High-frequency signal enhancement algorithm under strong electromagnetic interference environment

Le résumé fourni par la source

High-frequency signal enhancement uses the difference in statistical characteristics between signal and noise to achieve signal enhancement. In practical applications, it is often difficult to obtain an accurate reference signal, resulting in poor enhancement effect. Therefore, a high-frequency signal enhancement algorithm under strong electromagnetic interference environment is designed. The empirical mode decomposition and wavelet transform techniques are used to decompose the signal. The empirical mode decomposition mines the frequency domain change pattern and extracts the intrinsic mode function. The multi-scale convolutional neural network is used to capture the time-frequency domain characteristics of the high-frequency signal, and the signal is converted into a time-frequency graph. The signal morphology time-frequency variables are extracted and the characteristic parameters of the high-frequency signal are identified by combining Euclidean distance matching. According to the characteristic parameters, a personalized enhancement strategy is designed to reconstruct the details and approximate coefficients of the enhanced signal, retain the original features and show good enhancement effect. The experimental results show that in a strong electromagnetic interference environment, the amplitude and stability are better, the lowest distortion is always maintained, and the noise suppression and signal fidelity can be dynamically balanced, which provides a useful reference for the development of signal processing technology in related fields.

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