Unmanned aerial vehicle target dynamic locking method based on multi-source sensing fusion

By fusing multi-source sensor data and modeling causal relationships, and utilizing Manhattan diagrams and an improved Bellman-Ford algorithm, a target state evolution trajectory is constructed, which solves the problem of unstable target locking by UAVs in complex environments and achieves high-precision and real-time dynamic target tracking.

CN121479650AInactive Publication Date: 2026-02-06SHANDONG JIAOTONG UNIV
View PDF 0 Cites 0 Cited by

Patent Information

Application Number
CN202511601791.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-04
Publication Date
2026-02-06
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing UAV target recognition methods struggle to effectively integrate multi-source sensor data in complex environments, failing to fully characterize the target's dynamic behavior. This results in unstable target locking, decreased prediction accuracy, and a lack of analysis of the main information propagation path during the target's state transition process.

Method used

By employing multi-source sensor data fusion, causal relationship modeling, and an improved Bellman-Ford algorithm, the target state evolution trajectory is constructed through Manhattan diagrams and causal relationship diagrams, identifying the main information propagation channels and key nodes, thereby achieving dynamic and stable locking of UAVs onto moving targets.

Benefits of technology

It significantly improves the accuracy and real-time performance of UAV target tracking in complex environments, enhances the ability to respond to rapid target maneuvers, and possesses noise resistance and intelligent decision-making capabilities, solving the problems of insufficient multi-source data fusion and one-sided behavior modeling in traditional methods.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN121479650A_ABST
    Figure CN121479650A_ABST
Patent Text Reader

Abstract

The invention discloses an unmanned aerial vehicle target dynamic locking method based on multi-source sensing fusion. The method comprises the following steps of 1, collecting and standardizing multi-source original data from an unmanned aerial vehicle sensor; 2, performing feature extraction on the standardized data set to obtain a data feature vector; 3, calculating the similarity between the data feature vectors by using a Manhattan distance, and establishing a Manhattan graph; 4, performing causal analysis on the Manhattan graph to construct a causal relationship graph; 5, performing K-means clustering on nodes in the causal relationship graph to obtain an optimized causal relationship graph; step 6, calculating a shortest propagation path in the optimized causal diagram by using an improved BellmanFord algorithm, and identifying key nodes to obtain an optimal information propagation path; and 7, determining the current predicted position state of the target, and executing the target locking action of the unmanned aerial vehicle. According to the method, causal analysis and an improved BellmanFord algorithm are fused, and the target of the unmanned aerial vehicle is dynamically and accurately locked.
Need to check novelty before this filing date? Find Prior Art