Short-term Prediction for Polar Motion Based on Chaos and Volterra Adaptive Algorithm

  Objectives   The polar motion (PM) is an important part of the Earth rotation parameters (ERP). the prediction error of ERP can be effectively reduced by improving the prediction accuracy of PM.   Methods   Aiming at the complex time variation characteristics of PM, a high-precision prediction method based on the Volterra adaptive algorithm was proposed for the first time, which taken the PM series as chaos. Firstly, the maximum Lyapunov exponent was calculated by using the small data sets method. This analysis proves that the PM has chaotic characteristics. Then two experiments were performed for the second order Volterra adaptive algorithm.   Results   The results of the experimental results were compared with the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) and Bulletin A, the official forecast product of IERS. The results show that the prediction accuracy of this method is higher than that of EOP PCC, and Xp component prediction accuracy is improved significantly, Yp component can be also slightly improved. Compared with Bulletin A, the accuracy of the two forecast results has advantages and disadvantages.   Conclusions   The example further proves that our method can obtain good forecast results in the short-term polar motion forecast, especially the prediction period is more accurate than that of the small period.