This article investigates the impact of fire on electromagnetic waves within a 70–90 GHz frequency range. To gain a deeper understanding of interactions between fire and radar signals, it is essential to consider a dielectric model for flames. Three primary mechanisms have been identified: the relative permittivity of reaction products, absorption of ionized gases, and the influence of incomplete combustion. To explore these mechanisms, two distinct test fires (TF) were examined in a fire laboratory in a style of the European standard EN54. Initial experimental series involved the combustion of liquid n-heptane, followed by a series of experiments focusing on open polyurethane combustion. Measurements were conducted using a frequency modulated continuous wave radar sensor. Additionally, a PT1000 resistor and an optical extraction measuring instrument were used as reference. Based on highly precise phase evaluation of the measurement signal, initial correlations were established between the radar-measured phase and the measured particle density. The proven ability to differentiate between two TF suggests the potential for fire detection using frequency-modulated continuous wave (FMCW) radar sensors.

This work introduces a method for plasma state supervision, based on a frequency-modulated continuous wave radar sensor and a suitable signal evaluation enabling a continuous supervision method for the plasma state. Highly precise phase evaluation of the signal allows us to detect and visualize smallest changes in the plasma state. Assuming the plasma to act like a frequency-dependent dielectric material, the propagation of the electromagnetic wave depends on the plasma state and hence, also the measured phase. Broadband measurements are carried out at center frequencies of 80 and 140 GHz in a low-pressure plasma. The radar-based setup can be used for a very flexible application, capable for spatially resolved measurements in the plasma bulk. At the same time, the high measurement rate allows for quasi real-time monitoring, so that transient processes in the plasma are recorded. Due to the simple setup, this approach is most suitable for industrial applications to improve process control. The chosen different frequencies will show a change in the influence of the plasma on the electromagnetic wave demonstrating the advantages of multi-frequency approaches in future applications.

Understanding and detecting smoke effectively is crucial in emergency scenarios where traditional optical methods may fail. This article investigates the definition of smoke and its characterization from an electromagnetic perspective, focusing particularly on the use of frequency-modulated continuous wave (FMCW) radar sensors operating in the millimeter-wave range. We explore the influence of both laminar and turbulent smoke flows on the measurement accuracy. Our study is grounded in dielectric models in the millimeter-wave spectrum, demonstrating that the dielectric properties of smoke exhibit minimal variations. Consequently, we use phase-based radar signal processing to detect these subtle changes. Unlike previous studies that primarily evaluate sensor performance, this article aims to use the minimal impact on the measured signal to characterize different smoke scenarios comprehensively. Our findings demonstrate that radar sensors can provide valuable insights into smoke properties and are suitable to extend material model for millimeter-wave frequencies, enhancing situational awareness and response strategies in smoke-obscured environments.

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.

Traditional smoke detection systems share inherent limitations. Inter alia, these systems are fundamentally limited by their purely reactive and non-predictive operation. The objective of this contribution is to extend the advantageous probing capabilities of radar systems toward the augmentation of traditional smoke detectors for continuous condition and hazard monitoring. Particular emphasis is placed here on early-stage hazards and critical precursor conditions for preventive fire safety. To this end, static-target radar echoes are classified using deep learning (DL) methods by extracting characteristic patterns caused by subtle changes in the surrounding medium (e.g., water vapor). Measurements were conducted employing ultra-wideband frequency-modulated continuous-wave (FMCW) radar systems operating from 80 GHz to 240 GHz, in a controlled indoor proof-of-concept setup, enabling a systematic multispectral analysis. Several scenarios representative of domestic settings were considered, including the frying of a burger patty (UL 268 standard). The proposed concept achieves classification accuracies of more than 90%, even under strong restrictions. The developed models remain lightweight and fast, well suited for embedded implementations.