This article develops and examines methods for the production of real-world, very high-resolution imagery using a high-frequency drone-borne synthetic aperture radar (SAR) operating at short ranges. The significance of motion errors which lead to space-invariant/variant phase errors is discussed. Subsequently, an imaging algorithm capable of handling these errors is proposed and presented. The validity of the approach is tested through both simulation and experiment. We present novel short-range, fine-resolution imagery (less than 2 cm in cross-range) of an extended target area generated using a low-cost, drone-borne vehicular frequency-modulated continuous-wave (FMCW) radar operating at 77 GHz, without using a dedicated inertial navigation system (INS) or Global Positioning System (GPS).

This article develops and examines techniques to enable coherent change detection (CCD) for short-range, high-resolution drone-borne synthetic aperture radar (SAR) systems operating at high frequencies. The potential of using high frequencies at short ranges for fine-resolution imagery and sensitivity to temporal change detection is highlighted, as are the challenges in terms of sensitivity to SAR motion errors. SAR system characteristics for CCD are derived, and the impact of motion errors, which leads to spatial decorrelation and co-registration errors on CCD maps, is discussed. Subsequently, a CCD algorithm able to generate change maps with a >0.75 average coherence value is presented. The validity of the approach is tested through various experimental scenarios. As a result, car tyre marks and human footprints are possible to discern with a drone-borne SAR demonstrator operating at 24 GHz.

This paper deals with the exploitation of shadow regions in high-resolution drone-borne synthetic aperture radar (SAR) images and utilizing them for accurate target height and shape estimation. While prior research has shown the potential of SAR shadows for height estimation, these efforts have primarily focused on airborne and spaceborne platforms and lack a theoretical analysis for assessing achievable performance. In contrast, this work introduces a processing chain for target height and shape extraction and proposes a theoretical framework to evaluate system performance. The study demonstrates that the geometric flexibility and maneuverability of drones offer significant advantages in height estimation accuracy compared to conventional SAR platforms. The proposed method is validated using experimental data from a 24 GHz INRAS radar-equipped drone-borne SAR system, showing strong alignment between the extracted 2D shapes and height estimates and the theoretical predictions. Additionally, the study highlights how multi-perspective drone imagery enables the reconstruction of three-dimensional structures, further extending the potential of drone-borne SAR systems.

In this paper, the extrapolation of a 2D multiple-input multiple-output (MIMO) array is proposed using the Burg algorithm to achieve higher angular resolution beyond that of the corresponding 2D MIMO virtual array. The main advantage of such an approach is that it allows us to dramatically decrease both the physical size and the number of antenna elements of the MIMO array. The performance and limitations of the Burg algorithm are examined through both simulation and experimentation at 77 GHz. The experimental methodology used to acquire 3D data of range, azimuth and elevation information with the 1D MIMO off-the-shelf radar is described. Using this method, the performance of the proposed array can be tested experimentally, especially at frequencies where it is desired to assess the antenna response prior to fabricating the antenna.

This paper presents an algorithm of improving the range resolution of passive synthetic aperture radar (SAR) images by combining multiple terrestrial digital video broadcasting (DVB-T) channels while also suppressing artefacts arising from the direct transmit-receive signal. The proposed algorithm has two parts: spectral estimation techniques for combining DVB-T channels and direct signal suppression (DSS) techniques based on modified CLEAN and sub-aperture imagery-based minimum pixel selection (SA-MIPS). Those capabilities can be achieved with a single-channel, wideband receiver and at shorter ranges and are thus consistent with drone-based operation. The performance of the techniques is demonstrated through both simulation and realworld experiments with a drone-based demonstrator.

This article proposes the study and development of interferometric synthetic aperture radar (InSAR) from compact, high-frequency radar sensors onboard commercial uncrewed aerial vehicles (UAVs), (often referred to as “drones”) to create precision digital elevation models (DEMs). The potential of such InSAR systems, due to the higher operating frequency and target proximity, is quantified, but it is also shown how the same features, combined with the instability of UAV platforms, lead to motion errors that either deteriorate or altogether deny capability. This article thus quantifies acceptable motion error limits and shows how complex autofocus can restore height estimation performance, through analytical modeling that is verified by simulation and validated through outdoor experiments with a 24 GHz UAV-based demonstrator. To our knowledge, this is the first demonstration of a UAV-based InSAR system in this high-frequency band.