Supervised machine learning algorithms usually require huge labeled data sets to produce sufficiently good results. For many applications, these data sets are still not available today, and the reasons for this can be manifold. As a solution, the missing training data can be generated by fast simulators. This procedure is well studied and allows filling possible gaps in the training data, which can further improve the results of a machine learning model. For this reason, this article deals with the development of a two-dimensional electromagnetic field simulator for modeling the response of a radar sensor in an imaging system based on the synthetic aperture radar principle. The creation of completely random scenes is essential to achieve data sets with large variance. Therefore, special emphasis is placed on the development of methods that allow creating random objects, which can then be assembled into an entire scene. In the context of this contribution, we focus on humanitarian demining with regard to improvised explosive devices using a ground-penetrating radar system. This is an area where the use of trained classifiers is of great importance, but in practice, there are little to no labeled datasets for the training process. The simulation results show good agreement with the measurement results obtained in a previous contribution, demonstrating the possibility of enhancing sparse training data sets with synthetic data.

Particle tracking in densely packed granular assemblies is of great interest in mechanical process engineering. In this contribution, a radar-based system for particle localization as an initial step towards tracking is presented. This system comprises six transmitting and receiving antennas forming a “multiple-input multiple-output” setup positioned around a cuboidal reactor. The reactor is a standard batch grate system, which contains stationary spherical polyoxymethylene particles with a 10 mm diameter and a spherical steel tracer particle with a 20 mm diameter. The tracer is positioned at various locations at an optically transparent reactor wall. Electromagnetic waves must pass through the remaining three reactor walls to detect the tracer particle. Operating in the Frequency Modulated Continuous Wave mode within a 1.5 to 8.5 GHz frequency range, we compared radar-detected tracer positions with those from camera images. The results demonstrate a vertical localization accuracy with a standard deviation of ${\sigma }_{\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{t}}=$ 0.86 cm and a horizontal position accuracy with ${\sigma }_{\mathrm{h}\mathrm{o}\mathrm{r}}=$ 0.17 cm. This study not only presents the achievements of radar-based particle localization but also delves into the potential and challenges of applying this technology to a specific measurement scenario within mechanical process engineering.