UAV signal recognition of heterogeneous integrated KNN based on genetic algorithm

Ying Xue, Yuanpei Chang, Yu Zhang, Jingguo Sun, Zhangyuan Ji, Hewei Li, Yue Peng () and Jiancun Zuo ()
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Ying Xue: Shanghai Polytechnic University
Yuanpei Chang: Shanghai Polytechnic University
Yu Zhang: Shanghai Polytechnic University
Jingguo Sun: Shanghai Polytechnic University
Zhangyuan Ji: Shanghai Polytechnic University
Hewei Li: Shanghai Polytechnic University
Yue Peng: Shanghai Polytechnic University
Jiancun Zuo: Shanghai Polytechnic University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2024, vol. 85, issue 4, No 4, 599 pages

Abstract: Abstract To address the detection difficulty problem of unmanned aerial vehicles (UAVs) in complex electromagnetic environments, this paper proposes a genetic algorithm-based heterogeneous integrated k-nearest neighbor (KNN) model for UAV signal recognition. First, the original data is pre-processed by discrete Fourier transform (DFT). Next, the genetic algorithm is deployed to find feature points for each base classifier to be integrated into the high-density power spectrum. Following this, each base classifier to be integrated is set into a strong classifier, and finally, the data to be detected is transferred to the trained integrated classifier to get the UAV signal detection results. The experimental results show that the genetic algorithm bagging KNN (GA-Bagging-KNN) algorithm achieves 98% accuracy in detecting binary classification and 79% accuracy in quadruple classification.

Keywords: UAV signal recognition; Bagging integration; Genetic algorithm; K-nearest neighbor algorithm (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11235-023-01099-x

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