Short-Range Millimetric-Wave Radar System for Occupancy Sensing Application

2018 ◽  
Vol 2 (3) ◽  
pp. 1-4 ◽  
Author(s):  
Avik Santra ◽  
Raghavendran Vagarappan Ulaganathan ◽  
Thomas Finke
Keyword(s):  
Short Range ◽  
Millimetric Wave ◽  
Radar System ◽  
Wave Radar ◽  
Sensing Application

Robust Gesture Recognition Using Millimetric-Wave Radar System

2018 ◽  
Vol 2 (4) ◽  
pp. 1-4 ◽  
Author(s):  
Souvik Hazra ◽  
Avik Santra
Keyword(s):  
Gesture Recognition ◽  
Millimetric Wave ◽  
Radar System ◽  
Wave Radar

Separation of Incident and Reflected Waves by Means of a Wave Radar System

2021 ◽  
pp. 1-5
Author(s):  
Francesco Serafino ◽  
Simone Bonamano ◽  
Francesco Paladini de Mendoza ◽  
Marco Marcelli
Keyword(s):  
Radar System ◽  
Reflected Waves ◽  
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 A Doppler Range Compensation for Step-Frequency Continuous-Wave Radar for Detecting Small UAV

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1331
Author(s):  
Massimiliano Pieraccini ◽  
Lapo Miccinesi ◽  
Neda Rojhani
Keyword(s):  
Short Range ◽  
Continuous Wave ◽  
Range Shift ◽  
Specific Method ◽  
Step Frequency ◽  
Wave Radar ◽  
Small Uav ◽  
The Doppler Effect ◽  
Set Up ◽  
Free Falling

Step-frequency continuous-wave (SFCW) modulation can have a role in the detection of small unmanned aerial vehicles (UAV) at short range (less than 1–2 km). In this paper, the theory of SFCW range detection is reviewed, and a specific method for correcting the possible range shift due to the Doppler effect is devised. The proposed method was tested in a controlled experimental set-up, where a free-falling target (i.e., a corner reflector) was correctly detected by an SFCW radar. This method was finally applied in field for short-range detection of a small UAV.

Three dimensional imaging laser radar system for short-range target detection and identification

2008 ◽  
Author(s):  
Ping Li ◽  
Kun Li ◽  
Huimin Chen ◽  
Weirong Gou
Keyword(s):  
Target Detection ◽  
Short Range ◽  
Three Dimensional ◽  
Radar System ◽  
Laser Radar ◽  
Three Dimensional Imaging ◽  
Detection And Identification

Short-Range Tracking of Moving Targets by a Handheld UWB Radar System

2012 ◽  
pp. 207-225 ◽  
Author(s):  
Dušan Kocur ◽  
Jana Rovňáková
Keyword(s):  
Short Range ◽  
Radar System ◽  
Moving Targets ◽  
Uwb Radar

scholarly journals Small displacement detecting method based on multifrequency continuous wave radar system

2019 ◽  
Vol 1195 ◽  
pp. 012017 ◽  
Author(s):  
Aloysius Adya Pramudita ◽  
Dharu Arseno ◽  
Erfansyah Ali
Keyword(s):  
Continuous Wave ◽  
Radar System ◽  
Small Displacement ◽  
Wave Radar ◽  
Detecting Method
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