Distributed anti-interruption cooperative navigation method based on generative graph adaptive filtering

By generating virtual observation and prediction covariance matrices using a masked spatiotemporal graph Transformer model and combining it with adaptive Kalman filtering, the problem of error accumulation caused by link interruption in distributed cooperative navigation systems is solved, achieving high-precision navigation estimation and robustness.

CN122108116APending Publication Date: 2026-05-29THE 54TH RESEARCH INSTITUTE OF CHINA ELECTRONICS TECHNOLOGY GROUP CORPORATION
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
THE 54TH RESEARCH INSTITUTE OF CHINA ELECTRONICS TECHNOLOGY GROUP CORPORATION
Filing Date
2026-01-28
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

In complex environments where satellite navigation signals are denied, millimeter-wave and terahertz signals in distributed cooperative navigation systems are easily blocked, leading to sudden interruptions in communication and measurement links. Traditional retransmission mechanisms fail, and missing observations result in error accumulation. Existing deep learning methods lack group perception and uncertainty quantification, leading to error cascading.

Method used

A masked spatiotemporal graph Transformer model is used to generate virtual observation and prediction covariance matrices. Combined with adaptive Kalman filtering, adaptive measurement pairs are constructed, and uncertainty perception is used to suppress error cascading, thereby achieving high-precision navigation.

Benefits of technology

In the event of a link interruption, high-precision virtual observations are generated to suppress error cascading, maintain the consistency of navigation estimation, dynamically adjust filter weights, and improve the robustness and accuracy of the navigation system.

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Abstract

The application provides a distributed anti-interruption cooperative navigation method based on generative graph adaptive filtering, and relates to the technical field of wireless communication and navigation. The method comprises the following steps: modeling a cluster topology as a dynamic space-time graph, constructing a mask space-time graph Transformer model, predicting the mean value and covariance matrix of a virtual observation value by using the geometric distribution of neighbor nodes and the group semantic correlation, constructing an uncertainty-aware distributed extended Kalman filter, adaptively switching physical observation and virtual observation based on link state, taking the network-generated covariance as an adaptive weighting item to be integrated into the filter update step, dynamically adjusting the Kalman gain, and determining the navigation state estimation result of the target node. By completing the missing information during the link interruption, the error propagation in the distributed network is effectively suppressed, and the anti-interruption cooperative navigation is realized.
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