Abstract: Disclosed is a BeiDou satellite ephemeris prediction method and system based on a water-wave parallel network. The method includes: receiving broadcast ephemeris data; calculating according to the broadcast ephemeris data to obtain a sequence of extrapolation errors and historical extrapolated states; and inputting the sequence of extrapolation errors and the historical extrapolated states into a pre-trained prediction model to output an error prediction result. The system includes: a data receiving module, a dynamical calculation module, an error prediction module and a correction module. The present disclosure allows for accurate ephemeris prediction at a lower computational cost, and can be widely applied in the field of trajectory prediction.

Abstract: Disclosed is a high-precision BeiDou satellite positioning method and system for a complex urban environment. The method includes the following steps: acquiring measurement data; solving the measurement data based on Kalman filtering before predicting to obtain an initial positioning result; performing graph-based data modeling based on the measurement data to obtain a sky satellite graph; and performing feature extraction on the sky satellite graph based on a Transformer model with a graph-structure perception module, and outputting a position correction value. By using the present disclosure, the positioning accuracy and generalization performance of a positioning model in complex scenarios can be improved. The present disclosure is widely applicable in the field of satellite positioning.

Abstract: Disclosed is a suppression method for a multipath signal of an image mode based on correlation peaks of a satellite baseband signal, including the steps of: constructing a real-world direct-multipath two-dimensional color image mode data set; and building a multipath suppression model of a deep learning network based on a long-short term memory (LSTM) and a self-attention mechanism module, and training the model. In the present disclosure, a satellite signal can be quickly captured without losing the sensitivity of the captured signal, and a Beidou satellite baseband signal can be captured from a signal recorded in the real world, which enables the model to learn complex signal patterns, improving the accuracy and robustness of multipath signal suppression in urban complex scenes.

Abstract: The present disclosure provides a satellite multipath signal identification method based on temporality and spatial interaction. The method includes: acquiring satellite data, and dividing the satellite data into a time series dataset and a multi-satellite input dataset; building a multipath signal identification model, and inputting the time series dataset and the multi-satellite input dataset into the multipath signal identification model, where the multipath signal identification model includes a long short-term memory (LSTM) network, a transformer block, and a fully connected network; performing, by the LSTM network, feature extraction on the time series dataset to acquire a time series feature; performing, by the transformer block, feature extraction on the multi-satellite input dataset to acquire an environmental characterization; and fusing, by the fully connected network, the time series feature and the environmental characterization to acquire a multipath signal identification result.