Target Classification and Identification Using Sparse Model Representations of Frequency-Domain Electromagnetic Induction Sensor Data

2013 ◽  
Vol 51 (5) ◽  
pp. 2689-2706 ◽  
Author(s):  
Stacy L. Tantum ◽  
Waymond R. Scott ◽  
Kenneth D. Morton ◽  
Leslie M. Collins ◽  
Peter A. Torrione
Keyword(s):  
Frequency Domain ◽  
Electromagnetic Induction ◽  
Sensor Data ◽  
Target Classification ◽  
Sparse Model ◽  
Induction Sensor

Portable Magnetic/Frequency Domain Electromagnetic Induction Sensor System Development

2008 ◽  
Vol 13 (3) ◽  
pp. 237-245
Author(s):  
D. Wright ◽  
H. H. Bennett ◽  
J. H. Ballard ◽  
M. P. Fields ◽  
T. A. DeMoss ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Electromagnetic Induction ◽  
System Development ◽  
Sensor System ◽  
Induction Sensor

scholarly journals Electromagnetic Induction Sensor Data to Identify Areas of Manure Accumulation on a Feedlot Surface

2009 ◽  
Vol 73 (6) ◽  
pp. 2068-2077 ◽  
Author(s):  
Bryan L. Woodbury ◽  
Scott M. Lesch ◽  
Roger A. Eigenberg ◽  
Daniel N. Miller ◽  
Mindy J. Spiehs
Keyword(s):  
Electromagnetic Induction ◽  
Sensor Data ◽  
Induction Sensor

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.

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