Radar signature generation for feature-aided tracking research

2005 ◽  
Author(s):  
Teri L. Piatt ◽  
John U. Sherwood ◽  
Stanton H. Musick
Keyword(s):  
Radar Signature ◽  
Signature Generation

scholarly journals Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3937
Author(s):  
Seungeon Song ◽  
Bongseok Kim ◽  
Sangdong Kim ◽  
Jonghun Lee
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Smart Home ◽  
Doppler Radar ◽  
Radar Images ◽  
Radar Signature ◽  
Memory Efficiency ◽  
High Compression ◽  
Value Decomposition ◽  
Deep Learning Model

Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.

The accuracy and predictability of micro Doppler radar signature projection algorithm measuring functional movement in NCAA athletes

2021 ◽  
Vol 85 ◽  
pp. 96-102
Author(s):  
Cayce Onks ◽  
Donald Hall ◽  
Tyler Ridder ◽  
Zacharie Idriss ◽  
Joseph Andrie ◽  
...  
Keyword(s):  
Doppler Radar ◽  
Projection Algorithm ◽  
Functional Movement ◽  
Radar Signature ◽  
Ncaa Athletes

scholarly journals MORSE: mesoscale ocean radar signature experiments

2002 ◽  
Author(s):  
L. Leviandier ◽  
W. Alpers ◽  
E. Barthelemy ◽  
P. Brandt ◽  
P. Brennan ◽  
...  
Keyword(s):  
Radar Signature ◽  
Ocean Radar

Modeling the Radar Signature of Raindrops in Aircraft Wake Vortices

2013 ◽  
Vol 30 (3) ◽  
pp. 470-484 ◽  
Author(s):  
Zhongxun Liu ◽  
Nicolas Jeannin ◽  
Francois Vincent ◽  
Xuesong Wang
Keyword(s):  
Traffic Control ◽  
Vortex Flow ◽  
Doppler Radar ◽  
Elevation Angle ◽  
Wake Vortex ◽  
Wake Vortices ◽  
Radar Signature ◽  
Radar Echo ◽  
Aircraft Wake ◽  
Resolution Cell

Abstract The present work is dedicated to the modeling and simulation of the radar signature of raindrops within wake vortices. This is achieved through the computation of the equation of raindrop motion within the wake vortex flow. Based on the inhomogeneous distribution of raindrops within wake vortices, the radar echo model is computed for raindrops in a given resolution cell. Simulated Doppler radar signatures of raindrops within wake vortices are shown to be a potential criterion for identifying wake vortex hazards in air traffic control. The dependence of the radar signature on various parameters, including the radial resolution and antenna elevation angle, is also analyzed.

Measurements and analysis of the radar signature of a new wind turbine design at X ‐band

2013 ◽  
Vol 7 (2) ◽  
pp. 170-177 ◽  
Author(s):  
Alessio Balleri ◽  
Allann Al‐Armaghany ◽  
Hugh Griffiths ◽  
Kinfai Tong ◽  
Takashi Matsuura ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Radar Signature ◽  
X Band ◽  
Turbine Design

Integrated Power Signature Generation Circuit for IoT Abnormality Detection

2022 ◽  
Vol 18 (1) ◽  
pp. 1-13
Author(s):  
David Thompson ◽  
Haibo Wang
Keyword(s):  
Cmos Technology ◽  
Wireless Transmission ◽  
Wireless Sensor ◽  
Voltage Regulators ◽  
Measurement Equipment ◽  
Data Sampling ◽  
Abnormality Detection ◽  
Operation Conditions ◽  
Signature Generation ◽  
Iot Devices

This work presents a methodology to monitor the power signature of IoT devices for detecting operation abnormality. It does not require bulky measurement equipment thanks to the proposed power signature generation circuit which can be integrated into LDO voltage regulators. The proposed circuit is implemented using a 130 nm CMOS technology and simulated with power trace measured from a wireless sensor. It shows the generated power signature accurately reflects the power consumption and can be used to distinguish different operation conditions, such as wireless transmission levels, data sampling rates and microcontroller UART communications.

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