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

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

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 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

scholarly journals Stationary Time Statistical Property of Ionospheric Clutter in High-Frequency Surface-Wave Radar

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Shang Shang ◽  
Kangning He ◽  
Zhaobin Wang ◽  
Xuguang Yang
Keyword(s):  
Surface Wave ◽  
High Frequency ◽  
Real Data ◽  
Radar System ◽  
Detection Performance ◽  
Frequency Selection ◽  
Statistical Property ◽  
Stationary Time ◽  
Wave Radar

In HFSWR (high-frequency surface-wave radar) system, the detection performance is impacted seriously by ionospheric clutter. Frequency selection is an effective method to avoid the effect of ionospheric clutter. The key to the method is the stationarity of ionospheric clutter over a period of time. This paper mainly researches the stationary time statistical property of the ionospheric clutter. A large number of real data including ionospheric clutter in HFSWR are processed and analyzed. It shows that ionospheric clutter in HFSWR has the characteristics of approximate stationarity within a period of time.

24 GHz FMCW Radar System for Real-Time Hand Gesture Recognition Using LSTM

2018 ◽  
Author(s):  
Jun Seuk Suh ◽  
Siiung Ryu ◽  
Bvunghun Han ◽  
Jaewoo Choi ◽  
Jong-Hwan Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Radar System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Fmcw Radar ◽  
24 Ghz

scholarly journals A dataset of clinically recorded radar vital signs with synchronised reference sensor signals

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Sven Schellenberger ◽  
Kilin Shi ◽  
Tobias Steigleder ◽  
Anke Malessa ◽  
Fabian Michler ◽  
...  
Keyword(s):  
Continuous Monitoring ◽  
Continuous Wave ◽  
Vital Signs ◽  
Radar System ◽  
Tilt Table ◽  
Non Invasive ◽  
Impedance Cardiogram ◽  
Wave Radar ◽  
Reference Device ◽  
Sensor Signals

Abstract Using Radar it is possible to measure vital signs through clothing or a mattress from the distance. This allows for a very comfortable way of continuous monitoring in hospitals or home environments. The dataset presented in this article consists of 24 h of synchronised data from a radar and a reference device. The implemented continuous wave radar system is based on the Six-Port technology and operates at 24 GHz in the ISM band. The reference device simultaneously measures electrocardiogram, impedance cardiogram and non-invasive continuous blood pressure. 30 healthy subjects were measured by physicians according to a predefined protocol. The radar was focused on the chest while the subjects were lying on a tilt table wired to the reference monitoring device. In this manner five scenarios were conducted, the majority of them aimed to trigger hemodynamics and the autonomic nervous system of the subjects. Using the database, algorithms for respiratory or cardiovascular analysis can be developed and a better understanding of the characteristics of the radar-recorded vital signs can be gained.

Sign in / Sign up

Export Citation Format

Share Document