Face identification using millimetre-wave radar sensor data

2020 ◽  
Vol 56 (20) ◽  
pp. 1077-1079
Author(s):  
J. Kim ◽  
J.-E. Lee ◽  
H.-S. Lim ◽  
S. Lee
Keyword(s):  
Sensor Data ◽  
Face Identification ◽  
Radar Sensor ◽  
Millimetre Wave ◽  
Wave Radar

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

Sensors ◽  
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.

scholarly journals Identification of Human Motion Using Radar Sensor in an Indoor Environment

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2305
Author(s):  
Sung-wook Kang ◽  
Min-ho Jang ◽  
Seongwook Lee
Keyword(s):  
Signal Processing ◽  
Indoor Environment ◽  
Human Motion ◽  
Sensor Data ◽  
Statistical Characteristics ◽  
Radar Signal ◽  
Radar Sensor ◽  
Wave Radar ◽  
The Moment ◽  
Human Motions

In this paper, we propose a method of identifying human motions, such as standing, walking, running, and crawling, using a millimeter wave radar sensor. In our method, two signal processing is performed in parallel to identify the human motions. First, the moment at which a person’s motion changes is determined based on the statistical characteristics of the radar signal. Second, a deep learning-based classification algorithm is applied to determine what actions a person is taking. In each of the two signal processing, radar spectrograms containing the characteristics of the distance change over time are used as input. Finally, we evaluate the performance of the proposed method with radar sensor data acquired in an indoor environment. The proposed method can find the moment when the motion changes with an error rate of 3%, and also can classify the action that a person is taking with more than 95% accuracy.

scholarly journals One‐shot learning‐based driver's head movement identification using a millimetre‐wave radar sensor

2022 ◽  
Author(s):  
Hong Nhung Nguyen ◽  
Seongwook Lee ◽  
Tien‐Tung Nguyen ◽  
Yong‐Hwa Kim
Keyword(s):  
Head Movement ◽  
Radar Sensor ◽  
Millimetre Wave ◽  
Wave Radar

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

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

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

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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