关键词: 智能轨道预报, 月球极低轨, Bi-LSTM模型, 缩放点积注意力机制, 修正预报

Abstract: The prediction of extremely-low lunar orbits is affected by the influence of high-order gravity perturbation, leading the traditional high-order gravity term integral method usually facing great computation cost. To address this problem, an intelligent orbit prediction method is proposed with the fusion of Scaled Dot-Product Attention (SDPA) and advanced Bi-Long Short-Term Memory (Bi-LSTM). This method uses the integration with lower-order spherical harmonic function expansion, combining the Bi- LSTM model to predict the integration error between the lower-order and the high-order gravity term and revising the prediction error, which can accomplish the high-accuracy prediction of extremely-low lunar orbits in a shorter time. Where, a SDPA with the capability of efficient global critical information feature capturing is proposed in Bi-LSTM, improving the learning ability and prediction performance of classical Bi-LSTM. For the mission scenario of extremely-low lunar orbit at 15 km altitude, the proposed intelligent orbit prediction method achieves a final prediction error of 10-meters scale, and reduces about 51.4% total computation time compared to traditional integral prediction method. The improved Bi-LSTM elevates final position prediction accuracy of 23.7%, 13.8% and 17.9% in X axis, Y axis, Z axis compared with general LSTM, respectively.The proposed advanced Bi-LSTM with SDPA intelligent orbits prediction can compensate for the error of integration which only uses the low-order gravity terms, achieving the high-precision prediction under the environment of extremely-low lunar obits with 15 km altitude. Moreover, it can greatly reduce the time cost of the traditional integration prediction method.

Key words: intelligent orbits prediction, extremely-low lunar orbit, Bi-LSTM model, Scaled Dot-Product Attention, prediction revision