A compact ultra-wideband mmWave radar sensor at 80 GHz based on a SiGe transceiver chip (Focused session on highly-integrated millimeter-wave radar sensors in SiGe BiCMOS technologies)

The importance of human-robot collaboration systems in industrial applications has increased due to the orientation towards the smart factory approach. To ensure the safety of such systems, collision avoidance techniques based on millimeter-wave radar sensors are used. Safe communication between the robot and the radar sensor is ensured by using the safety system on a chip based on 1oo2D architecture. The design and prototyping of a multi-channel communication FPGA-based development board is presented. Furthermore, the FPGA implementation of SoC with multiple Ethernet MAC interfaces based on various soft cores is demonstrated.

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Millimeter-Wave Radar Sensor for Automated Tomographic Imaging of Composite Materials in a Manufacturing Environment

IEEE Sensors Letters ◽

10.1109/lsens.2021.3055060 ◽

2021 ◽

Vol 5 (3) ◽

pp. 1-4

Author(s):

Dominik Meier ◽

Christian Zech ◽

Benjamin Baumann ◽

Bersant Gashi ◽

Matthias Malzacher ◽

...

Keyword(s):

Composite Materials ◽

Millimeter Wave ◽

Tomographic Imaging ◽

Radar Sensor ◽

Manufacturing Environment ◽

Millimeter Wave Radar ◽

Wave Radar

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Virtual Radar: Real-Time Millimeter-Wave Radar Sensor Simulation for Perception-driven Robotics

IEEE Robotics and Automation Letters ◽

10.1109/lra.2021.3068916 ◽

2021 ◽

pp. 1-1

Author(s):

Christian Schoffmann ◽

Barnaba Ubezio ◽

Christoph Boehm ◽

Stephan Muhlbacher-Karrer ◽

Hubert Zangl

Keyword(s):

Real Time ◽

Millimeter Wave ◽

Radar Sensor ◽

Millimeter Wave Radar ◽

Wave Radar

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Simultaneous Target Classification and Moving Direction Estimation in Millimeter-Wave Radar System

Sensors ◽

10.3390/s21155228 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5228

Author(s):

Jin-Cheol Kim ◽

Hwi-Gu Jeong ◽

Seongwook Lee

Keyword(s):

Millimeter Wave ◽

Radar System ◽

Sensor Data ◽

Test Field ◽

Target Classification ◽

Radar Sensor ◽

Millimeter Wave Radar ◽

Direction Estimation ◽

Wave Radar ◽

Moving Direction

In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data for a pedestrian, a cyclist, and a car are obtained in the test field. Then, a You Only Look Once (YOLO)-based network is trained with the sensor data to perform simultaneous target classification and moving direction estimation. To generate input data suitable for the deep learning-based classifier, a method of converting the radar detection result into an image form is also proposed. With the proposed method, we can identify the type of each target and its direction of movement with an accuracy of over 95%. Moreover, the pre-trained classifier shows an identification accuracy of 85% even for newly acquired data that have not been used for training.

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