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

Dong Zhang,Yongshun Zhang,Cunqian Feng

doi:10.1155/2017/6862852

Dong Zhang, Yongshun Zhang + Show 1 more

Open Access

https://doi.org/10.1155/2017/6862852

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Abstract

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.

Highlights

Compressive sensing is becoming more and more popular for its superiority in parameter super-resolution estimation using short observation [1,2,3]
Many problems in signal processing can be seemed as sparse matrix reconstruction problem, such as the DOA estimation [7] and STAP [8]
Considering the advantages of sparse matrix reconstruction, here, we research the estimation of DOA and DOD in MIMO radar based on sparse matrix reconstruction method

Summary

Introduction

Compressive sensing is becoming more and more popular for its superiority in parameter super-resolution estimation using short observation [1,2,3]. We consider the estimation of DOA and DOD in MIMO radar It can be solved by the traditional subspace method, such as MUSIC and ESPRIT algorithm. We revise the sparse matrix model by the one-order Taylor expansion and propose the joint sparse matrix model This model eliminates the grid mismatch effect by introducing some joint sparse items. In order to solve the joint sparse matrix reconstruction problem efficiently, we revise the 2D-SL0 algorithm and put forward the Joint-2D-SL0 algorithm. It can get a high estimation accuracy with satisfied speed. Both our method and the method in [14] are applied to the signal matrix without stacking the signal into 1D vector

Problem Formulation

Joint Sparse Matrix Reconstruction

Ξ I Δ2T ΦT φ

Simulation Results

Conclusion