Low Frequency Weak Signal Detection System Based on Embedded

Abstract

It has important application value to detect weak signal in embedded system. In the process of low-frequency weak signal detection of embedded system, the traditional algorithm is limited, which results in low detection accuracy of embedded system. Therefore, a low-frequency weak signal detection method based on wavelet neural network algorithm is proposed. The wavelet neural network signal detection model is established, the wavelet transform coefficients of useful signals covered by background noise are obtained by neural network optimization processing, and the matrix is established according to the wavelet transform coefficients, so as to obtain the initial data information of any signal. The experimental results show that using the improved algorithm to detect the low-frequency weak signal in the embedded system can greatly improve the accuracy of low-frequency weak signal detection and achieve satisfactory results.