Accelerating Deep Learning in Radar Systems: A Simulation Framework for 60 GHz Indoor Radar - Reitz, Philipp; Maiwald, Timo; Bönsch, Jonas; Franchi, Norman; Lübke, Maximilian (Remote Sensing, 2024)
FMCW radar systems are increasingly used in diverse applications, and emerging technologies like JCAS offer new opportunities. However, machine learning for radar faces challenges due to limited application-specific datasets, often requiring advanced simulations to supplement real-world data. This paper presents a setup for generating synthetic radar data for indoor environments, evaluated using CNNs. The setup involves comprehensive modeling, including far-field antenna simulations, variations in human radar cross-section, and detailed representations of indoor environments with their corresponding propagation channel properties. These synthetic data are used to train CNNs, and their performance is assessed on real measurement data. The results demonstrate that CNNs trained on synthetic data can perform well when tested on real measurement data. Specifically, the models trained with synthetic data showed performance comparable to models trained with real measurement data, which required a minimum of 300 samples to reach similar levels of accuracy. This result demonstrates that synthetic data can effectively train neural networks, providing an alternative to real measurement data, particularly when collecting sufficient real-world samples is difficult or costly. This approach significantly reduces the time required for generating datasets, and the ability to quickly label data in simulations simplifies and accelerates post-processing. Additionally, the generated datasets can be made more heterogeneous by introducing varying signal conditions, enhancing the diversity and robustness of the training dat
Keywords: convolutional neural network; FMCW radar; mm-wave; radar cross section; simulation and measurement

A Human Pose Recognition Model Comparison to Label Radar Data for Gesture Recognition in Robotics - Maiwald, Timo; Ederer, Johannes; Fischer, Georg; Lurz, Fabian (Latin America Microwave Conference (LAMC), 2025)
Instantaneous radar data labeling during data set acquisition, through camera based computer vision methods, is a challenging task. In context of human pose and gesture recognition, there is a huge amount of computer vision models available and whether they are suitable for instantaneous data labeling needs to be investigated. Therefore, this paper compares 20 different models regarding inference time and human keypoint location deviations. The most suitable model is statistically investigated further using more than 250 thousand images, where person presence detection and gesture detection are evaluated using simple threshold methods. Obtained results reveal 94% of all images are correctly classifie
Keywords: camera; computer vision; data labeling; gesture recognition; radar

Automated Radar Data Labeling through Computer Vision - Gabsteiger, Jasmin; Maiwald, Timo; Wünsche, Simon; Weigel, Robert; Lurz, Fabian (Wamicon, 2023)
This paper presents the combination of an existing computer vision model with a highly integrated mmwave radar system for automated radar data labeling. A pre trained model for person presence detection is used in combination with a camera sensor to determine, whether a person is present or not. The binary prediction is used to label simultaneously measured radar data of the same scenery. The paper addresses the challenges of varying illumination conditions, camera and radar sensor aperture angles and data quality which are dominant influences at the labeling process. The system significantly decreases human effort by automating the labeling process. It is used to generate 10,000 data points, label them and train a neural network with 95.7% accuracy. Finally, a proof-of-concept, training, and evaluation of radarbased activity detection with automatically labeled data is presented. The proposed method can contribute in person presence detection under challenging light condition
Keywords: radar data labeling; computer vision

Automated Radar Data Labeling using MoveNet for Human Gesture Recognition - Maiwald, Timo; Gabsteiger, Jasmin; Weigel, Robert; Lurz, Fabian (2023 Asia-Pacific Microwave Conference (APMC), 2024)
Integrated radar sensors constitute an emerging technology used from smart home to autonomous driving applications. In combination with machine learning for radar data signal processing, they experience increasing popularity and are heavily investigated. These methods however need huge amounts of labeled training data to perform well in realistic scenarios, which is time consuming and cost intensive. To increase data acquisition speed, this paper presents a system using a fast computer vision model for automated radar data labeling. It is capable of generating 10 labeled radar data frames per second. To demonstrate the abilities it is used for human gesture recognition.<br
Keywords: gesture recognition; fmcw radar; computer vision; machine learning

A Wireless Lightweight System Node for Energy Efficient Beehive Sensing - Kurin, Thomas; Horlbeck, Marie; Maiwald, Timo; Weigel, Robert; Lurz, Fabian (2023 IEEE Topical Conference on Wireless Sensors and Sensor Networks, WiSNeT 2023, 2023)
This paper discusses a novel ultra low power sensor system for monitoring a beehives' health condition, which can be determined by measuring internal temperature and humidity variations. It comprises an autonomous battery powered sensor node which transmits the measured data over a 868 MHz wireless radio link. Expected runtime is greater than 2.1 years.
CRIS-Team Scopus Importer:2023-03-17
Keywords: beehive; ultra low power; WSN

Deep Learning-based Person Detection on a Moving Robot - Gabsteiger, Jasmin; Maiwald, Timo; Kurin, Thomas; Dorn, Christian; Weigel, Robert; Lurz, Fabian (Radio and Wireless Week, 2024)

Evaluating Synthetic Data Potential for 60 GHz FMCW Radar Simulations with Real Measurements - Reitz, Philipp; Maiwald, Timo; Franchi, Norman; Weigel, Robert; Lübke, Maximilian (11th Microwave and Radar Week, 2024)
A fully integrated simulation environment for an FMCW radar system in an indoor environment operating at 60 GHz has been proposed. The simulation of the entire signal processing chain, starting with a CST simulation of the antenna pattern and extending to the radar signal processing, enabled the demonstration of the radar hardware’s and the channel’s inherent properties. Measurements and simulations have revealed the inherent fluctuation paths of a human target by showing a variation of 10 dB in the reflection power of the human and 0.5 m in the range profile in measurements. The papers results could demonstrate that synthetic datasets can be considered valid alternatives in radar detection. The channel properties could be examined by investigating different up-tilt configurations, and problems such as ground reflections caused by side lobes and multipath propagation within the human target could be further investigated.<br
Keywords: radar cross section; FMCW radar; simulation and measurement; mm-wave

Gesture Recognition to Control a Moving Robot With FMCW Radar - Maiwald, Timo; Gabsteiger, Jasmin; Weigel, Robert; Lurz, Fabian (2024 IEEE Radio and Wireless Symposium (RWS), 2024)
Gesture recognition with radar sensors experienced great scientific interest in recent years and may become an alternative or supplement technology to camera based computer vision. However, most publications focus on static radars, which is not applicable in case of moving robots, where the radar is attached to the robot itself. This paper presents a system for automated radar data acquisition and labeling in context of radar based gesture recognition, mounted on a moving robot. It is used to collect labeled radar data of 4 different gestures to control the robot. In addition, results of a comparably small CNN, which processes range-Doppler- and range-angle-images, achieving 86 % of evaluation accuracy is presented.<br
Keywords: gesture recognition; robot; radar; computer vision; machine learning

Nuclide Identification using CsI(Tl) Gamma Ray Spectra and Neural Networks - Maiwald, Timo; Leder, Erich; Pijahn, Ralf; Buchhold, Reinhard; Fischer, Georg (21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2023)
With increasing popularity of machine learning methods used in gamma ray spectroscopy for radioisotope identification, quality and scope of the data, to train and evaluate models, constitute the key towards applicable approaches. This paper demonstrates several steps needed to be considered in order to create a novel dataset, which is motivated by physical, technical and standardization requirements. Therefore offset shifts, gain drifts, variating acquisition times and nuclide mixtures are considered. Furthermore, initial classification results gained from a neural network approach are presented.<br
Keywords: nuclide identification; gamma ray spectra; machine learning; neural networks

Planar Coils for Detection of Magnetic Nanoparticles in a Testbed for Molecular Communication - Bartunik, Max; Faghih-Naini, Samira; Maiwald, Timo; Kirchner, Jens (Proceedings of the 9th ACM International Conference on Nanoscale Computing and Communication, NANOCOM 2022, 2022)
Testbeds enable experimental investigation of molecular communication concepts and transmission schemes. One approach to biocompatible molecular communication, implemented in a testbed, is the use of magnetic nanoparticles as information carriers. These nanoparticles can be detected based on their high susceptibility using an inductive sensor. So far, a wire-wound sensing coil, that requires manual manufacturing and is difficult to install for a real-life transmission channel, was used. Here, we propose the use of planar coils. To this end, a selection of commercially available coils are evaluated in comparison to a conventional wire-wound coil as reference. A planar coil is then successfully used to detect magnetic nanoparticles in the context of the molecular communication testbed. The contribution is concluded with an outlook on future improvements made possible through the application of planar sensing coils.
CRIS-Team Scopus Importer:2022-11-11
Keywords: Magnetic nanoparticles; Molecular communication; Testbed

Time Domain Integration in Range Doppler/Angle Images for Human Gesture Recognition with FMCW Radar - Maiwald, Timo; Schmidt, Philipp; Fischer, Georg; Lurz, Fabian (Asia-Pacific Microwave Conference (APMC), 2025)
This paper presents a radar data labeling method and discusses results obtained by training a CNN for human gesture recognition with FMCW radar. Superimposed range-Doppler and range-angle images are used to integrate time dependent information into single images, which reduces the amount of data the CNN has to deal with. Obtained results indicate 92% accuracy in terms of the automated label assignment. An average classification accuracy of 85% is achieved, whereas this value drops by 2% only if in addition the CNN also differentiates between the left and right hand resulting in 23 categorie
Keywords: radar; fmcw; human gesture recognition; machine learning

Timing Deviations in Wireless Networks using Bluetooth Periodic Advertising - Maiwald, Timo; Zhang, Qing; Bartunik, Max; Shi, Kilin; Lurz, Fabian; Fischer, Georg; Ackermann, Thomas (2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2022)
Monitoring and control systems, including spatially distributed devices, often require simultaneous responses to control commands. Especially when timing is crucial a synchronized connection between all devices is necessary to minimize differences in response time. This paper introduces a method which uses Bluetooth periodic advertising to establish a wireless synchronized multicast to simultaneously control devices. Results of time difference measurements between two devices, reacting on control commands received by the same periodic advertising packet, are introduced and discussed using large scale experimental data. Device configurations, where an impact on timing was experienced, are presented. Time difference values with a low maximum delay of 11 μs were achieve
Keywords: Periodic Advertising; Bluetooth; Wireless Synchronization