Abstract
In order to effectively reduce the risk of high-risk intrusion signals to wireless communication networks, an automatic anomaly identification of wireless communication signals based on nonlinear analysis is proposed. The fuzzy phase frequency feature of denoised abnormal signal is extracted and the wireless communication signal parameters are estimated by optimizing the fuzzy network abnormal signal feature extraction method of particle filter. The wireless communication signal is collected based on nonlinear analysis, the wireless communication signal network model is constructed, and the Back Propagation Neural Network—Dempster Shafer method is used to classify and fuse the results of feature extraction to realize the automatic identification of wireless communication signal anomalies. The experimental results show that the signal initial frequency estimation and signal slope estimation obtained by the research method have high accuracy, strong coverage performance, large total amount of automatic identification abnormal signals and high compatibility level.
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