一种基于感知增强轨迹预测的无人机资源优化方法
By constructing a three-dimensional low-altitude scene model and using perception-enhanced trajectory prediction based on multimodal state sequences, the problems of UAV trajectory prediction accuracy and resource scheduling lag were solved, achieving high-precision and robust resource allocation and scheduling decisions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CIVIL AVIATION FLIGHT UNIV OF CHINA
- Filing Date
- 2026-05-13
- Publication Date
- 2026-07-17
AI Technical Summary
Existing UAV trajectory prediction methods lack accuracy in complex low-altitude environments, lack foresight in resource scheduling, and lack uncertainty expression in prediction results, leading to lag in resource allocation and insufficient scheduling robustness.
A three-dimensional low-altitude scene model is constructed, and a multimodal state sequence is formed by integrating UAV operation, environment, link and mission status. Deep fusion and feature extraction are performed through a perception-enhanced trajectory prediction model. A synesthetic computing resource optimization model is constructed using position probability distribution parameters and predicted risk scores to achieve rolling time-domain closed-loop update of prior pre-allocation and online correction.
It significantly improves the accuracy and generalization ability of trajectory prediction in complex low-altitude environments, realizes the forward-looking configuration of bandwidth, power and sensing tasks, enhances the robustness of scheduling and system stability, and adapts to highly dynamic and strongly occluded scenarios.
Smart Images
Figure CN122198562B_ABST