Guangdong University of Technology

Zhenni Li

Shengli Xie

Kan Xie

Victor Fedorovich Kuzin

Kungan Zeng

Xin Li

Mingwei Wang

Hailun Tan

Qiming Chen

Qianming Wang

Qingsong Yu

Jianhao Tang

Rong Yuan

Jiaqi Xie

Xiaoying Guo

Yi Ji

Yinghe Xiao

Yihui Cai

Junxian Lin

Jiayin Xiao

Shaolong Zheng

Ci Chen

Bo Li

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

The present disclosure provides a retrieval and push method based on a fine art image tag, including the following steps: establishing a tag model database: training different tag contents with training samples of different subjects and different categories, and categorizing the training samples in the database according to knowledge point tags to obtain the tag model database; retrieval and push: uploading fine art work image samples to the trained tag model database, then extracting knowledge point tags of the fine art work image samples, retrieving associated fine art works, and then pushing the associated fine art works; real-time updating: recording knowledge point tags of input fine art work image samples by the tag model database in real time, and establishing a common tag and a model, and subsequently increasingly pushing contents relevant to the common tag; and generating a user portrait.

Retrieval and push method based on fine art image tag, and use thereof

Growing community of inventors

Guangdong, China

Zhenni Li