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Radar recognition system based on XG-Boost

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DOI: 10.23977/jeis.2024.090116 | Downloads: 3 | Views: 100

Author(s)

Hongxiang Luo 1, Jianxin Guo 1, Pengxi Fu 1

Affiliation(s)

1 School of Electronic Information, Xijing University, Xi'an, 710109, China

Corresponding Author

Jianxin Guo

ABSTRACT

With the development of science and technology, radar recognition has gradually entered our lives. With the introduction of deep learning technology in the field of radar recognition, with its strong automatic feature learning ability and end-to-end processing advantages, its recognition accuracy has been further improved. Based on this, a radar recognition system based on XG-Boost is proposed in this paper. The system was used to identify five different materials: air, books, hands, knives and plastic boxes. After a series of experiments, it is found that the recognition accuracy of XG-Boost algorithm is as high as 97.8%, which is higher than the 96.4% of SVC algorithm and 92.8% of GaussianNB algorithm. And the XG-Boost algorithm has achieved 100% recognition rate for air, books, hands and plastic boxes. There was only an error in the identification of the knife. 

KEYWORDS

Radar recognition, deep learning, XG-Boost algorithm

CITE THIS PAPER

Hongxiang Luo, Jianxin Guo, Pengxi Fu, Radar recognition system based on XG-Boost. Journal of Electronics and Information Science (2024) Vol. 9: 117-123. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2024.090116.

REFERENCES

[1] Dandan Gu, Yi Liao, Xiaobing Wang. Progress and Thinking of Intelligent Recognition Technology Guided by Radar Target Characteristics Knowledge[J]. Guidance & Fuze, 2022, 43 (04): 57-64.
[2] Marlin. Survey of Radar Target Recognition Technology[J]. Modern Radar, 2011, 33(06): 7.
[3] Pengcheng Guo, Jingjing Wang, Longshun Yang. Current status and prospect of radar ground target recognition technology [J].Aero Ordnance, 2022, 29(02):1-12.
[4] Asif N , Tariq A , Ghulam M , et al.A novel technique for detecting electricity theft in secure smart grids using CNN and XG-boost[J].Intelligent Systems with Applications, 2023, 17.
[5] Lina YANG, Kaixue YAO, Yong HE et al. Research on road condition sensing method based on SmoteEnn_XGBoost model [J]. Intelligent Computer and Application, 2021, 11(11):137-142+147.
[6] Miaomiao Li. Drug relocation prediction based on XG-B00ST and multiple data sources [J]. Software Guide, 2020, 19(02):110-113.
[7] Kangmo Jung. Support Vector Machines for Unbalanced Multicategory Classification [J].Mathematical Problems In Engineering, 2015, 1(1). 
[8] Yingran S , Chandra G , Yang Y , et al.Recurrence prediction of lung adenocarcinoma using an immune gene expression and clinical data trained and validated support vector machine classifier.[J].Translational lung cancer research, 2023, 12(10):2055-2067. 
[9] Yitou Li. Improved classification algorithm based on support vector machine[J].Computer System Applications, 2019, 28(10):145-151. DOI:10.15888/j.cnki.csa.007080 
[10] Danling Chen. Research on fault diagnosis method of rotating machinery based on wavelet packet and support vector machine [D]. Jiangxi University of Science and Technology, 2010.
[11] Xin LIU, Haochen WANG, Yuhu HUANG. Recognition of telecom fraud information based on plain Bayesian classification [J]. Computer Age, 2023, (04):29-32+38. DOI: 10.16644/j.cnki.cn33-1094/tp.2023.04.006

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