Abstract
High-quality three-dimensional (3-D) radar imaging is one of the challenging problems in radar imaging enhancement. The existing sparsity regularizations are limited to the heavy computational burden and time-consuming iteration operation. Compared with the conventional sparsity regularizations, the super-resolution (SR) imaging methods based on convolution neural network (CNN) can promote imaging time and achieve more accuracy. However, they are confined to 2-D space and model training under small dataset is not competently considered. To solve these problem, a fast and high-quality 3-D terahertz radar imaging method based on lightweight super-resolution CNN (SR-CNN) is proposed in this paper. First, an original 3-D radar echo model is presented and the expected SR model is derived by the given imaging geometry. Second, the SR imaging method based on lightweight SR-CNN is proposed to improve the image quality and speed up the imaging time. Furthermore, the resolution characteristics among spectrum estimation, sparsity regularization and SR-CNN are analyzed by the point spread function (PSF). Finally, electromagnetic computation simulations are carried out to validate the effectiveness of the proposed method in terms of image quality. The robustness against noise and the stability under small are demonstrate by ablation experiments.
Highlights
Three-dimension (3-D) radar imaging can prominently reflect the 3-D spatial structure of the target with respect to conventional 2-D radar imaging and serve as a significant application such as geological hazard monitoring and forewarning [1], ecological applications [2], and military reconnaissance [3]
Was proposed in this paper, which broke the limit of time consumption in the conventional sparsity-regularization method and outstood the SR imaging based on convolution neural network (CNN)
By the designed lightweight network ‘Fire’ module and effective supervised training, the complete training framework of superresolution CNN (SR-CNN) was provided in detail
Summary
Three-dimension (3-D) radar imaging can prominently reflect the 3-D spatial structure of the target with respect to conventional 2-D radar imaging and serve as a significant application such as geological hazard monitoring and forewarning [1], ecological applications [2], and military reconnaissance [3]. Typical 3-D radar imaging systems encompass the interferometric synthetic aperture radar (InSAR) [4], multiple-input multiple-output inverse SAR (MIMO ISAR) [5], and tomographic SAR [6]. The interferometry imaging handles phase differences from multiple SAR/ISAR images produced by multiple receivers of different views. This method is limited to distinguish scatterers located at the same Range-Doppler unit. Second-class imaging systems obtain the full 3-D radar echo data, which can form the synthetic aperture in azimuth and elevation dimension. Tomographic SAR is the representative of the second class [8], which develops azimuth aperture by flying a linear trajectory in a spotlight mode while the synthetic aperture in elevation dimension is formed by multiple closely spaced tracks. Different from tomographic SAR, 3-D imaging based on different configurations of antenna arrays
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