A Reconstruction Algorithm for Distributed Compressed Sensing: Joint Forward Orthogonal Pursuit

In distributed compressed sensing, the Joint Forward Orthogonal Pursuit algorithm serves as a reconstruction technique designed to enhance compressed sensing performance by exploiting correlations among multiple sensors. This algorithm integrates forward orthogonal pursuit into the distributed compressed sensing framework to reduce the number of measurements required for signal reconstruction. Specifically, the method employs Orthogonal Matching Pursuit (OMP) to determine coefficients for each individual sensor and performs joint optimization to update the global reconstructed signal. The algorithm implementation typically involves iterative selection of the most correlated atoms from a dictionary using OMP's greedy approach, followed by a distributed optimization step that combines local estimates through consensus mechanisms or weighted averaging. Additionally, by incorporating noise modeling techniques, the algorithm further improves signal reconstruction accuracy. This approach demonstrates promising potential for widespread application in various distributed compressed sensing scenarios, particularly in wireless sensor networks and multi-sensor imaging systems where computational efficiency and measurement reduction are critical.