Three-dimensional synthetic aperture radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range, and elevation coordinates. It is based on multipass SAR data obtained usually by nonuniformly spaced acquisition orbits. A common 3-D SAR focusing approach is Fourier-based SAR tomography, but this technique brings about image quality problems because of the low number of acquisitions and their not regular spacing. Moreover, attained resolution in elevation is limited by the overall acquisitions baseline extent. In this paper, a novel 3-D SAR data imaging based on Compressive Sampling theory is presented. It is shown that since the image to be focused has usually a sparse representation along the elevation direction (i.e., only few scatterers with different elevation are present in the same range-azimuth resolution cell), it suffices to have a small number of measurements to construct the 3-D image. Furthermore, the method allows super-resolution imaging, overcoming the limitation imposed by the overall baseline span. Tomographic imaging is performed by solving an optimization problem which enforces sparsity through ℓ₁-norm minimization. Numerical results on simulated and real data validate the method and have been compared with the truncated singular value decomposition technique.

In this paper, a generalized likelihood ratio test (GLRT) detector, based on support estimation (Sup-GLRT), for multiple scatterers detection in SAR Tomography, is proposed. It incorporates, in the statistical model, a sparsity assumption of the signal in the elevation direction, which is always verified in practical cases for scarcely vegetated areas. The test consists of sequential steps: first the presence of scatterers is detected; then, the determination of the number of scatterers and the estimation of their positons is performed sequentially, one by one, by means of a signal support detection-estimation operation. The test proposed follows an approach similar to SGLRT, where decisions are taken based on subspace energy measurements, but it is derived following a different testing order and is investigated both at the nominal system resolution and in the super-resolution cases, showing in the latter case, a detection gain with respect to SGLRT. Sup-GLRT performance is evaluated in terms of receiver operating characteristic (ROC) curves for different signal-to-noise ratio values. Experimental results obtained on real COSMO-SkyMed data are shown.

Along-track interferometric synthetic aperture radar (AT-InSAR) can be used to estimate the radial velocity of ground moving targets, starting from interferometric phase measures. The estimation obtained from a single-phase interferogram suffers from ambiguities. To solve these problems, multichannel AT-InSAR systems are required. In this letter, we analyze the radial velocity maximum-likelihood estimation accuracy with respect to AT-InSAR system parameters, such as velocity values and different clutter and thermal noise levels. We consider two different models for the target response: a deterministic model and a zero-mean Gaussian model. The presented results show that AT-InSAR systems exhibit better estimation accuracies for low-velocity values (slow targets).

Three dimensional (3-D) Synthetic Aperture Radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range and elevation co-ordinates. For 3-D image focusing multiple signals, acquired along different orbits, are required. The practical application of the focusing methods requires that non-uniformly spaced acquisition orbits have to be considered. In this paper we propose a technique exploiting the Compressive Sampling theory, and assuming that the image to be focused has a sparse representation along the elevation directions, which amounts to suppose that only few point-like scatterers with different elevation are present in the same range-azimuth resolution cell. Numerical results on simulated data show the good performance of the method.