Super-Resolution Radar Imaging with Sparse Arrays Using a Deep Neural Network Trained with Enhanced Virtual Data

This paper introduces a method based on a deep neural network (DNN) that is perfectly capable of processing radar data from extremely thinned radar apertures. The proposed DNN processing can provide both aliasing-free radar imaging and super-resolution. The results are validated by measuring the detection performance on realistic simulation data and by evaluating the Point-Spread-function (PSF) and the target-separation performance on measured point-like targets. Also, a qualitative evaluation of a typical automotive scene is conducted. It is shown that this approach can outperform state-of-the-art subspace algorithms and also other existing machine learning solutions. The presented results suggest that machine learning approaches trained with sufficiently sophisticated virtual input data are a very promising alternative to compressed sensing and subspace approaches in radar signal processing. The key to this performance is that the DNN is trained using realistic simulation data that perfectly mimic a given sparse antenna radar array hardware as the input. As ground truth, ultra-high resolution data from an enhanced virtual radar are simulated. Contrary to other work, the DNN utilizes the complete radar cube and not only the antenna channel information at certain range-Doppler detections. After training, the proposed DNN is capable of sidelobe- and ambiguity-free imaging. It simultaneously delivers nearly the same resolution and image quality as would be achieved with a fully occupied array.Comment: 15 pages, 12 figures, Accepted to IEEE Journal of Microwave

A Computationally Efficient Reconstruction Approach for Imaging Layered Dielectrics With Sparse MIMO Arrays

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

Secondary Radar Beacons for Local Ad-Hoc Autonomous Robot Localization Systems

In this paper, we present a detailed analysis and implementation of secondary radar beacons designed for a local ad-hoc localization and landing system (LAOLa) to support the navigation of autonomous ground and aerial vehicles. We discuss a switched linear feedback network as a virtually coherent oscillator and show how to use it as a secondary radar transponder. Further, we present a signal model for the beat signal of the transponder response in an FMCW radar system, which is more detailed than in previously published papers. An actual transponder realization in the 24 GHz ISM band is presented. Its RF performance was evaluated both in the laboratory and in the field. Finally, we put forward some ideas on how to overcome the range measurement inaccuracy inherent in this transponder concept

SAW RFID Tag Spatial Division Multiple Access Based on 3D Reflector Response Localization Using a Wideband Holographic Approach

Surface acoustic wave (SAW) radio-frequency identification (RFID) has high potential for industrial applications, where automated identification and localization of assets represent the backbone of process controlling and logistics. However, in situations where multiple tags are simultaneously interrogated, the response patterns corresponding to the hard-coded reflectors are prone to overlap, preventing their association with the corresponding tags and, hence, the correct tag decoding. Identification and localization of multiple SAW RFID tags are addressed in this work under this challenging effect, known as collision, with a multi-antenna mobile robot-based synthetic aperture approach. Using the estimation of the spatial probability density functions of the SAW tag reflectors over a given interrogation aperture, the received impulse responses can be resolved in three dimensions and clustered with respect to their estimated locations. The performance of the proposed approach to associate and localize the signals from multiple tags was evaluated theoretically and experimentally

An Extended Kalman Filter for Direct, Real-Time, Phase-Based High Precision Indoor Localization

Radio-based indoor localization is currently a very vibrant scientific research field with many potential use cases. It offers high value for customers, for example, in the fields of robotics, logistics, and automation, or in context-aware IT services. Especially for autonomous systems, dynamic human-machine interaction, or augmented reality applications, precise localization coupled with a high update rate is a key. In this paper, we present a completely novel localization concept whereby received radio signal phase values that are fed into an extended Kalman filter (EKF) without any preprocessing are evaluated. Standard preprocessing steps, such as angle-of-arrival estimation, beamforming, and time-of-flight or time-difference-of-arrival estimations are not required with this approach. The innovative localization concept benefits from the high sensitivity of radio signals' phase to distance changes and the fast and straightforward recursive computation offered by the EKF. It completely forgoes the computational burden of other phase-based high-precision localization techniques, such as synthetic aperture methods. To verify the proposed method, we use an exemplary setup employing a 24 GHz frequency-modulated continuous-wave (CW) single-input multiple-output secondary radar with 250 MHz bandwidth. A high-precision six-axis robotic arm serves as a 3D positioning reference. The test setup emulates a realistic industrial indoor environment with significant multipath reflections. Despite the challenging conditions and the rather low bandwidth, the results show an outstanding localization 3D RMSE of around 1.7 cm. The proposed method can easily be applied to nearly any type of radio signal with CW carrier and is an attractive alternative to common multilateration and multiangulation localization approaches. We think it is a quantum leap in wireless locating, as it has the potential for precise, simple, and low-cost wireless localization even with standard narrowband communication signals

Radar-Based Recognition of Static Hand Gestures in American Sign Language

In the fast-paced field of human-computer interaction (HCI) and virtual reality (VR), automatic gesture recognition has become increasingly essential. This is particularly true for the recognition of hand signs, providing an intuitive way to effortlessly navigate and control VR and HCI applications. Considering increased privacy requirements, radar sensors emerge as a compelling alternative to cameras. They operate effectively in low-light conditions without capturing identifiable human details, thanks to their lower resolution and distinct wavelength compared to visible light. While previous works predominantly deploy radar sensors for dynamic hand gesture recognition based on Doppler information, our approach prioritizes classification using an imaging radar that operates on spatial information, e.g. image-like data. However, generating large training datasets required for neural networks (NN) is a time-consuming and challenging process, often falling short of covering all potential scenarios. Acknowledging these challenges, this study explores the efficacy of synthetic data generated by an advanced radar ray-tracing simulator. This simulator employs an intuitive material model that can be adjusted to introduce data diversity. Despite exclusively training the NN on synthetic data, it demonstrates promising performance when put to the test with real measurement data. This emphasizes the practicality of our methodology in overcoming data scarcity challenges and advancing the field of automatic gesture recognition in VR and HCI applications.Comment: 5 pages, 6 figures. Accepted to IEEE Radarconf202

A Wireless Angle and Position Tracking Concept for Live Data Control of Advanced, Semi-Automated Manufacturing Processes

Despite recent industrial automation advances, small series production still requires a considerable amount of manual work, and training, and monitoring of workers is consuming a significant amount of time and manpower. Adopting live monitoring of the stages in manual production, along with the comprehensive representation of production steps, may help resolve this problem. For ergonomic live support, the overall system presented in this paper combines localization, torque control, and a rotation counter in a novel approach to monitor of semi-automated manufacturing processes. A major challenge in this context is tracking, especially hand-guided tools, without the disruptions and restrictions necessary with rigid position encoders. In this paper, a promising measurement concept involving wireless wave-based sensors for close-range position tracking in industrial surroundings is proposed. By using simple beacons, the major share of processing is transferred to fixed nodes, allowing for reduced hardware size and power consumption for the wireless mobile units. This requires designated localization approaches relying on only relative phase information, similar to the proposed Kalman-filter-based-beam-tracking approach. Measurement results show a beam-tracking accuracy of about 0.58 ∘ in azimuth and 0.89 ∘ in elevation, resulting in an overall tracking accuracy of about 3.18 cm

A Realistic Radar Ray Tracing Simulator for Hand Pose Imaging

With the increasing popularity of human-computer interaction applications, there is also growing interest in generating sufficiently large and diverse data sets for automatic radar-based recognition of hand poses and gestures. Radar simulations are a vital approach to generating training data (e.g., for machine learning). Therefore, this work applies a ray tracing method to radar imaging of the hand. The performance of the proposed simulation approach is verified by a comparison of simulation and measurement data based on an imaging radar with a high lateral resolution. In addition, the surface material model incorporated into the ray tracer is highlighted in more detail and parameterized for radar hand imaging. Measurements and simulations show a very high similarity between synthetic and real radar image captures. The presented results demonstrate that it is possible to generate very realistic simulations of radar measurement data even for complex radar hand pose imaging systems.Comment: 4 pages, 5 figures, accepted at European Microwave Week (EuMW 2023) to the topic "R28 Human Activity Monitoring, including Gesture Recognition

Mechanical performance evaluation of fiber composites equipped with In-Situ wireless sensor bodies

In modern day structural engineering, fiber-composites play a vital role for their capability for light-weight construction and high stiffness value. More and more applications are being developed in various industries ranging from science, architecture and engineering. These structures can also be equipped with multi-component sensor systems for different performance evaluations both during pre- and post-curing processes. In this work a novel method is developed to place wireless sensors inside the fiber reinforced composite system to enable multifunctionality without much trade-off in mechanical performance. Key objective here was to optimize the sensor shape to minimize stress accumulation and crack propagation around the sensor geometry inside the cured composite sample under stress. A finite element simulation model is developed for this purpose and a parametric model for the sensor geometry provided better insight into the force distribution along the fibers around the sensor element. Consequently, different testing sample combinations were prepared, for which, fibers were either cut or bend around the sensors and dielectric channels. Various composite samples with different shapes of sensor dummies were also experimentally tested to validate the computational results. CT scan models of post-cure samples before and after loading enabled in-depth understanding of fiber alignment that could cause disturbances in overall mechanical performance. The scan models also provided with sufficient information about unwanted porosity, and micro-crack growth inside the composite under loading, which turned out to be vital for establishing a reliable simulation model and improving parameters in manufacturing process. In the end, the goal of the work was to transport the know-how of such production unit from experimental and flexible manufacturing system like vacuum assisted resin infusion (VARI) to more sophisticated processing systems like prepreg manufacturing where all necessary information can be provided as inputs prior to the impregnation, thus removing error occurred due to manual handling