A Cognitive Signals Reconstruction Algorithm Based on Compressed Sensing

Compressed Sensing (CS) theory has been widely used in radar signal processing field, and the reconstruction algorithm is the key to whether the original signal can be reconstructed from limited observations. However, the existing reconstruction algorithms either don't consider and remove the noise in signal reconstruction, or need the iterative estimation of noise variance during the signal reconstruction processing, which will lead the poor anti-noise performance or large computation load. In this paper, a cognitive signals reconstruction algorithm based on compressed sensing is proposed. In the method, the noise variance can be estimated by subspace decomposition method, and then the estimated noise variance is used as priori information in reconstruction algorithms to improve the reconstruction accuracy or reduce the computation load. As a result, the reconstruction algorithm performance can be improved effectively. Some simulation results illustrate the effectiveness of the proposed method.