Polarimetric Analysis of Radar Signature of a Manmade Structure

2006 ◽  
Author(s):  
J.-S. Lee ◽  
T. Ainsworth ◽  
E. Krogagor ◽  
W.-M. Boerner
Keyword(s):  
Radar Signature

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

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

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

Microphysical and Polarimetric Radar Modeling of Hydrometeor Refreezing

2021 ◽  
Author(s):  
Dana M. Tobin ◽  
Matthew R. Kumjian
Keyword(s):  
Polarimetric Radar ◽  
Horizontal Polarization ◽  
Radar Signature ◽  
Liquid Portion ◽  
Linear Depolarization Ratio ◽  
Bin Microphysics ◽  
Ice Shell ◽  
Differential Reflectivity ◽  
The Impact ◽  
Polar Correlation

AbstractA unique polarimetric radar signature indicative of hydrometeor refreezing during ice pellet events has been documented in several recent studies, yet the underlying microphysical causes remain unknown. The signature is characterized by enhancements in differential reflectivity (ZDR), specific differential phase (KDP), and linear depolarization ratio (LDR), and a reduction in co-polar correlation coefficient (ρhv) within a layer of decreasing radar reflectivity factor at horizontal polarization (ZH). In previous studies, the leading hypothesis for the observed radar signature is the preferential refreezing of small drops. Here, a simplified, one-dimensional, explicit bin microphysics model is developed to simulate the refreezing of fully melted hydrometeors, and coupled with a polarimetric radar forward operator to quantify the impact of preferential refreezing on simulated radar signatures. The modeling results demonstrate that preferential refreezing is insufficient by itself to produce the observed signatures. In contrast, simulations considering an ice shell growing asymmetrically around a freezing particle (i.e., emulating a thicker ice shell on the bottom of a falling particle) produce realistic ZDR enhancements, and also closely replicate observed features in ZH, KDP, LDR, and ρhv. Simulations that assume no increase in particle wobbling with freezing produce an even greater ZDR enhancement, but this comes at the expense of reducing the LDR enhancement. It is suggested that the polarimetric refreezing signature is instead strongly related to both the distribution of the unfrozen liquid portion within a freezing particle, and the orientation of this liquid with respect to the horizontal.

Sign in / Sign up

Export Citation Format

Share Document