Holographic SAR Tomography 3-D Reconstruction Based on Iterative Adaptive Approach and Generalized Likelihood Ratio Test

Holographic synthetic aperture radar (HoloSAR) tomography is an attractive imaging mode that can retrieve the 3-D scattering information of the observed scene over 360° azimuth angle variation. To improve the resolution and reduce the sidelobes in elevation, the HoloSAR imaging mode requires many passes in elevation, thus decreasing its feasibility. In this article, an imaging method based on iterative adaptive approach (IAA) and generalized likelihood ratio test (GLRT) is proposed for the HoloSAR with limited elevation passes to achieve super-resolution reconstruction in elevation. For the elevation reconstruction in each range-azimuth cell, the proposed method first adopts the nonparametric IAA to retrieve the elevation profile with improved resolution and suppressed sidelobes. Then, to obtain sparse elevation estimates, the GLRT is used as a model order selection tool to automatically recognize the most likely number of scatterers and obtain the reflectivities of the detected scatterers inside one range-azimuth cell. The proposed method is a super-resolving method. It does not require averaging in range and azimuth, thus it can maintain the range-azimuth resolution. In addition, the proposed method is a user parameter-free method, so it does not need the fine-tuning of any hyperparameters. The super-resolution power and the estimation accuracy of the proposed method are evaluated using the simulated data, and the validity and feasibility of the proposed method are verified by the HoloSAR real data processing results.