Guangdong University of Technology

Kungan Zeng

Shengli Xie

Kan Xie

Zhenni Li

Victor Fedorovich Kuzin

Xin Li

Bo Li

Ci Chen

Rong Yuan

Mingwei Wang

Jianhao Tang

Qingsong Yu

Qianming Wang

Qiming Chen

Hailun Tan

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.

Satellite multipath signal identification method based on temporality and spatial interaction

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.

Beidou satellite ephemeris prediction method and system based on water-wave parallel network

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.

High-precision beidou satellite positioning method and system for complex urban environment

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.

Suppression method for multipath signal of image mode based on correlation peaks of satellite baseband signal

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Guangdong, China

Kungan Zeng