Design and Implementation of a Multirotor Unmanned Aircraft System for Target Tracking

Athilingam, R.; Kumar, K. Senthil; Rasheed, Anjum

doi:10.1007/s13369-016-2256-6

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…13 However, the size of the target in the image is easy to change when the UAV is tracking the target. In order to overcome the challenge of the target size and background light changing in the process of UAV target tracking, the Camshift algorithm was proposed to track the target moving on the ground, 14 , 15 but the position coordinates of the tracked target are easily affected when the target was occluded by the obstacles. The Kalman filter algorithm was proposed to predict the center position coordinates of the occluded target when the target was occluded by the obstacles.…”

Section: Introductionmentioning

confidence: 99%

BP neural Network-Kalman filter fusion method for unmanned aerial vehicle target tracking

Liu

Nie

Dong

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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The poor real-time performance and target occlusion occurred easily when the UAV was tracking the target. In this paper, a target tracking method based on the Back Propagation neural network fusion Kalman filter algorithm was developed to solve the position prediction problem of the UAV target tracking in real time. Firstly, the target tracking algorithm was used to acquire the center position coordinates of the target on the onboard computer, and then the coordinate difference matrix was constructed to train the BP neural network in real time. Secondly, when the target was occluded by the obstacles judged by the Bhattacharyya coefficient, the BP neural network fusion Kalman filter algorithm was used to accurately predict the center position coordinates of the occluded target. Then the flight speed of UAV was calculated by the deviation between the coordinates of the target and the image center. Finally, the velocity command was sent to the UAV by the onboard computer. The experimental results shown that the target position predicted by BP neural network fusion Kalman filter algorithm was more accurate and robust in predicting the center position coordinates of the target, and the UAV can track the moving target on the ground stably.

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Section: Introductionmentioning

confidence: 99%