High-precision positioning algorithm for UAV based on random forest weight compensation

被引：0
|
作者：
Fang K. [1 ,2 ]
Li X. [1 ]
Fan T. [1 ]
机构：
[1] State Key Laboratory of Integrated Business Network, Xidian University, Xi'an
[2] Henan Branch of National Computer Network and Information Security Management Center, Zhengzhou
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 01期
关键词：
Chan-Taylor; parameter extraction; random forest; unmanned aerial vehicle (UAV);
D O I：
10.12305/j.issn.1001-506X.2023.01.24
中图分类号：
学科分类号：
摘要：
In order to reduce the positioning error in the process of outdoor unmanned aerial vehicle (UAV) monitoring and realize real-time positioning of UAV, a Chan-Taylor three-dimensional positioning algorithm based on random forest is proposed. After the positioning data is expanded by the K-nearest neighbor pair, the random signal multipath noise is converted into Gaussian distribution according to the Chan-Taylor algorithm, which is convenient for the model to extract signal features. Cross validation is used to realize the adaptive determination of random forest feature parameters and confusion matrix threshold, and this threshold is used to measure the consistency of the model. Using the classification results to update the UAV positioning weight matrix, and effectively compensate the target height data. In addition, the calibration UAV is used to estimate the device error and correct the positioning result. Theoretical analysis and simulation results show that the algorithm can effectively improve the UAV positioning accuracy, and realize the passive location of UAV using the mobile communication base stations. © 2023 Chinese Institute of Electronics. All rights reserved.
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页码：202 / 209
页数：7
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