Research on iterative thresholding compressive sampling matching pursuit algorithm based on sparsity adaptive
Focusing on the characteristics of original signal with unknown sparsity in practical application, with deeply research on the iterative thresholding and greedy algorithms, we propose a novel iterative thresholding compressive sampling matching pursuit algorithm based on sparsity adaptive. It not only improves the reconstruction accuracy and efficiency with good noise robustness, but also has strong stability when measurement value is small, which realizes the sparsity adaptive in reconstruction process. Based on MATLAB simulation experiments, and compared with common matching pursuit algorithms, it has obvious advantages in stability, precision and noise robustness, which can promote compressive sensing to be applied in practical system.
Author's Name: Sun, G., Zhou, Y., Zuo, J., Wang, Z.