Data fusion of space electronic detection signal based on symmetrical wavelet reconstruction algorithm

In the data fusion process of space electronic detection signal, a large amount of signal energy will be lost, resulting in a large amount of data redundancy, which will affect the accuracy of subsequent analysis. Therefore, a data fusion method of space electronic detection signal based on symmetric wavelet reconstruction algorithm is proposed. The modular maximum denoising method is used to denoise the space electronic detection signal, and the Lipschitz index is used as the conversion medium between signal and noise to carry out blind separation and coherent registration of the denoised signal. After the signal is processed, the time-delay hybrid model of the receiving array antenna is established. The energy composition matrix decomposed by symmetric wavelet reconstruction is used to normalize the feature vector. The signal features are extracted by symmetric wavelet reconstruction algorithm, and the signal features are clustered by K-means algorithm. According to the results of feature clustering, the corresponding space electronic detection signals are clustered to complete the data fusion of space electronic detection signals. The experimental results showed that the method designed by the research institute processed the signal, resulting in an average signal redundancy of 22.3 MB, a fusion signal analysis error of about 20%, and data signal noise consistently below 20 dB. This indicates that the signals fused by research methods have lower noise, energy loss, and data redundancy, fewer analysis errors, and better overall performance.