The difficulty of an accurate and reliable detection of land mines requires a detailed analysis of ground penetrating radar data. This includes the power and phase of the received stepped frequency continuous wave signal, which both shows a unique pattern. In the B-Scan a typical hyperbola is present with an increased or decreased contrast of the reflected power or a constant phase along this hyperbola resulting in a circular pattern in the corresponding C-Scan. Thereby the phase along such a circular shape is constant, which is unique for the anti personnel mine simulants used in context of this paper. The measurements conducted also exhibit the importance of the measurement grid size with respect to the object size.

A major problem using a ground penetrating radar (GPR) system to detect objects just below the surface at a depth of 0.01m to 0.1m is the topology of the surface. This leads to a distortion of the system response, depending mainly on the characteristics of the vegetation. By knowing the vegetation and its height structure, false alarms when detecting objects can be minimised and detected buried objects can be verified. The existing surface condition can be analysed using multispectral imaging by creating vegetation indices. Changes in surface biomass can be correlated with changes in GPR-data for a more reliable mine detection, by reducing the false alarm rate.

Landmines, especially anti-personnel mines, are shallowly buried, below the range resolution of a Ground-Penetrating-Radar. Therefore it seems to be impossible to detect these objects. But in these case, interference effects occur and make it possible to detect metallic as well as non-metallic objects. Lab measurements with a VS50 dummy mine in two configurations demonstrate this behavior. They also show that the expected signature from the simulations is visible in the C-Scans. The shallowly buried non-metallic VS50 exhibits a negative contrast compared with the surrounding soil. While the metallic objects show an increased response. This is due to the reflection phase shift at the object. As a result, it is not sufficient to detect only maxima in the area of interest, likewise it is necessary to look for the presence of specific signatures.

The use of unmanned aerial vehicles as carriers for remote sensing became more and more popular over the past few years. As a result, UAV-based ground penetrating synthetic aperture radars were presented. These sensor systems can be used in many different applications, such as archaeology or landmine detection. Especially due to the increasing number of newly buried landmines it is important to perform this task efficiently. The goal of this paper is to present a new detection algorithm to detect shallowly buried objects with a UVA-based GPSAR-system. Therefore the phase image of a complex GPSAR-image is used to apply a Circle-Hough-Transformation. Due to the extent of the target signatures over several depths, the detections from multiple focusing depths could be combined and increase the receiver operating characteristic (ROC) of the system. Results show that with the Circle-Hough-Transformation the number of false alarms is less and the true detections, with the presence of circular signatures in the phase image, are at the same level as with an constant false alarm rate detector.

Airborne ground-penetrating synthetic aperture radar (GPSAR) using unmanned aerial vehicles (UAVs) offers advantages for many applications compared to established methods. In particular, these systems are useful for non-traversable terrain, where they could be used for the detection and localization of buried objects and structures. At distances of a few meters to the ground, refraction effects at the surface need to be considered in processing even for shallowly buried objects. Often the ground is assumed to be horizontal, which is not the case in many situations, such as uneven meadows or acres. This work investigates the influence of uneven ground on GPSAR images. It is shown that neglecting tilt in backprojection for shallowly buried targets leads to an apparent target shift. A model is derived to predict the shift for circular measurement trajectories and to estimate, if quality loss is to be expected. The model is verified using simulations, which are verified by measurement. It is concluded that tilt can be neglected in processing for shallowly buried targets. This is confirmed in a realistic measurement scenario.

The use of unmanned aerial vehicles as carriers for remote sensing became more and more popular over the past few years. As a result, UAV-based ground penetrating synthetic aperture radars were presented. These sensor systems can be used in many different applications, such as archaeology or glacier survey. The performance capability is highly affected by environmental conditions like soil moisture and the nature of the area of interest in general. The goal of this paper is to show the influence of various types of vegetation on the detection of shallowly buried metal objects. The results indicate a significant influence of vegetation water content on the performance of UAV-based GPSAR. The shape of the plants also affects the detection capability. It is still possible to detect buried metal targets trough dried vegetation.

Modelling and simulations of gravel with different pebble diameters for an FDTD simulation are implemented and verified with measurements in a gravel quarry. Therefore, a script is written which randomly arranges pebbles. The aim is to detect buried targets by down-looking UAV based GPR and characterize the impact of gravel. Different measured and simulated B-Scans, with a follow-up back-projection, are performed and discussed. As a result it can be stated that the simulations agree with measurements in a gravel quarry. Fine gravel exhibits similar properties as homogeneous soil and allowing the detection of buried targets by a down-looking GPR.