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.

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.

The field of multicopter unmanned aerial vehicles (UAVs) has seen massive technological advances and decreasing costs over the last decade. Due to the higher prevalence and availability of these multicopter UAVs, there have also been rapid improvements in the software interfaceability, enabling the easy integration of custom software for flight planning and controllers. This opened up completely new possibilities, such as low-cost individual aerial photography, the most common civilian application. Furthermore, lightweight multicopters became widely accessible to the public, boosting the development of UAVs even more.

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 holistic a approach, the proposed concepts may provide a significant advancement in environmental monitoring technology.

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.

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.