Sparse reconstruction techniques in tomographic SAR inversion

Tomographic SAR inversion is essentially a spectral analysis problem. The resolution in the elevation direction depends on the spread of orbit tracks. Since the orbits of modern meter-resolution space-borne SAR systems, such as TerraSAR-X, are tightly controlled, the tomographic elevation resolution is at least an order of magnitude lower than in range and azimuth. Hence, super-resolution reconstruction algorithms are desired. Considering the sparsity of the signal in elevation, here the theory of compressive sensing comes into play. In this paper, recent developments on compressive sensing applied to tomographic SAR inversion are presented: A compressive sensing based algorithm “SL1MMER” was proposed; The ultimate bounds of the technique on localization accuracy and super-resolution power were investigated; the super-resolution capability of SL1MMER is demonstrated using TerraSAR-X real data examples.