Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction

Abstract EN

Sparse matrix reconstruction has a wide application such as DOA estimation and STAP.

However, its performance is usually restricted by the grid mismatch problem.

In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion.

And it can overcome the grid mismatch problem.

Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently.

Compared with the Kronecker compressive sensing method, our proposed method has a higher computational efficiency and acceptable reconstruction accuracy.

Finally, simulation results validate the superiority of the proposed method.