Radars mounted on unmanned aerial vehicles (UAVs) enable a high flexibility on data acquisition since arbitrary and especially non-linear flight trajectories can be achieved. Compared to linear flight trajectories, non-linear trajectories achieve better image quality and allow the scene and targets to be observed from different angles, thereby capturing angle-dependent features. However, inversion of synthetic aperture radar (SAR) data for non-linear apertures and sample acquisition is computationally taxing and artifacts may arise in the image due to the aperture’s structure. In order to suppress aperture artifacts and clutter in the SAR image, a backprojection imaging algorithm combined with compressed sensing methods is proposed. In this work, an efficient way to calculate the backprojection in a compressed sensing framework is described that does not require interpolation steps. Since SAR images are neither strictly sparse nor noise dominated, an adapted compressed sensing algorithm is proposed that accounts for clutter and extended targets which are spread across multiple image pixels. In addition, an image segmentation approach is presented that enables faster processing while being robust to extended targets located at segment edges. With the proposed approach, SAR images of arbitrary flight paths can be processed efficiently with fewer acquisition points while achieving better clutter suppression at the same image quality compared to equally dense measurement sets.

Multicopter unmanned aerial vehicle (UAV)-based radar systems enable high-resolution subsurface imaging using methods such as backprojection (BP). These methods place high demands on localization accuracy and radar range calibration. This work presents a method for the calibration of the range offset that relies only on the presence of reference targets on the ground surface. The range offset is estimated using radar measurements, localization data, and a digital elevation model (DEM). No additional positional information about the reference targets is required. The proposed method is first investigated in theory and simulation, deriving requirements for the measurement geometry. Using measurements, a calibration is then performed and the results validated.

Synthetic aperture radar (SAR) interferometry is an essential remote sensing technique with a wide range of applications. Studying and monitoring dynamic processes on the Earth’s surface requires digital elevation models (DEMs) at short time intervals, making distributed and multi-static SAR systems a very promising solution to this need. This work introduces a concept for distributed SAR interferometry using a cluster of smallsats with small antenna apertures operating with a pulse repetition frequency much smaller than the Doppler bandwidth and capable of single-pass DEM generation with high accuracy and robustness to phase unwrapping errors. The novelty of the proposed method is that the DEM is extracted from a tomogram formed using all available data without requiring as an intermediate step the formation of SAR images. This allows reducing the number of required satellites and obtaining increased baseline diversity at the same time. A cluster of smallsats is a very attractive low-cost solution for the implementation of future interferometric SAR missions.

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.

Highly accurate digital elevation models (DEMs) from spaceborne synthetic aperture radar (SAR) interferometry are often affected by phase unwrapping errors. These errors can be resolved by the use of additional interferograms with different baselines, but this requires additional satellites in a single-pass configuration, resulting in higher cost and system complexity, or additional passes of the satellites, which affects mission planning and makes the system less suitable for monitoring fast-changing phenomena. This work proposes augmenting a bistatic SAR interferometer with one or more receive-only CubeSats, whose images are used to form an additional interferogram with a small baseline, making the system robust to unwrapping errors. In spite of the lower quality of the CubeSat images due to their small antenna aperture, this additional information can be used to detect and resolve phase unwrapping errors in the DEM without impacting its resolution or accuracy. A processing scheme for the phase unwrapping correction is presented along with a theoretical model for its performance. Finally, a design example is presented and discussed along with a simulation based on TanDEM-X data. It is also shown that CubeSat add-ons allow further increasing the baseline and thus improving the accuracy of DEMs. This concept represents a cost-effective solution for the generation of highly accurate, robust DEMs and paves the way to distributed SAR interferometric concepts based on CubeSats.

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.

Antipersonnel mines are hidden weapons that are usually buried close to the surface and are triggered by a foot step of the victim. A sensor that is able to detect minimum metal mines is a ground penetrating synthetic aperture radar (GPSAR). In previous work, an unmanned aerial vehicle (UAV)-based GPSAR was developed and tested for the detection of buried landmines. The real topography was neglected and the interface between air and soil was assumed as a horizontal plane surface. Since the dielectric properties of the soil reduce the propagation velocity of the electromagnetic wave, the interface between air and soil has to be known precisely for subsurface focusing. Neglecting the surface profile has a negative impact on the image quality and prevents the detection of buried objects. In this letter, a novel two-step procedure is used to overcome this issue. In time-division multiplex mode, the radar transmits two frequency-modulated ramps in two different frequency bands. The data of the upper frequency band are used to generate a digital elevation model (DEM) through interferometry. The data of the lower frequency band are used for GPSAR focusing on the basis of the DEM. It is demonstrated that the GPSAR image quality is improved on nonideal planar surfaces and the probability of detection is increased.

For unmanned aerial vehicles (UAVs), grid maps can be a versatile tool for navigation and self-localization. In general, payload is critical for UAVs and every additional sensor limits the flight duration. Due to its robustness and the ability to directly measure velocities, radar sensors are well suited for sense and avoid applications (SAAs) for UAVs. It would be advantageous if these sensor data could be used to generate grid maps instead of mounting additional sensors such as light detection and ranging (LiDAR). This letter demonstrates that using the data from high-resolution multiple-input–multiple-output (MIMO) imaging radars, high-resolution 2-D and 3-D radar grid maps can be created. The necessary adaption of the sensors free-space model for MIMO radar-based occupancy grid maps is presented in detail. UAV-borne measurements resulting in 2-D and 3-D grid maps with an adequate representation of the environment validate this approach.

Anti-personnel fragmentation mines and improvised explosive devices often use metallic wires-so-called tripwires-for the trigger mechanism. Depending on environment and vegetation, these wires are hardly visible. Therefore, an airborne synthetic aperture radar (SAR) was developed to assist the process of mine clearance. In this work, an algorithm for the detection and localization of tripwires in SAR images is proposed. A modified Radon transform is employed that relies on the SAR phase information. Instead of simply integrating absolute values over a straight line, the complex-valued data is analyzed in the frequency domain. The result in the transformed image is then given by the maximum amplitude within a given frequency range, causing tripwires to clearly separate from clutter. The functionality of this algorithm is successfully demonstrated by measurements of tripwires attached to a dummy mine in wet grass.

Antipersonnel fragmentation mines are relatively large metallic mines, which are only partially buried and often triggered by a metallic tripwire. In humanitarian mine clearance, the search for the wires is usually carried out manually. As a new approach, an airborne system for the detection of tripwires using a synthetic aperture radar is presented. The system consists of an industrial multicopter, a frequency-modulated continuous-wave radar, and a real time kinematic global navigation satellite system. For image formation, a backprojection algorithm is used. Measurements with tripwires attached to a dummy mine successfully demonstrate the functionality of this system approach. In addition, the influence of wire length, vegetation, and incidence angle are investigated. It is shown that several overflights with different directions of flight are required to detect randomly oriented tripwires.

In this contribution a polarimetric side-looking synthetic aperture radar (SAR) mounted on a unmanned aerial vehicle (UAV) is presented and discussed with respect to the detection and localization of landmines. As an example for an anti-personal mine a PFM-1 which contains an elongated aluminium rod was considered. Such anisotropic geometries exibit a polarization dependend radar cross section (RCS). Through a special configuration of three antennas, polarimetric SAR measurements involving a back-projection algorithm could be implemented. This concept allows for the detection and furthermore the classification of such anisotropic objects. First field tests using a tachymeter for localization of the UAV over a snow covered meadow successfully demonstrated the performance by the detection of small metal rods depending on their orientation with respect to the flight path of the UAV. These experimental results were supported by simulations expressing the necessity of polarimetric measurements in combination with a distinct flight trajectory for a robust detection of certain landmines.

In man-made surroundings, many structure elements appear as straight line segments in a radar gridmap representation. If they do not move over time, they are well-suited as landmarks for environment-based vehicle self-localization. For use in localization, current observations need to be associated to known landmarks. This paper investigates this association step. Two information sources for association (geometric distance and feature signature) are compared and combined into a common association method.

A subsurface focusing method for multicopter-based mine detection using a ground penetrating synthetic aperture radar (GPSAR) is presented. In the first part of this paper, the challenges of a side-looking single-input single-output (SISO) GPSAR operating in stripmap mode are highlighted by simulation results. As verification, rail-based frequency-modulated continous-wave (FMCW) GPSAR measurement are shown. In the second part of this paper, the 3D imaging circular SAR (CSAR) approach for subsurface focusing is discussed. It is shown that the 3D position of buried point-like targets can be determined unambiguously. As proof of concept simulation results as well as first multicopter-based CSAR measurements are presented.