BeiDou satellite clock bias prediction algorithm by integrating of SSA and BiLSTM

In response to the challenge of existing satellite clock bias prediction models in capturing its nonlinear characteristics, a Beidou satellite clock bias prediction algorithm by integrating sparrow search algorithm (SSA) and bidirectional long short-term memory network (BiLSTM) is proposed. BiLSTM is employed for forecasting clock bias, and SSA is introduced for network hyperparameter selection, which can better capture the characteristics in sequence data and improve the accuracy of model prediction. Experimental validations are conducted using precise BDS-3 satellite clock bias data provided by the German Research Centre for Geosciences, encompassing clock bias predictions for 1 h, 3 h, 6 h, 12 h, 24 h, and 48 h intervals. In terms of satellite orbit types and model universality, single-day forecast and multi-day forecast are compared with common models. The results show that compared with the polynomial model, wavelet neural network, long short-term memory model, and BiLSTM model, the average accuracy of clock bias prediction of the proposed algorithm is improved by 75.12%%, 67.44%, 75.18% and 48.65%, respectively. © 2024 Editorial Department of Journal of Chinese Inertial Technology. All rights reserved.