SAR tomographic imaging technique based on fusion of the Prony-inspired parametric and MVDR-inspired non-parametric DOA spatial spectral estimators

In this paper, the synthetic aperture radar tomography (SARTom) vertical distribution estimation problem is treated within the direction of arrival (DOA) estimation framework. Super-resolution parametric DOA estimation methods improve the vertical resolution and mitigate the effect of sidelobes. Nevertheless, these techniques have the main drawback related to the assumption that the scene is composed by a finite number of point-type backscattering sources. On the other hand, the minimum variance distortionless response (MVDR) inspired non-parametric DOA estimation methods are better suited to cope with scenarios characterized by the presence of distributed scatterers. In this work, we propose to decompose the SARTom vertical distribution estimation problem into two paradigms: parametric DOA estimation for point-type scatterers and non-parametric recovery of the spatial spectrum pattern (SSP) of the spatially distributed scattering components. The principal innovative contribution of this study relates to the proposition for fusion of the Prony-inspired parametric and MVDRinspired non-parametric DOA estimation paradigms through the use of the spectral positional invariance property of the point-type targets, which holds with the extended Prony model.