Multi-target tracking latency optimization system and method for drone swarm

What is AI technical title?
AI technical title is built by Patsnap AI team. It summarizes the technical point description of the patent document.
By optimizing the resource scheduling of UAV swarms through the ByteTrack algorithm and low-latency communication, the problems of latency and incomplete data in multi-target tracking of UAV swarms are solved, thereby improving tracking efficiency and accuracy.

CN122111044APending Publication Date: 2026-05-29南京海汇装备科技有限公司

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Applications(China)
Current Assignee / Owner: 南京海汇装备科技有限公司
Filing Date: 2026-03-03
Publication Date: 2026-05-29

AI Technical Summary

Technical Problem

Drone swarms suffer from latency issues during multi-target tracking, especially in complex scenarios where tracking data is incomplete and resource allocation is insufficient, leading to low tracking efficiency.

Method used

The ByteTrack target tracking algorithm is used to determine tracking priority. Combined with resource scheduling and rectification areas, the tracking mechanism of the drone swarm is predicted. The scheduling between drones is optimized through a low-latency communication protocol to reduce communication latency and trajectory interaction.

Benefits of technology

It improves the real-time performance and accuracy of drone swarms in collaborative tracking of multiple targets, avoids incomplete tracking data collection, and optimizes tracking latency.

✦ Generated by Eureka AI based on patent content.

Smart Images

Figure CN122111044A_ABST

Patent Text Reader

Abstract

The application discloses a multi-target tracking delay optimization system and method for a UAV swarm, relates to the technical field of multi-target tracking delay optimization, and comprises the following steps: S10, determining a resource scheduling rectification area of the UAV swarm in real time; S20, predicting tracking mechanisms of each target locking and tracking target of each to-be-scheduled UAV; S30, predicting delay time generated in each tracking mechanism and delay time generated between each associated tracking mechanism group; S40, adding tracking limiting conditions for each to-be-scheduled UAV and tracking mechanisms of associated UAVs of each to-be-scheduled UAV, and scheduling each to-be-scheduled UAV according to the tracking mechanisms and the tracking limiting conditions of the tracking mechanisms. The application reduces delay caused by communication and calculation by introducing a predictive tracking mechanism, effectively reduces overall delay in the multi-target tracking process of the UAV swarm, and improves the cooperative tracking precision and real-time performance of the swarm on multiple targets.

Need to check novelty before this filing date? Find Prior Art