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A high-resolution imaging of small objects for subsurface radar data processing

In subsurface radar applications, it is often experienced that the requirements for the resolution and the penetration depth contradict each other. For example, pre-excavation survey of archaeological sites may require one to detect buried objects of the order of 10 cm at a depth of 1 m. In such a case, the compromise will be the use of around 500 MHz, for which the target is on the order of a radar wavelength. We refer to the target in such a situation as a 'small object'. It is thus hard to identify the shape of the object with conventional signal processing such as the synthetic aperture method. Our objective is to develop a robust high-resolution imaging algorithm applicable to targets whose dimension is close to the radar wavelength. In order to avoid instabilities in super-resolution techniques, we limit the freedom in the estimation by properly modeling the target as well as the parameters of the medium.