Wideband Signal Detection Algorithm Based on Compressed Sensing

According to the traditional blind detection algorithm of broadband signal with poor detection effect under low signal-to-noise ratio, a blind detection algorithm of eigenvalue distribution map is proposed under modulated wideband converter (MWC) sampling. The algorithm decomposes the eigenvalue of the covariance matrix of the compressed sampling sequence, establishes the determination basis for broadband signal detection according to the different distribution laws of eigenvalues, and accomplishes efficient and accurate signal detection from the eigenvalue distribution map by using the powerful image feature extraction capability of deep learning. In order to further improve the detection performance of the algorithm at low signal-to-noise ratio, the correlation between elements in the presence of signals is enhanced by calculating the quadratic covariance matrix, and then obtaining the eigenvalue distribution map to complete the signal detection. Compared with conventional method, the results show that the method has better detection probability and is robust to different channels and different number of sampling channels.