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Climate change is associated with a variety of environmental phenomena, such as the melting of glaciers, the drying out of crops and soils, and an increase in the risk of avalanches. To detect and monitor these changes, satellite-based radar imaging is widely used, as it enables the large-scale mapping of the Earth's surface independent of weather and sunlight. An emerging field of research is to use radar systems mounted on unmanned aerial vehicles (UAVs) for this purpose. UAV-borne radar systems image smaller areas, but can capture them in greater detail, more flexibly in terms of flight trajectory, and at shorter repetition intervals. This article presents the capabilities of UAV-based radar systems and the associated challenges in terms of system design and imaging techniques for detecting and monitoring the effects of climate change. Current research results are shown. In addition, we give an outlook on using a swarm of cooperative UAVs, each carrying a radar. UAV swarms will enable a higher imaging quality with respect to resolution and detectability, but at the same time come with a number of additional challenges, which will be discussed in the paper as well.

Radio detection and ranging-based (radar) sensing offers unique opportunities for biomedical monitoring and can help overcome the limitations of currently established solutions. Due to its contactless and unobtrusive measurement principle, it can facilitate the longitudinal recording of human physiology and can help to bridge the gap from laboratory to real-world assessments. However, radar sensors typically yield complex and multidimensional data that are hard to interpret without domain expertise. Machine learning (ML) algorithms can be trained to extract meaningful information from radar data for medical experts, enhancing not only diagnostic capabilities but also contributing to advancements in disease prevention and treatment. However, until now, the two aspects of radar-based data acquisition and ML-based data processing have mostly been addressed individually and not as part of a holistic and end-to-end data analysis pipeline. For this reason, we present a tutorial on radar-based ML applications for biomedical monitoring that equally emphasizes both dimensions. We highlight the fundamentals of radar and ML theory, data acquisition and representation and outline categories of clinical relevance. Since the contactless and unobtrusive nature of radar-based sensing also raises novel ethical concerns regarding biomedical monitoring, we additionally present a discussion that carefully addresses the ethical aspects of this novel technology, particularly regarding data privacy, ownership, and potential biases in ML algorithms.

The radar-based analysis of human motion is actively being researched due to its contact- and markerless nature and ability to measure motion directly via the Doppler effect. Especially in medical and biomechanical fields, precise movement analysis is crucial. However, existing radar-based studies typically exhibit low lateral resolution, focusing on velocity evaluations and the tracking of scattering centers resolvable in the range or Doppler domains. In this work, we present a novel concept that enables a pixel-wise velocity analysis of human motion in radar near-field imaging scenarios. For this, we utilize the well-established back-projection technique to reconstruct consecutive radar images and perform a subsequent pixel-wise phase comparison. To accurately capture pixel-specific velocities along the depth dimension, this is followed by corrections of near-field geometry distortions accounting for aperture properties and pixel positions. Our theoretical derivations are supported by comprehensive point target simulations. To assess the performance of the proposed approach, we conducted a proof-of-concept study. We tracked a hand surface's movement while performing a finger tapping motion and compared the fingertip position and velocity determined by the radar with the respective values obtained from an optical marker-based system. The results showed a velocity measurement accuracy of $8.1 \,\mathrm{mms}^{-1}$ and a tracking accuracy of $1.4 \,\mathrm{m}\mathrm{m}$, demonstrating the great potential of our approach. The high angular resolution of the velocity measurement enables the tracking of the entire illuminated body shell, extending the range of future applications of radar-based motion analysis.

Reliable and convenient walk-through security scanning, which doesn't separate people or impede their movement, is an extremely challenging task. In this paper, a novel approach for a security check with an overhead observation and a polarimetric target decomposition is presented. The viewing angle of the scanner equals a side-looking airborne radar. However, it will be shown that the established polarimetric target decomposition methods of remote-sensing are not well suited for close-range radar imaging and need to be adapted due to the differences in the geometry of the imaging scenario. The usage of a multiple input and multiple output (MIMO) array and the shorter distance between array and target need a changed decomposition technique in order to distinguish between persons with or without worn threat objects. The differences between radar remote-sensing and close-range imaging scenarios are investigated. An optimized version of the Sato four-component polarimetric scattering decomposition is derived. The proposed close-range adjustment is applied to the model and investigated experimentally with a 4-to-12 GHz fully polarimetric MIMO imaging system. Polarimetric decomposition is carried out on defined test structures with known scattering mechanisms, as well as on mannequins and persons with different threat objects like guns or explosives. In test campaigns, promising results were achieved for a correct target decomposition in radar-based walk-through security scanning.

Nowadays, additive manufacturing provides far-reaching possibilities for use in radio frequency components. In addition to almost unlimited freedom of design as compared to conventional manufacturing, the absence of further assembly steps is a key aspect of 3D printing. In this paper, a 3D printed monolithic antenna for millimeter wave-sensing applications is presented with a full hemispherical coverage. The antenna is designed as an ensemble of a waveguide horn antenna and a differentially fed dipole antenna. The slotted waveguide approach was utilized to improve the manufacturing quality on the waveguide inside. The influence of two optimized antenna elements, a metal plane, and a cut-out window, on the beam pattern is comprehensively investigated. A huge half power beam width of 142° in both directions, elevation and azimuth, is presented at 79 GHz and a boresight gain of 4.7 dBi was measured. The beam pattern in the frequency range from 76 to 81 GHz is studied in greater detail, where a half power beam width of at least 112° is achieved. Due to the −10 dB matching capability bandwidth of over 28 GHz, the antenna is also suitable for extremely broadband applications with a −5 dB angular width of better than 100°. Furthermore, the system design describes how to integrate the antenna into hybrid circuit designs and the manufacturing tolerances are examined. The antenna offers attractive possibilities for millimeter wave-sensing applications in the area of assisted living and industrial monitoring, especially whenever blind spots have to be avoided.

In this paper, new concepts for continuous 24/7 real-time monitoring of plants and insects with miniaturized terahertz (THz) systems are described and experimentally tested. Thus, for the first time, small-scale insights into the instantaneous plant health but also in their long-term growth can be obtained. Using such compact THz systems, e.g. water uptake, insect infestation and the behavior of pollinators (i.e. honey bees) and pests can be measured dynamically and non-invasively at virtually any position in the close biotope surrounding them. In addition to general understanding, this can be used to optimize crop yield and reduce resource consumption as well as for identifying characteristics of insect-plant interactions induced by potential environmental stressors. Given such a holistic approach, the proposed concepts may provide a significant advancement in environmental monitoring technology.

Radar-based human motion and activity recognition is currently a topic of great research interest, as the aging population increases and older individuals prefer an independent lifestyle. This technology has a wide range of applications, such as fall detection in assisted living, gesture recognition for human-machine interfaces, and many more. Numerous studies exist on various approaches for radar-based activity capture and classification. However, most of these employ rather artificial data, often obtained in laboratory environments, and typically collected under particular conditions. Specifically, most research so far has aimed at distinguishing a predefined set of single activities with a defined start, stop and duration. This paper aims at drawing the attention to a so far less researched issue, one that will be of vital importance for future real-world application of radar-based human activity recognition: continuous activity recognition, i.e. recognizing specific activities in a stream of several sequential activities with unknown duration and arbitrary transitions between different classes of activities. A review on the current state of the art in this relatively new topic is given, followed by a discussion on future research directions.

Abstract Historical documents contain essential information about the past, including places, people, or events. Many of these valuable cultural artifacts cannot be further examined due to aging or external influences, as they are too fragile to be opened or turned over, so their rich contents remain hidden. Terahertz (THz) imaging is a nondestructive 3D imaging technique that can be used to reveal the hidden contents without damaging the documents. As noise or imaging artifacts are predominantly present in reconstructed images processed by standard THz reconstruction algorithms, this work intends to improve THz image quality with deep learning. To overcome the data scarcity problem in training a supervised deep learning model, an unsupervised deep learning network (CycleGAN) is first applied to generate paired noisy THz images from clean images (clean images are generated by a handwriting generator). With such synthetic noisy-to-clean paired images, a supervised deep learning model using Pix2pixGAN is trained, which is effective to enhance real noisy THz images. After Pix2pixGAN denoising, 99% characters written on one-side of the Xuan paper can be clearly recognized, while 61% characters written on one-side of the standard paper are sufficiently recognized. The average perceptual indices of Pix2pixGAN processed images are 16.83, which is very close to the average perceptual index 16.19 of clean handwriting images. Our work has important value for THz-imaging-based nondestructive historical document analysis.

Millimeter-wave radar imaging is a promising technique in non-destructive testing. When a small material defect is located closely to the test object’s surface, the strong reflection caused by the air-material interface can overlap with the defect’s weak reflection and mask it. If the image resolution is not sufficient, the defect cannot be resolved and will not be recognizable in the reconstruction image. To solve this problem, surface echo compensation techniques have been invented. However, these techniques are usually restricted to planar surfaces, which for most applications is not the case. This article presents a technique for surface echo compensation of arbitrarily shaped objects. We propose to combine radar and air-coupled ultrasound. Since aircoupled ultrasound is not able to penetrate solids, an air-coupled ultrasound imaging system is only able to detect the surface of a solid object. The idea of our proposed concept is to employ the ultrasound signal to compensate for the surface echo in the radar signal. The proposed method is based on the idea of using signals of equal wavelengths for both wave types and an amplitude calibration. Then, the resulting signals for radar and ultrasound are formally equal, which makes it possible to directly combine them numerically. That way, a modified radar image can be obtained which only contains the reflections from the inside and highlights interior defects.

In classical radar imaging, such as in Earth remote sensing, electromagnetic waves are usually assumed to propagate in free space. However, in numerous applications, such as ground penetrating radar or non-destructive testing, this assumption no longer holds. When there is a multi-material background, the subsurface image reconstruction becomes considerably more complex. Imaging can be performed in the spatial domain or, equivalently, in the wavenumber domain (k-space). In subsurface imaging, to date, objects with a non-planar surface are commonly reconstructed in the spatial domain, by the Backprojection algorithm combined with ray tracing, which is computationally demanding. On the other hand, objects with a planar surface can be reconstructed more efficiently in k-space. However, many non-planar surfaces are partly planar. Therefore, in this paper, a novel concept is introduced that makes use of the efficient k-space-based reconstruction algorithms for partly planar scenarios, too. The proposed algorithm forms an image from superposing sub-images where as many image parts as possible are reconstructed in the wavenumber domain, and only as many as necessary are reconstructed in the spatial domain. For this, a segmentation scheme is developed to determine which parts of the image volume can be reconstructed in the wavenumber domain. The novel concept is verified by measurements, both from monostatic synthetic aperture radar data and multiple-input–multiple-output radar data. It is shown that the computational efficiency for imaging irregularly shaped geometries can be significantly augmented when applying the proposed concept.

This paper introduces a novel deep learning based concept for image enhancement and distortion suppression in automotive radar signal processing. The deep neural network (DNN) is trained solely on virtual data that is generated by an automotive MIMO radar ray tracing simulator. The simulator mimics the raw data that would be provided by a specific automotive MIMO radar for which the signal processing is envisaged. This virtual radar sensor, which has the same properties as the real radar, creates the DNN training input data. The unique feature of our approach is that an advanced virtual radar sensor is used to create the target values for the DNN training. The advanced virtual sensor is a simulated radar that works on the same basic principle as the real radar and its resembling virtual sensor, but is significantly upgraded in terms of performance. In our experiments the advanced virtual sensor, e.g., has a notably larger array, i.e. a much better lateral resolution than the real radar. In addition, our advanced virtual sensor is not affected by multi-path effects, clutter, noise, beamforming sidelobes and other typical automotive radar distortions. The paper shows that a DNN trained on this advanced virtual sensor training data can deliver outstanding automotive radar signal processing results not only on simulated data but also on field data. The presented real-world automotive radar measurements show that multi-path, clutter and noise are efficiently suppressed by this DNN and that it has a remarkable ability to sharpen images and suppress sidelobes. The results presented in the paper suggest that the novel advanced DNN training concept based on virtual sensors offers opportunities that also go far beyond the radar signal processing capabilities illustrated. Hence, the concept put forward here is an attractive option for placing DNN-based radar signal processing on a novel and easy-to-implement foundation.

Recently, a novel chipless radio-frequency identification (RFID) concept based on polarimetric radar barcodes was introduced. These tags are similar to the well known optical quick response codes (QR codes). In polarimetric radar barcodes the information is stored as polarimetrically coded areas arranged in a grid and read out by an imaging radar. This paper presents a novel concept for elegantly creating polarimetric barcodes based on dipole scattering domains. Each scattering domain consists of several hundred dipoles. The dipoles are either uniformly arranged at a specific angle, which produces a specific angle-dependent polarization response, or randomly oriented to produce an unpolarized response. In order to read out the polarimetric information stored in the tags, the position and rotation of the radar barcodes needs to be determined. Our research reveals that using three reference elements and a novel decoding algorithm, a position- and rotation-tolerant readout is possible. To reduce distortions caused by the surface of the structure carrying the dipoles, different matching layer concepts were investigated. The novel chipless RFID tag concept is tested using a fully polarimetric W-band (75-$110 \,\mathrm{G}\mathrm{Hz}$) imaging radar. The implemented setup allows for a theoretical data capacity of $1.92 \,\mathrm{bit} \mathrm{/}\mathrm{c}\mathrm{m}^{2}\,$ on a single layer PCB. In our measurements, we achieved a data capacity of $1.52 \,\,\mathrm{bit} \mathrm{/}\mathrm{c}\mathrm{m}^{2}\,$. Utilizing the chosen tag size of $45 \,\mathrm{m}\mathrm{m} \times 45 \,\mathrm{m}\mathrm{m}$ resulted in a tag with a greater than 30 bit data capacity, which is an excellent value for chipless RFID.

This contribution presents a novel, computationally efficient approach to radar imaging of layered dielectrics with sparse MIMO arrays. Our concept does not impose any constraints on the array topology and at the same time promises to be more efficient than the state-of-the-art backprojection algorithm because it can make use of k-space reconstruction schemes. Experimental results with a sparse, non-equidistantly sampled array are provided. These demonstrate the feasibility of the approach and that the computational burden could be reduced by several orders of magnitude in a given practical example related to radar based non-destructive testing.

With the rise of high resolution multiple input multiple output (MIMO) systems, radar became an important sensor in the development of Advanced Driver Assistance Systems (ADAS) and autonomous driving applications. Autonomous driving will rely strongly on artificial intelligence. Since most modern classification algorithms are based on neural networks, they require huge amounts of data to perform well, especially in unexpected traffic situations. Radar sensor simulation can potentially produce a great variety of training data for machine learning algorithms, which makes it an important cornerstone in the development of ADAS. Furthermore, with radar simulators, different antenna configurations and various edge cases can be simulated. In this work, a versatile ray tracing toolchain based on the shoot and bouncing rays (SBR) approach is presented. The program is able to simulate complex urban environments including realistic clutter, by utilizing simplistic reflection models. The program does not only produce realistic radar images, but also generates camera-like images using the same materials. Furthermore, this work deals with the adaption of the SBR method to radar sensors with an arbitrary number of transmit (TX)- and receive (RX) antennas, which enables the simulation of large MIMO arrays. A novel performance optimization approach is proposed for large numbers of TX antennas, which reduces the runtime dramatically. The quality of the simulation is verified by measuring a complex and realistic scenario with a high resolution automotive MIMO radar. Also, a study of the effect on quality and runtime is being investigated for various optimization approaches, including the proposed method.

Subsurface radar imaging provides an insight into hidden scenes: For example, in security screening, radar can be used for the detection of dangerous objects or contraband. In non-destructive testing, radar imaging also allows to find internal material defects such as cracks or contaminations. Object contours and internal structures appear as echoes in the radar receive signal. The strongest echo typically is the one from the test object's surface. This echo can mask smaller echoes close to the surface and therefore, small inner structures may become invisible in the reconstruction image. To overcome this problem, we propose a novel concept to eliminate the surface echo in radar imaging. In contrast to existing surface elimination techniques, our approach can be used not only when screening objects with planar surfaces, but also on such that exhibit an unknown and possibly non-planar profile. Our approach is to combine radar and air-coupled ultrasound. Since air-coupled ultrasound cannot penetrate into solids, the receive signal only contains the echo from the test object's surface. The core of our concept is to generate radar and ultrasound signals with the same characteristics, i.e. they are equal in amplitude and phase. Then, the ultrasound data can be subtracted from the radar data and the resulting signal will only contain the echoes of the inner structures. When processing this data, the reconstructed images are almost free of reflections from the surface.