Research on adaptive artificial intelligence algorithm in signal denoising and enhancement

Multimodal signals are susceptible to external environmental disturbances, such as weather conditions, electromagnetic interference, etc., which may affect the accuracy and stability of the data. This paper utilizes the characteristics of continuous and discrete wavelet transforms to study the wavelet threshold denoising algorithm in-depth, and by adjusting the parameters therein, it avoids the problem that the traditional threshold function is set to zero when the wavelet coefficients are smaller than the threshold value. Then, the deep learning algorithm in artificial intelligence is used to complete the signal adaptive denoising and realize the suppression of deceptive signal interference. The test results show that the adaptive denoising optimization model based on the deep learning algorithm has significantly improved signal characteristics compared to the original signal. Under a 15% noise environment, the average error of the model is less than 0.1, and the signal-to-noise gain of the signal is 5.0547 dB, which can reliably complete the interference suppression of both multiple interference and single interference and realize the adaptive anti-jamming optimization of the signal.