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Telecommunication Systems

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14-02-2024

UAV signal recognition of heterogeneous integrated KNN based on genetic algorithm

Authors: Ying Xue, Yuanpei Chang, Yu Zhang, Jingguo Sun, Zhangyuan Ji, Hewei Li, Yue Peng, Jiancun Zuo

Published in: Telecommunication Systems | Issue 4/2024

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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.

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Title: UAV signal recognition of heterogeneous integrated KNN based on genetic algorithm
Authors: Ying Xue
Yuanpei Chang
Yu Zhang
Jingguo Sun
Zhangyuan Ji
Hewei Li
Yue Peng
Jiancun Zuo
Publication date: 14-02-2024
Publisher: Springer US
Published in: Telecommunication Systems / Issue 4/2024
Print ISSN: 1018-4864
Electronic ISSN: 1572-9451
DOI: https://doi.org/10.1007/s11235-023-01099-x

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