Abstract
Multiple-input multiple-output (MIMO) array synthetic aperture radar (SAR) can straightly obtain the 3-D imagery of the illuminated scene with the single-pass flight. Generally, the Rayleigh resolution of the elevation direction is unacceptable due to the length limitation of linear array. The super-resolution imaging algorithms within the compressive sensing (CS) framework have been extensively studied because of the essential spatial sparsity in the elevation direction. However, the super-resolution performance of the existing sparse reconstruction algorithms will deteriorate dramatically in the case of lower signal-to-noise ratio (SNR) level or a few antenna elements. To overcome this problem, a new super-resolution imaging structure based on CS and deep neural network (DNN) for MIMO array SAR is proposed in this article. In this new algorithm, the spatial filtering based on CS is first proposed to reserve the signals only impinging from the prespecified space subregions. Thereafter, a group of parallel end-to-end DNN regression models are designed for mapping the potential sparse recovery mathematical model and further locating the true scatterers in the elevation direction. Finally, extensive simulations and airborne MIMO array SAR experiments are investigated to validate that the proposed method can realize the state-of-the-art super-resolution imaging against other existing related methods.
Highlights
INTRODUCTION3-D synthetic aperture radar (3-D SAR) imaging has been extensively studied in recent years because of its attractive and distinct advantages in many application fields, e.g., 3-D reconstruction and deformation monitoring of man-made structures [1], [2], forest biomass estimation [3], [4], and glacier ablation analysis [5]
Multiple-input multiple-output (MIMO) array synthetic aperture radar (SAR) can straightly obtain the 3-D imagery of the illuminated scene with the single-pass flight
In this article, considering that the sparse recovery in the elevation direction of the MIMO array SAR system is usually solved within the framework of compressive sensing (CS), it can be regarded as the convex and nonlinear issue when using the common 1-norm minimization in practical applications
Summary
3-D synthetic aperture radar (3-D SAR) imaging has been extensively studied in recent years because of its attractive and distinct advantages in many application fields, e.g., 3-D reconstruction and deformation monitoring of man-made structures [1], [2], forest biomass estimation [3], [4], and glacier ablation analysis [5]. In this article, considering that the sparse recovery in the elevation direction of the MIMO array SAR system is usually solved within the framework of CS, it can be regarded as the convex and nonlinear issue when using the common 1-norm minimization in practical applications This implies that the DNN is applicable to the super-resolution imaging scenario of the elevation direction depending on the universal approximation theorem. A new super-resolution imaging framework based on CS and DNN for MIMO array SAR is proposed This approach mainly consists of the following three parts; that is, preliminary recovery with CS, spatial filtering, and a group of parallel DNN regression models that achieve the goal of superresolution reconstruction.
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