基于随机森林权重补偿的无人机高精度定位算法

doi:10.12305/j.issn.1001-506X.2023.01.24

Abstract

Abstract:

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.

Key words: random forest, Chan-Taylor, unmanned aerial vehicle (UAV), parameter extraction

CLC Number:

TN92

Kun FANG, Xiaohui LI, Tao FAN. High-precision positioning algorithm for UAV based on random forest weight compensation[J]. Systems Engineering and Electronics, 2023, 45(1): 202-209.

Figures/Tables 12

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References 30

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分类	正确判断	错误判断
正确分类	TP	FN
错误分类	FP	TN

参数	值
基站距离/m	1 000
信道类型	视距/非视距
检测空间直径/m	300
载频/GHz	2

评价标准	X-轴	Y-轴	Z-轴
召回率	74.54	89.62	86.06
精确率	90.73	83.50	87.45
检测值	81.85	86.45	86.75
E_value	94.85	95.02	89.28
F_value		93.05

算法	定位误差均值/m	定位所用时间/s
文献[5]	5.13	0.006 364 89
文献[9]	8.96	0.005 394 62
文献[10]	7.64	0.006 594 82
本文算法	4.58	0.006 782 88
文献[11]	4.68	0.008 568 16
文献[12]	4.96	0.009 765 92

定位点	实际坐标	估计坐标
1	(50, 50, 200)	(49, 50, 196)
2	(100, 100, 200)	(98, 97, 195)
3	(150, 150, 200)	(145, 146, 195)
4	(200, 200, 200)	(198, 199, 196)
5	(250, 250, 200)	(245, 248, 195)
6	(50, 80, 50)	(49, 79, 47)
7	(50, 80, 100)	(49, 79, 97)
8	(50, 80, 150)	(47, 79, 148)
9	(50, 80, 200)	(48, 79, 196)
10	(50, 80, 250)	(48, 78, 245)

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

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