Multi-linear sparse reconstruction for SAR imaging based on higher-order SVD

Yu-Fei Gao,Yue Yang,Yan-Bin Zou,Guan Gui,Qun Wan,Xun-Chao Cong

doi:10.1186/s13634-017-0479-7

Abstract

This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario, scatterers usually distribute in the block sparse pattern. Such a distribution feature has been scarcely utilized by the previous studies of SAR imaging. Our work takes advantage of this structure property of the target scene, constructing a multi-linear sparse reconstruction algorithm for SAR imaging. The multi-linear block sparsity is introduced into higher-order singular value decomposition (SVD) with a dictionary constructing procedure by this research. The simulation experiments for ideal point targets show the robustness of the proposed algorithm to the noise and sidelobe disturbance which always influence the imaging quality of the conventional methods. The computational resources requirement is further investigated in this paper. As a consequence of the algorithm complexity analysis, the present method possesses the superiority on resource consumption compared with the classic matching pursuit method. The imaging implementations for practical measured data also demonstrate the effectiveness of the algorithm developed in this paper.

Highlights

Synthetic aperture radar (SAR) is an important detection system in many fields for decades, such as remote sensing, environmental monitoring, and ground mapping [1,2,3,4]
The following is the organization of the rest in this paper: Section 2 illustrates the signal model for spotlight SAR; Section 3 introduces the tensor modeling based on higher-order singular value decomposition (HOSVD); Section 4 proposes the algorithm by introducing the multi-linear block sparse reconstruction and the preprocessing scheme for dictionary construction; Section 5 demonstrates several numerical simulations to verify the effectiveness of the proposed algorithm; Section 6 concludes this paper
6 Conclusions In a practical scenario, the block sparse distribution of targets is a common assumption for SAR imaging

Summary

Introduction

Synthetic aperture radar (SAR) is an important detection system in many fields for decades, such as remote sensing, environmental monitoring, and ground mapping [1,2,3,4]. The following is the organization of the rest in this paper: Section 2 illustrates the signal model for spotlight SAR; Section 3 introduces the tensor modeling based on HOSVD; Section 4 proposes the algorithm by introducing the multi-linear block sparse reconstruction and the preprocessing scheme for dictionary construction; Section 5 demonstrates several numerical simulations to verify the effectiveness of the proposed algorithm; Section 6 concludes this paper. It has been demonstrated that the recovery method based on Kronecker dictionary has much less strict requirements for coherence than the classic matching pursuit method with the same sparsity in regard to signal reconstruction, and it is deduced that the former has the higher successfully recovery bound [28, 45].

5: Dictionaries construction

Conclusions