Ultrawideband radar images of the surface disturbance produced by a submerged, mine-like object

2000 ◽  
Vol 38 (6) ◽  
pp. 2506-2514
Author(s):  
M.A. Sletten
Keyword(s):  
Radar Images ◽  
Surface Disturbance ◽  
Ultrawideband Radar

Classification of suspicious regions within ultrawideband radar images of the breast

2008 ◽  
Author(s):  
R.C. Conceicao ◽  
D. Byrne ◽  
M. O'Halloran ◽  
M. Glavin ◽  
E. Jones
Keyword(s):  
Radar Images ◽  
Ultrawideband Radar

Methods for Blind Evaluation of Noise Variance in Multichannel Optical and Radar Images

2006 ◽  
Vol 65 (6) ◽  
pp. 527-556 ◽  
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
N. N. Ponomarenko ◽  
Benoit Vozel ◽  
Kacem Chehdi
Keyword(s):  
Noise Variance ◽  
Radar Images

Study of the route correction system for the on-board navigation system of an unmanned aerial vehicle on the grounds of radar images of the terrain

2020 ◽  
pp. 129-134
Keyword(s):  
Predictive Model ◽  
Unmanned Aerial Vehicle ◽  
Predictive Models ◽  
Navigation System ◽  
Error Compensation ◽  
Compensation Scheme ◽  
Radar Images ◽  
Aerial Vehicle ◽  
System Errors ◽  
Correction System

The system of route correction of an unmanned aerial vehicle (UAV) is considered. For the route correction the on-board radar complex is used. In conditions of active interference, it is impossible to use radar images for the route correction so it is proposed to use the on-board navigation system with algorithmic correction. An error compensation scheme of the navigation system in the output signal using the algorithm for constructing a predictive model of the system errors is applied. The predictive model is building using the genetic algorithm and the method of group accounting of arguments. The quality comparison of the algorithms for constructing predictive models is carried out using mathematical modeling.

The research study in the convergence of the results of processing radar images Sentinel-1A with field observations of agricultural sites

2019 ◽  
Vol 950 (8) ◽  
pp. 52-58
Author(s):  
D.V. Mozer ◽  
Е.L. Levin ◽  
A.K. Satbergenova
Keyword(s):  
Satellite Remote Sensing ◽  
Satellite Images ◽  
Research Study ◽  
High Accuracy ◽  
Agricultural Fields ◽  
Field Observations ◽  
Radar Images ◽  
The Earth ◽  
Radar Satellite ◽  
Modern Technologies

The manuscript discusses how to monitor the condition of seedlings on agricultural fields planted with winter wheat, fodder maize and areas of fir forest located in the Freudenstadt district of Baden-Wuerttemberg in Germany. To solve the range of agricultural problems , they often use modern technologies such as satellite remote sensing of the Earth. The paper displays the monitoring results of the Sentinel-1A radar satellites scenes, as well as visual spectrum imagery of field observations are presented when leaving directly to terrain segments. The processing deployed data chain, consisting of 11 Sentinel-1A scenes acquired in the timefrane from March to November 2018. Specifically, the SNAP Sentinel Toolboxes software was used to process the radar satellite images Sentinel-1А, the. Based on the the research outcomes the Committee of Agriculture of the Freudenstadt district is able to predict the yield amount with high accuracy due to good data convergence. According to the study, the following three important problems can be resolved by means of Sentinel-1A imagery

Progressive neighbors pursuit for radar images classification

2021 ◽  
pp. 107194
Author(s):  
Shuyuan Yang ◽  
Guangying Xu ◽  
Huixiao Meng ◽  
Min Wang
Keyword(s):  
Radar Images

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.

Ultrawideband adaptive transmitter for ultrawideband radar feasibility demonstration

1992 ◽  
Author(s):  
Raymond Tang ◽  
Kuan M. Lee ◽  
Vincent P. Pusateri
Keyword(s):  
Ultrawideband Radar
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