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

INVESTIGATION OF ACCURACY OF OCEAN RADAR CURRNETS UPDATED SINCE 2016 IN THE ISE BAY

2020 ◽  
Vol 76 (2) ◽  
pp. I_1369-I_1374
Author(s):  
Takaki TSUBONO ◽  
Kazuhiro MISUMI ◽  
Daisuke TSUMUNE
Keyword(s):  
Ise Bay ◽  
Ocean Radar

FUNDAMENTAL STUDY ON ASSIMILATION OF TSUNAMI CURRENTS OBSERVED BY HF OCEAN RADAR

2019 ◽  
Vol 75 (2) ◽  
pp. I_1315-I_1320
Author(s):  
Masayasu IRIE ◽  
Pao KUSAKABE ◽  
Satoshi YAMANISHI ◽  
Hirofumi HINATA
Keyword(s):  
Fundamental Study ◽  
Ocean Radar ◽  
Tsunami Currents

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

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
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