Compressive sensing reconstruction techniques with magnitude prior information

This paper considers compressive sensing (CS) reconstruction with magnitude prior information. The magnitude prior information is described by mean and covariance of the unknown signal. Towards a reconstruction with minimum mean square errors (MMSE), we propose several CS reconstruction algorithms that use the magnitude prior information. Numerical simulations demonstrate that our approach reduces the reconstruction distortion. Potential applications of the proposed techniques include radio spectrum surveillance, sensor networks, etc.