The rapid development of civil UAV promotes the social and economic development, and the frequent “flying illegally” events has brought great challenges to aviation safety and government supervision. The frequency hopping communication system used in UAV data transmission and control link has the advantages of anti-jamming and anti-interception, and its complex electromagnetic environment, which also brings great difficulties to UAV detection. In this paper, the detection of civil UAV is realized by frequency hopping signal monitoring. Firstly, by analyzing the signal characteristics of UAVs, an adaptive noise threshold calculation method is proposed for find the signals from spectrum data. Then, the improved clustering analysis algorithm is proposed based on constructed the waveform shape characteristics and peak characteristics of UAV frequency hopping signal. Finally, according to the designed experimental process, the experimental environment is set up, and the UAV monitoring, discovery and parameter estimation are realized by using the improved clustering analysis algorithm, and compared with K-means, K-means ⁺⁺ , DBSCAN, Multi-hop and Auto-correlation methods. The results show that the method has certain robustness and has a good application prospect.

Hesitant fuzzy linguistic decision making is a focus point in linguistic decision making, in which the main method is based on preference ordering. This paper develops a new hesitant fuzzy linguistic TOPSIS method for group multi-criteria linguistic decision making; the method is inspired by the TOPSIS method and the preference degree between two hesitant fuzzy linguistic term sets (HFLTSs). To this end, we first use the preference degree to define a pseudo-distance between two HFLTSs and analyze its properties. Then we present the positive (optimistic) and negative (pessimistic) information of each criterion provided by each decision maker and aggregate these by using weights of decision makers to obtain the hesitant fuzzy linguistic positive and negative ideal solutions. On the basis of the proposed pseudo-distance, we finally obtain the positive (negative) ideal separation matrix and a new relative closeness degree to rank alternatives. We also design an algorithm based on the provided method to carry out hesitant fuzzy linguistic decision making. An illustrative example shows the elaboration of the proposed method and comparison with the symbolic aggregation-based method, the hesitant fuzzy linguistic TOPSIS method and the hesitant fuzzy linguistic VIKOR method; it seems that the proposed method is a useful and alternative decision-making method.

Specific Emitter Identification (SEI) is a vital technique in radio sensing for identifying forged communication nodes and illegal base stations. In this study, we propose a mixed CNN-Transformer model that achieves state-of-the-art performance on datasets collected from real-world environments. First, we utilize large kernel expansion convolution for direct modeling of long time-series IQ data to avoid destroying the feature distribution of tiny RF damage by traditional hand-designed algorithms. Second, we design the Transformer module inspired by the Visual Transformer (ViT) architecture, which combines class labeling and absolute position encoding to facilitate global feature extraction. In a Bluetooth dataset collected from 33 mobile phones, our model achieves a Top-1 recognition rate of over 98.6% with four sampling rates. In experiments with a dataset of 16 WiFi devices, our data enhancement scheme is able to achieve excellent recognition rates in a small number of training sets, demonstrating its superior performance in SEI tasks in natural environments. All code is public: https://github.com/DuBianJun-007/Specific-Emitter-Identification.