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

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.