A Joint Reconstruction Algorithm for Multiple Sensor Data Based on Block A* Orthogonal Matching Pursuit

Considering the disadvantage of the high complexity and ignoring signals structural sparsity in A* Orthogonal Matching Pursuit (A*OMP) algorithm, a block A*OMP algorithm is proposed for block-sparse signals, and it is improved to solve the joint reconstruction problem for multiple signals in distributed compressed sensing. In the proposed algorithm, the single atom is replaced by a block that is composed of several atoms, and the sparsity is replaced by the maximum length of all the paths on the search tree when calculating the path cost. Then, on the basis of block A*OMP algorithm, a block A*OMP algorithm for Multiple Measurement Vector (MMV) problem is presented by projecting all blocks onto the residual matrix and selecting the block with the smallest projection error as a new node. With this algorithm, the temperature signals which are measured by sensors in the adjacent region are jointly reconstructed perfectly. Experiments show that the reconstruction performance of this algorithm outperform Orthogonal Matching Pursuit for MMV (OMPMMV) algorithm.