Compared with traditional Two-Dimensional(2D)Synthetic Aperture Radar(SAR)imaging,Three-Dimensional(3D)SAR imaging technology can overcome problems such as overlay and geometric distortion,thus having broad development space.As a typical 3D imaging system,the elevation resolution of array SAR is generally limited by the array aperture in theory,which is much lower than the range and azimuth resolution.To address this issue,an assumption of consistency in elevation between neighboring pixels is introduced and a re-weighted locally joint sparsity based Compressed Sensing(CS)approach is proposed for the array super-resolution imaging in the height dimension.Then,typical clustering methods such as K-means and Density-Based Spatial Clustering of Applications with Noise(DBSCAN)are used to achieve clustering analysis of specific targets(such as buildings and vehicles)in the observation scene.Finally,the experimental analysis using measured data is performed to confirm the effectiveness of the proposed algorithm.
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