Signal Reconstruction Algorithms
Fast and efficient signal reconstruction algorithms with reliable performance form the core component of compressive sensing theory, an area where numerous impactful research initiatives are currently underway. Since the introduction of compressive sensing theory, various sparse signal reconstruction algorithms have emerged, primarily categorized into three types: greedy algorithms, convex relaxation algorithms, and combinatorial algorithms. The focus here is on the Subspace Pursuit (SP) algorithm, which operates by iteratively selecting the most correlated atoms from the measurement matrix and refining the support set through orthogonal projection operations.