Sparsity based detection of multiple targets in 3D-SAR imaging

In this paper, a Constant False Alarm Rate (CFAR) detection approach of multiple scatterers in SAR tomography is presented. The detector exploits the sparsity assumption and is based on support detection, i.e. on the detection of the position of the non-zero elements in the unknown sparse vector, and on a Generalized likelihood Ratio Test (GLRT). It allows a reduction in the number of measurements required for obtaining a reliable solution and an increased resolution. The test is formulated for any number of scatterers K≤K_max, with K_max known. The method performance is evaluated in terms of probability of false alarm and probability of detection, for different values of SNR (signal to noise power ratio) and different number of measurements, in the cases of nominal and super-resolution reconstructions.