Physics-based data augmentation for high frequency 3D radar systems

2018 ◽  
Author(s):  
Kenneth D. Morton ◽  
Miles Crosskey ◽  
Patrick Wang ◽  
Rayn Sakaguchi
Keyword(s):  
High Frequency ◽  
Data Augmentation ◽  
Radar Systems

scholarly journals Experimentelles FMCW-Radar zur hochfrequenten Charakterisierung von Windenergieanlagen

2017 ◽  
Vol 15 ◽  
pp. 1-9
Author(s):  
Karsten Schubert ◽  
Jens Werner ◽  
Fabian Schwartau
Keyword(s):  
Renewable Energy ◽  
High Frequency ◽  
Renewable Energy Sources ◽  
Cost Effective ◽  
Energy Sources ◽  
Interference Effects ◽  
Fmcw Radar ◽  
Weather Radars ◽  
Radar Systems ◽  
Radar Echoes

Abstract. During the increasing dissemination of renewable energy sources the potential and actual interference effects of wind turbine plants became obvious. Turbines reflect the signals of weather radar and other radar systems. In addition to the static radar echoes, in particular the Doppler echoes are to be mentioned as an undesirable impairment Keränen (2014). As a result, building permit is refused for numerous new wind turbines, as the potential interference can not be reliably predicted. As a contribution to the improvement of this predictability, measurements are planned which aim at the high-frequency characterisation of wind energy installations. In this paper, a cost-effective FMCW radar is presented, which is operated in the same frequency band (C-band) as the weather radars of the German weather service. Here, the focus is on the description of the hardware design including the considerations used for its dimensioning.

scholarly journals Growing Network of Radar Systems Monitors Ocean Surface Currents

Eos ◽  
2016 ◽  
Vol 97 ◽  
Author(s):  
Hugh Roarty ◽  
Lisa Hazard ◽  
Enrique Fanjul
Keyword(s):  
High Frequency ◽  
Ocean Surface ◽  
Surface Currents ◽  
High Frequency Radar ◽  
Radar Systems ◽  
Radar Network ◽  
Ocean Surface Currents ◽  
Growing Network

Fourth Meeting of the Global High Frequency Radar Network; Heraklion, Crete, Greece, 22–23 September 2015

scholarly journals Interoperability of Direction-Finding and Beam-Forming High-Frequency Radar Systems: An Example from the Australian High-Frequency Ocean Radar Network

2019 ◽  
Vol 11 (3) ◽  
pp. 291 ◽  
Author(s):  
Simone Cosoli ◽  
Stuart de Vos
Keyword(s):  
High Frequency ◽  
Direction Finding ◽  
Coastal Monitoring ◽  
Vector Correlation ◽  
High Frequency Radar ◽  
Pointing Errors ◽  
Radar Systems ◽  
Radar Network ◽  
Vector Correlations ◽  
Ocean Radar

Direction-finding SeaSonde (4.463 MHz; 5.2625 MHz) and phased-array WEllen RAdar WERA (9.33 MHz; 13.5 MHz) High-frequency radar (HFR) systems are routinely operated in Australia for scientific research, operational modeling, coastal monitoring, fisheries, and other applications. Coverage of WERA and SeaSonde HFRs in Western Australia overlap. Comparisons with subsurface currents show that both HFR types agree well with current meter records. Correlation (R), root-mean-squares differences (RMSDs), and mean bias (bias) for hourly-averaged radial currents range between R = (−0.03, 0.78), RMSD = (9.2, 30.3) cm/s, and bias = (−5.2, 5.2) cm/s for WERAs; and R = (0.1, 0.76), RMSD = (17.4, 33.6) cm/s, bias = (0.03, 0.36) cm/s for SeaSonde HFRs. Pointing errors (θ) are in the range θ = (1°, 21°) for SeaSonde HFRs, and θ = (3°, 8°) for WERA HFRs. For WERA HFR current components, comparison metrics are RU = (−0.12, 0.86), RMSDU = (12.3, 15.7) cm/s, biasU = (−5.1, −0.5) cm/s; and, RV = (0.61, 0.86), RMSDV = (15.4, 21.1) cm/s, and biasV = (−0.5, 9.6) cm/s for the zonal (u) and the meridional (v) components. Magnitude and phase angle for the vector correlation are ρ = (0.58, 0.86), φ = (−10°, 28°). Good match was found in a direct comparison of SeaSonde and WERA HFR currents in their overlap (ρ = (0.19, 0.59), φ = (−4°, +54°)). Comparison metrics at the mooring slightly decrease when SeaSonde HFR radials are combined with WERA HFR: scalar (vector) correlations for RU, V, (ρ) are in the range RU = (−0.20, 0.83), RV = (0.39, 0.79), ρ = (0.47, 0.72). When directly compared over the same grid, however, vectors from WERA HFR radials and vectors from merged SeaSonde–WERA show RU (RV) exceeding 0.9 (0.7) within the HFR grid. Despite the intrinsic differences between the two types of radars used here, findings show that different HFR genres can be successfully merged, thus increasing current mapping capability of the existing HFR networks, and minimising operational downtime, however at a likely cost of slightly decreased data quality.

scholarly journals Evaluation of Attenuation Correction Methodology for Dual-Polarization Radars: Application to X-Band Systems

2005 ◽  
Vol 22 (8) ◽  
pp. 1195-1206 ◽  
Author(s):  
Eugenio Gorgucci ◽  
V. Chandrasekar
Keyword(s):  
Attenuation Correction ◽  
High Frequency ◽  
Lower Cost ◽  
Dual Polarization ◽  
Phase Measurements ◽  
Differential Phase ◽  
High Frequency Radar ◽  
Radar Systems ◽  
X Band ◽  
The Impact

Abstract Monitoring of precipitation using high-frequency radar systems, such as the X band, is becoming increasingly popular because of their lower cost compared to their S-band counterpart. However, at higher frequencies, such as the X band, the precipitation-induced attenuation is significant, and introduces ambiguities in the interpretation of the radar observations. Differential phase measurements have been shown to be very useful for correcting the measured reflectivity for precipitation-induced attenuation. This paper presents a quantitative evaluation of two attenuation correction methodologies with specific emphasis on the X band. A simple differential phase–based algorithm as well as the range-profiling algorithm are studied. The impact of backscatter differential phase on the performance of attenuation correction is evaluated. It is shown that both of the algorithms for attenuation correction work fairly well, yielding attenuation-accurate corrected reflectivities with a negligible bias.

scholarly journals Deep Learning-Based High-Frequency Ultrasound Skin Image Classification with Multicriteria Model Evaluation

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5846
Author(s):  
Joanna Czajkowska ◽  
Pawel Badura ◽  
Szymon Korzekwa ◽  
Anna Płatkowska-Szczerek ◽  
Monika Słowińska
Keyword(s):  
Image Classification ◽  
Model Evaluation ◽  
High Frequency ◽  
Data Augmentation ◽  
Region Of Interest ◽  
Skin Layer ◽  
High Frequency Ultrasound ◽  
Deep Model ◽  
Multicriteria Model ◽  
Frequency Ultrasound

This study presents the first application of convolutional neural networks to high-frequency ultrasound skin image classification. This type of imaging opens up new opportunities in dermatology, showing inflammatory diseases such as atopic dermatitis, psoriasis, or skin lesions. We collected a database of 631 images with healthy skin and different skin pathologies to train and assess all stages of the methodology. The proposed framework starts with the segmentation of the epidermal layer using a DeepLab v3+ model with a pre-trained Xception backbone. We employ transfer learning to train the segmentation model for two purposes: to extract the region of interest for classification and to prepare the skin layer map for classification confidence estimation. For classification, we train five models in different input data modes and data augmentation setups. We also introduce a classification confidence level to evaluate the deep model’s reliability. The measure combines our skin layer map with the heatmap produced by the Grad-CAM technique designed to indicate image regions used by the deep model to make a classification decision. Moreover, we propose a multicriteria model evaluation measure to select the optimal model in terms of classification accuracy, confidence, and test dataset size. The experiments described in the paper show that the DenseNet-201 model fed with the extracted region of interest produces the most reliable and accurate results.

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