Perceptual data processing and tracking method suitable for low-altitude economic unmanned aerial vehicle

Through multi-source sensor data fusion and trajectory management algorithms, the problem of insufficient sensor data fusion in the drone's perception and avoidance system is solved, high-precision target recognition and tracking is achieved, and the safety and efficiency of autonomous flight are improved.

CN120686802APending Publication Date: 2025-09-23PEIFENG ZHIXING (TIANJIN) TECH CO LTD
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Patent Information

Application Number
CN202510687418.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-27
Publication Date
2025-09-23

AI Technical Summary

Technical Problem

Existing drone perception and avoidance systems fail to effectively achieve deep fusion of multi-sensor data and target tracking, resulting in insufficient accuracy and stability of perception results, affecting the safety and efficiency of autonomous flight, especially in complex low-altitude environments where it is difficult to cope with emergencies.

Method used

A multi-source sensor data fusion framework is adopted, combined with extended Kalman filtering and trajectory initialization algorithm. Through ADS-B, ATAR, Camera and GBSS sensor data, a target trajectory database is established and managed, the optimal sensor data source is selected, and trajectory linking and extrapolation are performed to ensure the real-time and stability of the target state.

Benefits of technology

It significantly improves the accuracy and environmental adaptability of perception data, reduces missed detection and false detection rates, and enhances the UAV's rapid response capability in complex low-altitude environments and the reliability and safety of autonomous flight path planning.

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Abstract

The invention discloses a perception data processing and tracking method suitable for a low-altitude economic unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicles, and the method comprises the following steps: receiving measurement data from various sensors and local state data; establishing a track for each detection target of each sensor and linking tracks of the same target from different sensors; managing tracks in the track database and finding out an optimal sensor track; according to the method, data of ADS-B, ATAR, Camera and GBSS multi-source sensors are integrated, coordinate conversion and a track association algorithm are combined, accurate recognition and tracking of cooperative and non-cooperative intrusion targets are achieved, compared with a single sensor or a simple weighted fusion method, the method has the advantages that the method is simple, and the method is easy to implement. According to the method, the missing detection rate and the false detection rate are effectively reduced, and the accuracy and the environmental adaptability of sensing data in a complex low-altitude environment are remarkably improved.
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