SOMP Algorithm: Subspace Reconstruction in Compressed Sensing

This content introduces SOMP (Subspace Orthogonal Matching Pursuit), a subspace reconstruction algorithm specifically designed for compressed sensing applications. The SOMP algorithm leverages the sparsity of measurement signals to reconstruct original signals through iterative subspace projection and residual updating mechanisms. Its core implementation typically involves: 1) Initializing residual signals, 2) Iteratively selecting atoms with maximum correlation to the residual, 3) Orthogonal projection onto the selected subspace, and 4) Updating residuals until convergence criteria are met. This approach enables efficient signal recovery in various domains including medical imaging and wireless communications. For researchers interested in SOMP, we provide executable source code (featuring key functions like atom selection and orthogonal projection) along with a detailed technical paper. These resources demonstrate practical implementation techniques and offer comprehensive insights into SOMP's theoretical foundations and application scenarios, supporting further development and integration into your projects.