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.

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 capturing of hands, including their poses, shapes and motions, has numerous potential applications, such as human-machine interfaces and medical use cases. However, in the radar context, most existing methods only allow for the recognition of dynamic hand gestures based on Doppler evaluations due to the respective systems’ limited lateral resolution. Radar-based high-resolution three-dimensional (3D) imaging using multiple-input multiple-output (MIMO) radars is currently the state-of-the-art in personnel security scanning. However, the associated imaging techniques suffer from computationally burdensome reconstruction algorithms that sample the entire 3D space of interest, thereby making them less suitable for real-time applications. Moreover, their application in hand motion tracking scenarios is limited by low frame rates that result from a high number of transmit frequencies. Hence, we present an efficient and powerful approach for the radar-based 3D reconstruction of hand poses. The method extends the frequency shift keying continuous wave radar principle and reconstructs the hand surface using only two carrier frequencies. Instead of reconstructing an entire 3D volume, only two single-tone radar images are computed. Depth information is derived from phase differences between corresponding pixels in the images. The approach significantly reduces computational load by three orders of magnitude compared with the state-of-the-art and enables higher frame rates. Within this paper, this novel reconstruction principle is analyzed and compared to a state-of-the-art radar imaging approach using a MIMO radar system with 94 transmitting and 94 receiving antennas. Detailed simulations of point targets and comprehensive measurements demonstrate the excellent imaging performance of our approach.

In light of the state-of-the-art treatment options for patients with rheumatoid arthritis (RA), a detailed and early quantification and detection of impaired hand function is desirable to allow personalized treatment regiments and amend currently used subjective patient reported outcome measures. This is the motivation to apply and adapt modern measurement technologies to quantify, assess and analyze human hand movement using a marker-based optoelectronic measurement system (OMS), which has been widely used to measure human motion. We complement these recordings with data from markerless (Doppler radar) sensors and data from both sensor technologies are integrated with clinical outcomes of hand function. The technologies are leveraged to identify hand movement characteristics in RA affected patients in comparison to healthy control subjects, while performing functional tests, such as the Moberg-Picking-Up Test. The results presented discuss the experimental framework and present the limiting factors imposed by the use of marker-based measurements on hand function. The comparison of simple finger motion data, collected by the OMS, to data recorded by a simple continuous wave radar suggests that radar is a promising option for the objective assessment of hand function. Overall, the broad scope of integrating two measurement technologies with traditional clinical tests shows promising potential for developing new pathways in understanding of the role of functional outcomes for the RA pathology.

Near-field multiple-input multiple-output (MIMO) radar systems allow for high-resolution spatial imaging by leveraging multiple antennas to transmit and receive signals across multiple perspectives. This capability is particularly advantageous in challenging environments, where optical imaging techniques struggle. We present a novel approach to inverse rendering for near-field MIMO radar systems, aimed at reconstructing material properties such as surface roughness, dielectric constants, and conductivity from radar and ground-truth mesh data, for example obtained from multi-view stereo. Drawing inspiration from physically based rendering techniques in computer graphics, we formalize an advanced inverse rendering algorithm that integrates electromagnetic wave propagation models directly into the optimization process. To avoid bias from conventional radar image reconstruction algorithms in the optimization process, we directly derive gradients from raw radar outputs, resulting in more accurate material characterization. We validate our approach through extensive experiments on both synthetic and real radar datasets, demonstrating its effectiveness in a multitude of scenarios.

Utilizing the complementary strengths of wavelength-specific range or depth sensors is crucial for robust computer-assisted tasks such as autonomous driving. Despite this, there is still little research done at the intersection of optical depth sensors and radars operating close range, where the target is decimeters away from the sensors. Together with a growing interest in high-resolution imaging radars operating in the near field, the question arises how these sensors behave in comparison to their traditional optical counterparts. In this work, we take on the unique challenge of jointly characterizing depth imagers from both, the optical and radio-frequency domain using a multimodal spatial calibration. We collect data from four depth imagers, with three optical sensors of varying operation principle and an imaging radar. We provide a comprehensive evaluation of their depth measurements with respect to distinct object materials, geometries, and object-to-sensor distances. Specifically, we reveal scattering effects of partially transmissive materials and investigate the response of radio-frequency signals. All object measurements are made public in form of a multimodal dataset, called MAROON, which can be accessed at: https://vwirth.github.io/maroon .