A UAV Target Tracking and Control Algorithm Based on SiamRPN

doi:10.16182/j.issn1004731x.joss.22-0142

Abstract

Abstract:

Aiming at the requirement of autonomously tracking land moving targets of rotary-wing UAVs, an autonomous and stable UAV tracking and control system that can adapt to the common interference environments such as scale changes, occlusions, and attitude changes is constructed. The system extracts the imaging position of the target in airborne camera through the twin network based on deep learning, and obtains the relative pose of the target. The image processing algorithm is designed to process the icons in the tracking frame, and the yaw angle of UAV relative to the tracking target is obtained, Kalman filter is introduced to process the above pose information, and finally the tracking control nearly coinciding with the trajectory of the moving target is realized. The algorithm is based on Gazebo simulation platform and can track the moving target stably for a long time under interference condition.

Key words: siamese network, Kalman filtering, target tracking, trajectory tracking, image processing

CLC Number:

V279

Songming Jiao, Hui Ding, Yufei Zhong, Xin Yao, Jiahao Zheng. A UAV Target Tracking and Control Algorithm Based on SiamRPN[J]. Journal of System Simulation, 2023, 35(6): 1372-1380.

Figures/Tables 9

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