Wavelet Transform and Random Forest Based Identification of Interfering Signals from Electromagnetic Radiation

In this study, we propose an electromagnetic radiation interference signal identification method based on wavelet transform and random forest. By systematically preprocessing the electromagnetic radiation and acoustic emission signal data, including missing value processing, outlier detection and duplicate value exclusion, we ensure the quality of the data. Macro features, frequency-domain features and time-domain features are used in the feature extraction stage, where macro features describe the overall characteristics of the signals through statistics such as mean and variance, frequency-domain features analyse the spectral distribution of the signals through the Fourier transform, and time-domain features reveal the distribution pattern of the signals in time. Based on these features, we performed a multi-scale analysis of the signal using the wavelet transform and applied the random forest classification algorithm to build an identification model of the interference signal. By training and evaluating the model, we obtained a classification accuracy of up to 98%, indicating the high effectiveness of the method in interference signal recognition. The prediction results show that the interference signals in electromagnetic radiation and acoustic emission signals have obvious temporal regularity, concentrating on specific dates and time periods. The method provides a reliable tool and reference for interference signal detection and control in practical applications.