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Session C3: Spectrum: Protection and Optimization

A Jamming Detection Algorithm for GNSS Frequency-Hopping Signal Based on Multi-Node Collaboration and Multi-Segment Spectral Clustering
Chengjun Guo, Qing Zhao, University of Electronic Science and Technology of China (UESTC)

In order to enhance the anti-jamming capability of the next generation global navigation satellite system (GNSS)signals, researchers are considering incorporating frequency hopping(FH) modulation technology into GNSS signals design. On this basis, to further enhance the anti-jamming capability of GNSS FH signals, it is necessary to solve the problem of jamming blind detection for GNSS FH signals. Currently, the FH signal jamming detection algorithm is deficient in distinguishing between different signal components, especially in the strong FH signal environment, the jamming detection algorithm will misjudge the FH signal as an jamming signal, resulting in an increase in the probability of jamming misdetection. Therefore, this paper incorporates the idea of multiple segment spectral clustering into jamming detection algorithms. This algorithm uses the time-frequency differences between FH signals and jamming signals to perform cluster analysis on the spectral information of multiple time segment signals, thereby achieving jamming signals detection. Furthermore, in order to solve the problem that single node detection easily leads to low probability of jamming detection, this paper incorporates multi-node cooperation into jamming detection algorithms. The algorithm performs jamming detection through multiple nodes, sends the detection result of each node to the fusion center, and then obtains the global jamming detection result through the fusion rule of clustering. The experimental results show that the GNSS FH signal jamming detection algorithm based on multi-node collaboration and multi-segment spectral clustering proposed in this paper is able to increase the jamming detection probability while effectively reducing the jamming false detection probability. Meanwhile, compared with the single-node jamming detection algorithm, the multi-node cooperative jamming detection algorithm can effectively reduce the estimation error of the jamming parameters.



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