基于CNN-FAN的辐射源个体开集识别OA

In order to solve the problem of identifying unknown emitters in an open electromagnetic environment,we propose an open-set identification method based on the convolutional neural network-Fourier analysis network(CNN-FAN)model.The proposed method initially introduces the network layer of Fourier analysis networks(FAN).The Fourier series properties are utilized to effectively extract the frequency components of the signal,followed by its integration with a convolutional neural network to formulate the CNN-FAN network model.Subsequently,the Softmax layer is substituted with an open-set identification model,facilitated by the utilization of OpenMax.Finally,a joint optimization strategy combining center loss and cross-entropy loss is adopted to optimize model performance.This optimization reduces the feature distance of individual radiation sources and narrows inter-class gaps,which improves the classification performance and enables OpenMax to distinguish between known and unknown emitter categories.The proposed method is validated and subjected to experimental analysis on the open-source WiSig dataset under varying degrees of openness.Experimental results demonstrate that the proposed method attains a recognition rate of 95%at an openness level of 0.057 and an open-set recognition rate of 84%at an openness level of 0.184,thereby outperforming other open-set recognition methods.In addition,the proposed framework provides a systematic solution for open-set radio frequency fingerprint identification by jointly considering discriminative feature learning and unknown-class rejection.The CNN module captures local temporal characteristics from the input radio frequency signals,while the FAN layer further enhances the representation capability by modeling periodic and frequency-domain variations.This complementary feature extraction mechanism enables the network to obtain more robust and separable emitter-specific representations.Moreover,by incorporating OpenMax into the decision stage,the model is no longer restricted to closed-set classification and can assign samples from unseen emitters to unknown categories according to their activation distribution.The combination of feature compactness optimization and open-set probability calibration improves both known-emitter identification and unknown-emitter rejection.These results indicate that the proposed CNN-FAN-based open-set identification method has strong applicability in realistic electromagnetic environments where unknown emitters may appear during deployment.