BDS navigation satellite clock difference prediction based on PSO-Elman neural network

Satellite clock error is one of the important factors affecting the positioning accuracy of navigation and positioning system. Aiming at the problem of optimizing the precision clock error prediction performance of the BeiDou Navigation Satellite System (BDS), a method of optimizing the Elman neural network clock error prediction model based on particle swarm optimization (PSO) is proposed to solve the influence of the local optimal problem of Elman neural network on the clock error prediction results. Firstly, the clock error product is preprocessed. The initial weights and thresholds of Elman neural network are determined by iterative optimization of PSO algorithm, and the preprocessed sequence data are used for training modeling. The BDS precision clock error product data provided by IGS Data Analysis Center (WHU) of Wuhan University are used to predict the clock error, and then the prediction results are restored to predict the clock error. The results show that compared with the quadratic polynomial (QP) model, the polynomial (SA) model with additional period term, and the grey (GM) model, the accuracy is improved by 90.7%, 84.2%, 81.6%, and the stability is improved by 85.3%, 76.3%, 36.1%, respectively. The experimental results show that the prediction accuracy and stability of PSO-Elman model are significantly improved in 1−12 h short term forecast simulation, which verifies the feasibility of the proposed method.