Multi-source data driven unmanned aerial vehicle flight trajectory prediction method and system

By constructing a static spatiotemporal graph and using the Graph Transformer model for encoding and decoding, the problem of difficulty in mining spatiotemporal coupling relationships in multi-source data is solved, and high-precision, high-real-time prediction of UAV flight trajectories is achieved.

CN121902065BActive Publication Date: 2026-06-09NANJING SHENYE INTELLIGENT SYST ENG
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NANJING SHENYE INTELLIGENT SYST ENG
Filing Date
2026-03-23
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing methods for predicting UAV flight trajectories struggle to effectively uncover the spatiotemporal coupling relationships within multi-source data, resulting in high computational complexity, poor real-time performance, and insufficient prediction accuracy.

Method used

A multi-source data-driven approach is adopted to construct an independent static spatiotemporal graph through a spatiotemporal decoupling graph. The graph is then combined with a Graph Transformer model for intra-graph spatial encoding and inter-graph temporal encoding. A sparse attention decoding mechanism is introduced to predict the flight trajectory of UAVs.

Benefits of technology

It improves the accuracy and real-time performance of UAV flight trajectory prediction, effectively utilizes the spatiotemporal correlation characteristics and temporal evolution characteristics of multi-source data, and enhances prediction efficiency.

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Abstract

The application relates to the technical field of unmanned aerial vehicle trajectory prediction, and discloses a multi-source data driven unmanned aerial vehicle flight trajectory prediction method and system, multi-source data of an unmanned aerial vehicle flight state and an external environment are collected and preprocessed, abnormal data is detected, interpolation completion is carried out based on adjacent time points, a spatiotemporal graph sequence is formed based on a spatiotemporal decoupling graph construction method, an input sequence is intercepted through a trend smoothing adaptive window selection algorithm, an improved Graph Transformer model is input, double-branch coding is carried out to extract and fuse features, and then sparse self-attention decoding is carried out to predict a trajectory. The scheme effectively mines a spatiotemporal coupling relationship, reduces a calculation amount, and realizes high-precision and high-real-time prediction of an unmanned aerial vehicle trajectory in a complex environment.
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