] Aiming at the shortcomings of the existing t...

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Problems solved by technology

[0003] Aiming at the shortcomings of the existing technology that it is difficult to apply to near-exponential sequence fitting, the background value calculation method is too simple, and the prediction accuracy is not high, the present invention proposes a gray particle swarm satellite clock error adaptive prediction method and system, which can significantly improve the satellite clock error The prediction accuracy is high, and then assists the precision single point positioning receiver to perform high-precision real-time positioning calculations. The PGM(1,1) prediction model requires a small training sample, and the model parameters are adjusted by an adaptive method, with a small amount of calculation, which is convenient for engineering applications , and the accuracy of satellite clock error prediction is high, and the error is controlled within 1ns

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