Compressive sensing of complex-valued data using Gaussian entropy

Abstract

In this paper, we propose an effective compressive sensing algorithm based on Gaussian entropy for complex-data. Compared with the traditional mean squared error (MSE) method, we consider the full second order statistics information of Gaussian noise in the new algorithm, including relevant information and conjugate information, which makes the recovered signal closer to the original input signal. Simulation results of the synthesized 1D signal and 2D signal show that the proposed algorithm has better performance than the MSE method.