arXiv subjects

Martin Vossiek

Publications and source records attributed to Martin Vossiek.

22 records · Page 2Linked to original sources

arXiv2023

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.

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arXiv2023

Implementation of Real-Time Automotive SAR Imaging

This paper presents measures to reduce the computation time of automotive synthetic aperture radar (SAR) imaging to achieve real-time capability. For this, the image formation, which is based on the Back-Projection algorithm, was thoroughly analyzed. Various optimizations were individually tested and analyzed on graphics processing units (GPU). Apart from the time reduction gained from these measures, the data size needed for processing was also drastically decreased. With a combination of all measures, a high-resolution SAR image of 30 m by 30 m that combines 8192 chirps can be reconstructed in less than 30 ms using a standard GPU. It is thus demonstrated that a real-time implementation of automotive SAR is possible.

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arXiv2023

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.

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arXiv2023

Achieving Efficient and Realistic Full-Radar Simulations and Automatic Data Annotation by exploiting Ray Meta Data of a Radar Ray Tracing Simulator

In this work a novel radar simulation concept is introduced that allows to simulate realistic radar data for Range, Doppler, and for arbitrary antenna positions in an efficient way. Further, it makes it possible to automatically annotate the simulated radar signal by allowing to decompose it into different parts. This approach allows not only almost perfect annotations possible, but also allows the annotation of exotic effects, such as multi-path effects or to label signal parts originating from different parts of an object. This is possible by adapting the computation process of a Monte Carlo shooting and bouncing rays (SBR) simulator. By considering the hits of each simulated ray, various meta data can be stored such as hit position, mesh pointer, object IDs, and many more. This collected meta data can then be utilized to predict the change of path lengths introduced by object motion to obtain Doppler information or to apply specific ray filter rules in order obtain radar signals that only fulfil specific conditions, such as multiple bounces or containing specific object IDs. Using this approach, perfect and otherwise almost impossible annotations schemes can be realized.

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