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<p>To address the critical challenge of insufficient classification accuracy for UAV flight control radio frequency (RF) signals in complex electromagnetic environments, this study proposes a novel classification framework combining Multiscale Dispersion Entropy (MDE) feature fusion with an Artificial Lemming Algorithm (ALA)-optimized BP neural network. The proposed method first employs MDE to extract robust multiscale dynamic features from RF signals, constructing a discriminative 12-dimensional feature matrix, then utilizes the biologically inspired ALA to globally optimize the BP network’s weights, biases, and hidden layer architecture through its unique migration-burrowing-foraging-predator avoidance mechanisms that dynamically balance exploration and exploitation. Extensive experiments on the DroneRFa dataset containing six mainstream UAV models demonstrate the framework’s superior performance, achieving 97.2% classification accuracy (a 4.7-7.1% improvement over conventional GA-BP and PSO-BP methods), remarkable noise robustness (maintaining 90% accuracy at SNR=0 dB), and accelerated convergence (reaching 90% accuracy in just 65 iterations), with further validation from ROC analysis showing an exceptional AUC of 0.97. These results collectively confirm that the MDE-ALA-BP approach effectively overcomes the limitations of existing methods in complex electromagnetic scenarios, providing a reliable and efficient technical solution for enhanced airspace security monitoring and low-altitude traffic management systems.</p>

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Method for classification of UAV flight control RF signals based on multi-scale divergence entropy and optimized neural networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月9日

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  • Bing Liu,
  • Jiaqi Liu,
  • Mingxin Shi,
  • Li Zhao

摘要

To address the critical challenge of insufficient classification accuracy for UAV flight control radio frequency (RF) signals in complex electromagnetic environments, this study proposes a novel classification framework combining Multiscale Dispersion Entropy (MDE) feature fusion with an Artificial Lemming Algorithm (ALA)-optimized BP neural network. The proposed method first employs MDE to extract robust multiscale dynamic features from RF signals, constructing a discriminative 12-dimensional feature matrix, then utilizes the biologically inspired ALA to globally optimize the BP network’s weights, biases, and hidden layer architecture through its unique migration-burrowing-foraging-predator avoidance mechanisms that dynamically balance exploration and exploitation. Extensive experiments on the DroneRFa dataset containing six mainstream UAV models demonstrate the framework’s superior performance, achieving 97.2% classification accuracy (a 4.7-7.1% improvement over conventional GA-BP and PSO-BP methods), remarkable noise robustness (maintaining 90% accuracy at SNR=0 dB), and accelerated convergence (reaching 90% accuracy in just 65 iterations), with further validation from ROC analysis showing an exceptional AUC of 0.97. These results collectively confirm that the MDE-ALA-BP approach effectively overcomes the limitations of existing methods in complex electromagnetic scenarios, providing a reliable and efficient technical solution for enhanced airspace security monitoring and low-altitude traffic management systems.