SAR imaging of moving targets by subaperture based low-rank and sparse decomposition

Synthetic aperture radar (SAR) has gained significance as an indispensable instrument of remote sensing and airborne surveillance. Its applications extend to 3D terrain mapping, oil spill detection, crop yield estimation and disaster evaluation. SAR utilizes platform motion to synthesize a large antenna thus rendering a very fine spatial resolution. Nevertheless, imaging of moving targets with SAR is a challenging problem. In this thesis, we propose a moving target imaging approach for SAR which exploits the low-rank and sparse decomposition (LRSD) of the subaperture data. As a first step, multiple subapertures are constructed from the raw data using frequency domain filtering. In contrast to the stationary points, moving targets in the SAR scene shift their position in the various subapertures. This enables a successful low-rank and sparse decomposition of the subaperture data where the sparse component captures the moving targets’ phase histories and reflectivity profiles. On the other hand, the low-rank component consists of the static background due to fewer spatial variations in multiple subapertures. This framework allows the reconstruction of full-resolution sparse and low-rank images by combining the spectral information of the decomposed subapertures. Furthermore, it enhances the applicability of sparsity-driven moving target imaging frameworks to very low signal to clutter ratio (SCR) scenarios by offering a considerable SCR performance improvement. We manifest the effectiveness of our approach through experiments with synthetic as well as real SAR data. Our real SAR experiments were based on MiniSAR and EMISAR data

SAR imaging of moving targets by subaperture based low-rank and sparse decomposition

We propose a subaperture based method for synthetic aperture radar (SAR) imaging of moving targets. It exploits low-rank and sparse decomposition for extraction of moving targets from the complex SAR scene. First SAR raw data are divided into subapertures in the azimuth direction. Subsequently, low-rank and sparse decomposition is applied using the multiple subapertures data to accomplish the separation of moving targets from the stationary SAR background. A full resolution moving target image is reconstructed by combining the spectral information of the sparse subaperture images. Such an image has a high signal to clutter ratio and is well suited for motion estimation and focusing algorithms. This proposed framework extends the applicability of sparsity-driven moving target focusing methods to very low signal to clutter ratio environments. We demonstrate the performance of our approach through experiments with synthetic and real SAR data

A subaperture based approach for SAR moving target imaging by low-rank and sparse decomposition

In this paper, we propose a synthetic aperture radar (SAR) moving-target imaging approach that exploits the low-rank and sparse decomposition (LRSD) of subaperture data. The low-rank component consists of the static background whereas the sparse component captures the moving targets. This allows the reconstruction of a full resolution moving target image separate from the static background image after LRSD. Furthermore, it facilitates the applicability of sparsity-driven moving target imaging in low signal to clutter ratio (SCR) scenarios. We demonstrate the effectiveness of our approach with experiments on synthetic as well as real SAR data