Satellite clock bias prediction based on particle swarm optimization and weighted grey regression combined model

Abstract:

To improve the accuracy and stability of satellite clock bias (SCB) prediction, an adaptive SCB prediction method was proposed based on the combination of particle swarm optimization (PSO) and weighted grey regression with index function and linear function approximation. First, considering the phenomenon of frequent SCB clock jumps, the clock jump data was detected and excluded through median absolute deviation (MAD) before modeling, and the piecewise linear interpolation method was used to complement the missing clock bias data. Then, in order to deal with the system noise of the SCB data, the three-point smoothing method was used to smooth the clock bias data. After the processing, an SCB prediction model based on the combination of exponential function and linear function approximation for weighted grey regression was established. Aiming at the problem that the increasing precision factor in the model was difficult to determine, a PSO algorithm was used to adaptively optimize the accuracy increasing factor. Finally, a 6-hour forecast test was performed by adopting the post-accurate SCB product released on the IGS server and combining with four typical trends. Experimental results show that the prediction performance of the proposed method was significantly better than those of other commonly used models. Compared with the quadratic polynomial prediction model (QPM), grey prediction model (GM (1,1)), modified exponential curve method (MECM), and autoregressive moving average model (ARMA), the 6-hour average forecast accuracy (RMS) and stability (Range) of the proposed method were increased by 79.10%, 44.00%, 80.70%, 32.30%, and 63.10%, 29.80%, 77.60%, 26.30%, respectively.