A processing process of open-set specific emitter identification is built in order to achieve accurate control of urban frequency equipment. The core lies in the effective interval filtering of fingerprint features and the open set recognition model based on the deep self-encoder. By visualizing deep network activation using Class Activation Mapping(Grad-CAM), the section of signal contributing more to neural network activation can be determined, and then interval filtering for the signal can be performed without losing too much fingerprint information. On the other hand, an open-set specific emitter identification model is established based on semi-supervised adversarial autoencoders, achieving effective monitoring and identification of unknown emitters that may occur in the spectrum. Experiments show that Grad-CAM can filter out the most advantageous part of the extracted signal fingerprint, and the proposed model can achieve high-precision open set recognition without degrading the closed set recognition rate.