Aerial-ground cooperative target localization based on trajectory matching

Dingcheng Tang; Wencong Ma; Yanglei Ji; Jinyong Wang; Huizheng Wang

doi:10.1117/12.3031443

19 July 2024 Aerial-ground cooperative target localization based on trajectory matching

Dingcheng Tang, Wencong Ma, Yanglei Ji, Jinyong Wang, Huizheng Wang

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131812A (2024) https://doi.org/10.1117/12.3031443
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Aerial-ground unmanned systems are the mainstream of research in recent years, target localization is a key step in rescue or strike missions, and the use of aerial-ground unmanned systems for target localization has great research value. However, aerial-ground systems have large differences in maneuverability and large sensor parallax. In this paper, an aerial-ground cooperative target localization method based on trajectory matching is proposed. First, the algorithm obtains the traveling trajectory of the unmanned vehicle in the UAV pixels. Then, the pixel trajectory and the UAV local coordinate trajectory are utilized to solve the transformation matrix from the pixel coordinate system to the geographic coordinate system. Finally, the geographic coordinates of other targets using the transformation matrix. The algorithm uses very little sensor data and a small number of platforms, and the experimental results show that the algorithm has an average localization accuracy of 1.55m at a UAV flight altitude of 100m, which is better than other target localization algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dingcheng Tang, Wencong Ma, Yanglei Ji, Jinyong Wang, and Huizheng Wang "Aerial-ground cooperative target localization based on trajectory matching", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131812A (19 July 2024); https://doi.org/10.1117/12.3031443

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KEYWORDS

Unmanned aerial vehicles

Detection and tracking algorithms

Unmanned vehicles

Cameras

Visualization

Matrices

Image processing

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