Feature extraction algorithm of anti-jamming cyclic frequency of electronic communication signal

• Published in: Journal of intelligent systems Vol. 32; no. 1; pp. 9122 - 35
• Main Author: Yang, Xuemei
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 12-10-2023; Walter de Gruyter GmbH
• Subjects: Algorithms; anti-jamming cycle frequency; Binary phase shift keying; Classification; Communication; electronic communication signal; Feature extraction; Feature recognition; Interference; Jamming; Kernel functions; Particle swarm optimization; PSO; Sensitivity enhancement; Support vector machines; SVM
• DOI: 10.1515/jisys-2022-0295
• EISSN: 2191-026X
• ISSN: 2191-026X; 0334-1860

Anti-jamming cyclic frequency feature extraction is an important link in identifying communication interference signals, which is of great significance for eliminating electronic communication interference factors and improving the security of electronic communication environment. However, when the traditional feature extraction technology faces a large number of data samples, the processing capacity is low, and it cannot solve the multi-classification problems. For this type of problem, a method of electronic communication signal anti-jamming cyclic frequency feature extraction based on particle swarm optimization-support vector machines (PSO-SVM) algorithm is proposed. First, the SVM signal feature extraction model is proposed, and then the particle swarm optimization (PSO) algorithm is used. Optimize the kernel function parameter settings of SVM to raise the classifying quality of the SVM model. Finally, the function of the PSO-SVM signal feature extraction model is tested. The results verify that the PSO-SVM model begins to converge after 60 iterations, and the loss value remains at about 0.2, which is 0.2 lower than that of the SVM technique. The exactitude of signal feature extraction is 90.4%, and the recognition effect of binary phase shift keying signal is the best. The complete rate of signal feature extraction is 85%. This shows that the PSO-SVM model enhances the sensitivity of the anti-jamming cyclic frequency feature, improves the accuracy of the anti-jamming cyclic frequency feature recognition, reduces the running process, reduces the time cost, and greatly increases the performance of the SVM method. The good model performance also improves the application value of the method in the field of electronic communication.