Synthetic-aperture radar imaging through dispersive media

In this paper we develop a method for synthetic-aperture radar (SAR) imaging through a dispersive medium. We consider the case when the sensor and scatterers are embedded in a known homogeneous dispersive material, the scene to be imaged lies on a known surface and the radar antenna flight path is an arbitrary but known smooth curve. The scattering is modeled using a linearized (Born) scalar model. We assume that the measurements are polluted with additive noise. Furthermore, we assume that we have prior knowledge about the power-spectral densities of the scene and the noise. This leads us to formulate the problem in a statistical framework. We develop a filtered-back-projection imaging algorithm in which we choose the filter according to the statistical properties of the scene and noise. We present numerical simulations for a case where the scene consists of point-like scatterers located on the ground, and demonstrate how the ability to resolve the targets depends on a quantity which we call the noise-to-target ratio. In our simulations, the dispersive material is modeled with the Fung–Ulaby equations for leafy vegetation. However, the method is also applicable to other dielectric materials where the dispersion is considered relevant in the frequency range of the transmitted signals.