Multi-UAV RF Signals CNN|Triplet-DNN Heterogeneous Network Feature Extraction and Type Recognition

ObjectiveThis study addresses the detection requirements of simultaneous Unmanned Aerial Vehicle (UAV) operations. The strategy is based on extracting model-specific information features from Radio Frequency (RF) time-frequency spectra. A CNN|Triplet-DNN heterogeneous network is developed to optimize feature extraction and classification. The method resolves the problem of identifying individual UAV models within coexisting RF signals and supports efficient multi-UAV management in complex environments.MethodsThe CNN|Triplet-DNN architecture uses a parallel-branch structure that integrates a Convolutional Neural Network (CNN) and a Triplet Convolutional Neural Network (Triplet-CNN). Branch 1 employs a lightweight CNN to extract global features from RF time-frequency diagrams while reducing computational cost. Branch 2 adds an enhanced center-loss function to strengthen feature discrimination and address ambiguous feature boundaries under complex conditions. Branch 3, based on a Triplet-CNN framework, applies Triplet Loss to capture local and global features of RF time-frequency diagrams. The complementary features from the three branches are fused and processed through a fully connected DNN with a Softmax activation function to generate probability distributions for UAV signal classification. This structure improves UAV type recognition performance.Results and DiscussionsRF signals from the open-source DroneRFa dataset were superimposed to simulate multi-UAV coexistence, and real-world drone signals were collected through controlled flights to build a comprehensive signal database. (1) Based on single-UAV RF time-frequency diagrams from the open-source dataset, ablation experiments (Fig. 7) were conducted on the three-branch CNN|Triplet-DNN structure to validate its design, and each model was trained. (2) The simulated multi-UAV coexistence dataset was used for identification tasks to evaluate recognition performance under coexistence conditions. Results (Fig. 10) show that recognition accuracy for four or fewer UAV types ranges from 83% to 100%, confirming the effectiveness of the CNN|Triplet-DNN model. (3) Each model was trained using the flight dataset and then applied to real multi-UAV coexistence identification. The CNN|Triplet-DNN achieved recognition accuracies of 86%, 57%, and 73% for two, three, and four UAV types, respectively (Fig. 13). Comparison with the CNN, Triplet-CNN, and Transformer models shows that the CNN|Triplet-DNN has stronger generalizability. All models exhibited performance degradation on real-world data relative to the open-source dataset, mainly because drones dynamically adjust communication frequency bands, which reduces recognition performance under coexistence scenarios.ConclusionsA CNN|Triplet-DNN heterogeneous network is proposed for identifying RF signals emitted by multiple UAVs. The three-branch structure and backpropagation algorithm improve the extraction of discriminative aircraft-model features, and the DNN enhances model generalization. Experiments using open-source datasets and real flight scenarios verify the method’s effectiveness and practical value. Future work will address dataset expansion, model optimization for dynamic frequency-band adaptation, and improved recognition under complex coexistence conditions.