APPLICATION OF SNNS MODEL BASED ON MULTI-DIMENSIONAL ATTENTION IN DRONE RADIO FREQUENCY SIGNAL CLASSIFICATION

Spiking Neural Networks (SNNs) are attracting attention due to their energy efficiency and importance in neuromorphic computing. Therefore, we propose an SNN-based method for classifying drone RF signals in complex electromagnetic environments. Specifically, we designed a new SNNs model called Spiking-EfficientNet based on EfficientNetV2 and improved its performance with a multidimensional attention mechanism. Experimental results demonstrate that Spiking-EfficientNet achieved classification accuracy of 99.13% and 96.02% on the ZK RF and DroneDetectV2 datasets. Importantly, Spiking-EfficientNet not only outperforms traditional Artificial Neural Networks (ANNs) in performance, but also exhibits significantly lower energy consumption. The energy consumption is only 20.1% of EfficientNetV2, 2.56% of VGG11, 10.71% of ResNet18, and 61.15% of MobileNetV2. This study demonstrates the significant potential of SNNs in drone RF signal classification and provides a low-power solution.