A Large Along-Track Baseline Approach for Ground Moving Target Indication Using TanDEM-X

In the paper a new method for ground moving target indication (GMTI) using two satellites (i.e. the TerraSAR-X and the TanDEM-X satellite) together is presented. The along-track baseline between the satellites is chosen to be in the order of several kilometres, so that each satellite observes the same moving vehicles at different times in the order of one to several seconds. The proposed method allows the estimation of the ground velocity of the moving targets as well as the estimation of the broadside positions without the need of complex bistatic processing techniques

Digital Beamforming and Traffic Monitoring Using the new FSAR System of DLR

In November 2006 the first X-band test flight of DLR’s new FSAR system has been performed successfully and in February 2007 the first flight campaign has been conducted for acquiring experimental multi-channel data of controlled ground moving targets. In the paper the performed experiments and the used setup of the FSAR X-band section are described and preliminary results in the field of ground moving target indication and digital beamforming are presented

A sparsity-driven approach for joint SAR imaging and phase error correction

Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements it provides over existing techniques for model error compensation in SAR

A sparsity-driven approach for joint SAR imaging and phase error correction

Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements it provides over existing techniques for model error compensation in SAR