Reflectometry Signals Research Articles

Validation of short-pulse reflectometry turbulence measurements with a synthetic diagnostic

Experimental measurements of the turbulence amplitude utilizing a short pulse reflectometry method are presented. Two discharges with shaped plasma possessing opposite signs of triangularity are considered and a higher turbulence amplitude is found in the positive triangularity case. To confirm this result, a synthetic short pulse reflectometry diagnostic is developed. Local gyrokinetic modeling with the GENE code is carried out to produce turbulence relevant to the experimental conditions. This turbulence is then used as an input for the full-wave CUWA code to produce synthetic short pulse reflectometry signals. By matching synthetic and experimental reflectometry data, the difference between turbulence amplitudes in the two cases is confirmed. Additionally, the capability of the diagnostic to also measure the frequency spectrum of the turbulence is demonstrated.

De-noising of microwave reflectometry signal using maximal overlap discrete wavelet packet transform for plasma density measurement

For microwave reflectometry-based fusion plasma diagnostics, the acquired data is contaminated by the surrounding noise environment, making it difficult to measure the beat frequency accurately. Discrete wavelet and wavelet packet transforms are unsuitable as the former cannot decompose detailed coefficients, and the latter is a time-variant transform. This paper presents a correlation-based Maximal Overlap Discrete Wavelet Packet Transform (MODWPT), which decomposes the signal using different mother wavelet families. The suitable mother wavelet is chosen based on the metrics like signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean squared error (MSE), and mean absolute error (MAE). The data used for analysis is the stationary tokamak wall, for which the beat frequency and distance are measured. After rigorous analysis, it is found that sym12 outperforms with the following values 0.0493 (MSE), 0.1736 (MAE), 4.3334(SNR(dB)), 13.0634(PSNR(dB)) respectively. This technique can be extended to plasma scenarios for accurately measuring beat frequency and density profile.

CYGNSS Storm-Centric Tropical Cyclone Gridded Wind Speed Product

Abstract The Cyclone Global Navigation Satellite System (CYGNSS) mission has generated several new ocean surface data products. Because of its high overpass rate and ability to measure surface wind speeds through weather, these data products are typically higher temporal resolution than traditional satellite tropical cyclone (TC) data products. However, the nature of the Global Navigation Satellite Systems reflectometry (GNSS-R) signal, which is essentially a single pixelwide measurement along the CYGNSS specular point (SP) track on the ground, necessitates aggregating the wind speed data over a period of time to accurately characterize TCs in terms of intensity and structure. The standard CYGNSS level 3 (L3) wind products are averaged over 1 h, which typically is not sufficient to produce an accurate characterization. A new L3 storm-centric gridded (SCG) wind speed product is presented here. It has been developed to improve upon the standard L3 algorithm for the purpose of providing better characterization of TC wind fields over their storm life cycle. This paper describes the L3 SCG algorithm, provides examples of L3 SCG wind fields, and discusses the potential uses and limitations of the new data product.

Using Lidar Digital Elevation Models for Reflectometry Land Applications

Lidar elevation maps are utilized in a bistatic electromagnetic scattering model of the Cyclone Global Navigation Satellite System over San Luis Valley in Colorado, USA. The results are compared with results generated using the Shuttle Radar Topography Mission (SRTM) 1 arcsecond global digital elevation model (DEM). The high resolution lidar maps are also used to generate maps of surface roughness at length scales finer than the horizontal resolution of SRTM. Model results using these maps illustrate the importance of the spatial variation of surface roughness on reflectometry signals. Results also highlight the shortcomings of using SRTM in scattering models and show improvements when a lidar DEM is used.

A Hybrid Compact Polarimetry GNSS-R Analysis of the Earth’s Cryosphere

This manuscript provides the first Hybrid Compact Polarimetric (HCP) Global Navigation Satellite System – Reflectometry (GNSS-R) analysis of the cryosphere. By reconstructing the full-Stokes parameters and the child Stokes parameters from the reflectometry signals collected by the SMAP radar receiver, the sensitivity to the geophysical changes of the Earth’s landscapes of these opportunistic signals are analyzed. This analysis is focused over the Artic Sea ice formation zone, the northern latitudes presenting freeze and thawed transitions, and the Greenland ice sheet. We conduct a sensitivity analysis for each landscape. The signals analyzed over the Arctic Sea show correlation with sea ice concentration (SIC) of 0.77 for the second Stokes parameter <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and 0.7 for the child Stokes parameter χ, which describes the ellipticity of the wave. The signals analyzed over the northern latitudes present a sensitivity to the two states (frozen and thawed). The analysis over Alaskan landscape shows a total reflectivity Γ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> difference between freeze and thaw states of ~8 dB with a standard deviation of ~1 dB. Greenland presents a challenge since available datasets are not from the same timeframe, despite of this our initial evaluation shows that there is a ~0.5 correlation between ice thickness and the third Stokes parameter <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> and the relative phase δ between the two linear electromagnetic components, h-pol and v-pol. This manuscript provides an extensive view of the HCP GNSS-R signals over the cryosphere and finds initial sensitivities to the seasonal geophysical changes of the different areas.

Modeling and Simulation of Time Domain Reflectometry Signals on a Real Network for Use in Fault Classification and Location

Today, the classification and location of faults in electrical networks remains a topic of great interest. Faults are a major issue, mainly due to the time spent to detect, locate, and repair the cause of the fault. To reduce time and associated costs, automatic fault classification and location is gaining great interest. State-of-the-art techniques to classify and locate faults are mainly based on line-impedance measurements or the detection of the traveling wave produced by the event caused by the fault itself. In contrast, this paper describes the methodology for creating a database and a model for a complex distribution network. Both objectives are covered under the paradigm of the time-domain pulse reflectometry (TDR) principle. By using this technique, large distances can be monitored on a line with a single device. Thus, in this way a database is shared and created from the results of simulations of a real and complex distribution network modeled in the PSCAD™ software, which have been validated with measurements from an experimental test setup. Experimental validations have shown that the combination of the TDR technique with the modeling of a real network (including the real injector and the network coupling filter from the prototype) provides high-quality signals that are very similar and reliable to the real ones. In this sense, it is intended firstly that this model and its corresponding data will serve as a basis for further processing by any of the existing state-of-the-art techniques. And secondly, to become a valid alternative to the already well-known Test Feeders but adapted to work groups not used to the electrical world but to the environment of pure data processing.

Temporal convolution network with a dual attention mechanism for φ-OTDR event classification.

We propose a hybrid model named channel attention based temporal convolutional network combined with spatial attention and bidirectional long short-term memory network (ATCN-SA-BiLSTM) for phase sensitive optical time domain reflectometry signal recognition. This hybrid model consists of three parts: ATCN, which extracts temporal features and preserves causality of time domain signals, the SA mechanism, which re-weights spatial sequences for better feature extraction, and BiLSTM, which extracts spatial relationships considering the bidirectional propagation characteristics of disturbances in space domain signals. Experimental results show that our method achieves better classification performance with an accuracy of 93.4% and zero nuisance alarm rate.

Entropy-Based Coherence Metric for Land Applications of GNSS-R

A novel metric for detecting coherence in global navigation satellite system reflectometry (GNSS-R) signals is presented and evaluated. It applies the Von Neumann information entropy metric for density matrices, a powerful indicator of the degree of mixing between states, coherent and incoherent, of the scene under investigation. The metric is applied to a set of raw IF data acquired by the cyclone global navigation satellite system (CYGNSS) observatories over Lake Okeechobee FL, in order to test the sensitivity of the entropy to different land cover types, including wetlands and open water. Visual comparison of results with Sentinel-1 images provides a first step in the validation of the effectiveness of entropy in detecting the presence of water covered by emergent vegetation. In addition, the entropy-based metric could be implemented on future space-based GNSS-R receivers to adapt the incoherent integration times to the observed scene, thus achieving an improvement in along-track resolution.

Recovering complex reflection coefficient using the reference layer algorithm for multilayer systems with non-collinear magnetic ordering

This work analyzes two mathematical algorithms for processing experimental curves in polarized neutron reflectometry, one of which makes it possible to determine the complex reflection coefficient. The approbation was carried out for a Fe/Cr type superlattice with an irregular non-collinear ordering of the magnetic moments of the Fe layers. The processing of the experiment was carried out both by direct refinement of the structure parameters and by the method of calculating the module and phase of the reflectometry signal using the Gd reference layer. The results obtained with different methods are compared with each other. To clarify the structural and magnetic characteristics, the Levenberg-Marquardt algorithm was applied in both cases. The obtained data on the magnetic structure is in agreement with the theoretical model of magnetization of a layered antiferromagnet of finite dimensions in a weak field. The presented modification of the reference layer method can be considered as the phase problem solution in polarized neutron reflectometry.

Detection and Classification of Fault Types in Distribution Lines by Applying Contrastive Learning to GAN Encoded Time-Series of Pulse Reflectometry Signals

This study proposes a new method for detecting and classifying faults in distribution lines. The physical principle of classification is based on time-domain pulse reflectometry (TDR). These high-frequency pulses are injected into the line, propagate through all of its bifurcations, and are reflected back to the injection point. According to the impedances encountered along the way, these signals carry information regarding the state of the line. In the present work, an initial signal database was obtained using the TDR technique, simulating a real distribution line using (PSCAD™). By transforming these signals into images and reducing their dimensionality, these signals are processed using convolutional neural networks (CNN). In particular, in this study, contrastive learning in Siamese networks was used for the classification of different types of faults (ToF). In addition, to avoid the problem of overfitting owing to the scarcity of examples, generative adversarial neural networks (GAN) have been used to synthesise new examples, enlarging the initial database. The combination of Siamese neural networks and GAN allows the classification of this type of signal using only synthesised examples to train and validate and only the original examples to test the network. This solves the problem of the lack of original examples in this type of signal of natural phenomena which are difficult to obtain and simulate.

Slope-assisted Raman distributed optical fiber sensing

Raman distributed optical fiber sensing is required to achieve accurate temperature measurements in a micro-scale area. In this study, we first analyze and demonstrate the pulse transmission feature in the temperature variation area and the superposition characteristics of Raman optical time-domain reflectometry (OTDR) signals by numerical simulation. The equations of superimposed Raman anti-Stokes scattered signals at different stages are presented, providing a theoretical basis for the positioning and physical quantity demodulation of whole optical fiber systems based on the OTDR principle. Moreover, we propose and experimentally demonstrate a slope-assisted sensing principle and scheme in a Raman distributed optical fiber system. To the best our knowledge, this is the first experimental demonstration of Raman distributed optical fiber sensing in a centimeter-level spatial measurement region.

Detection and Characterization of Multiple Discontinuities in Cables with Time-Domain Reflectometry and Convolutional Neural Networks.

In this paper, a convolutional neural network for the detection and characterization of impedance discontinuity points in cables is presented. The neural network analyzes time-domain reflectometry signals and produces a set of estimated discontinuity points, each of them characterized by a class describing the type of discontinuity, a position, and a value quantifying the entity of the impedance discontinuity. The neural network was trained using a great number of simulated signals, obtained with a transmission line simulator. The transmission line model used in simulations was calibrated using data obtained from stepped-frequency waveform reflectometry measurements, following a novel procedure presented in the paper. After the training process, the neural network model was tested on both simulated signals and measured signals, and its detection and accuracy performances were assessed. In experimental tests, where the discontinuity points were capacitive faults, the proposed method was able to correctly identify 100% of the discontinuity points, and to estimate their position and entity with a root-mean-squared error of 13 cm and 14 pF, respectively.

Sensor Communication Implementation Using Multicarrier Reflectometry for Distributed Diagnosis in Complex Wired Networks

This article presents the integration of sensor communication using the diagnosis signal of wired networks. In order to cover the entire network, the diagnosis of branched networks requires the use of distributed diagnosis systems. The objective of this article is to propose a novel technique of ensuring efficient communication between distributed reflectometers (sensors) by using the transmitted part of the multicarrier time-domain reflectometry (MCTDR) signal. Our approach provides unambiguous fault location thanks to sensor data sharing and fusion, which improves the accuracy of fault location and the quality of the diagnosis. MCTDR has already been proven as an efficient method for online diagnosis by enabling precise bandwidth control while avoiding interferences. The main novelty of our technique is to inject an MCTDR signal carrying information which is capable of ensuring network diagnosis and reliable communication between several distributed sensors at the same time. This is done by exploiting, simultaneously, both the transmitted part and the reflected part of the MCTDR signal. The effectiveness of the proposed approach is evaluated by a series of simulations and experiments (based on field-programmable gate array implementation) on different types.

A State-Based Method to Simultaneously Reduce Cycle Slips and Noise in Coherent GNSS-R Phase Measurements From Open-Loop Tracking

The carrier phase measurements of coherent Global Navigation Satellite Systems Reflectometry (GNSS-R)signals have demonstrated the potential for high-accuracy altimetry applications. However, the coherent components in the reflected signal are often accompanied by noncoherent scattered signals, which may cause numerous cycle slips in the conventional open-loop (OL) tracking and postprocessing approach. These cycle slips can lead to meter-level errors in the altimetry retrieval. This article presents a simultaneous cycle slip and noise filtering (SCANF) method for dual-frequency GNSS-R phase measurements generated by OL tracking. The performance of this method is demonstrated using real GNSS-R phase measurements recorded by receivers on board Spire’s low-Earth orbit (LEO) satellites. The retrieved sea surface height anomaly (SSHA) has an average root mean square (rms) of 7.3 cm for sea and sea ice surface reflections relative to the mean sea surface (MSS). The technique presented in this article is also applicable to GNSS carrier phase measurements obtained under other challenging conditions, such as signals propagating through ionosphere plasma structures, radio occultation (RO) signals traversing lower troposphere, and multipath-rich environments.

Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information

The Cyclone Global Navigation Satellite System (CYGNSS) mission collects near-global hourly, pseudo-randomly distributed Global Navigation Satellite System - Reflectometry (GNSS-R) signals in the form of signal-to-noise ratio (SNR) point data, which is sensitive to the presence of surface water, due to their operating frequency at L-band. However, because of the pseudo-random nature of these points, it is not possible to obtain continuous flood inundation maps at adequately high resolution. By considering topological indicators, such as height above nearest drainage (HAND) and slope of nearest drainage (SND), which indicate the probability of a certain area being prone to flooding, we hypothesize that combining static topographic information with the dynamic GNSS-R signals can result in large-scale, high-resolution flood inundation maps. Flood mapping was performed and validated with flood extent derived using available Sentinel-1A synthetic aperture radar (SAR) data for flooding in Kerala during August 2018, and North India during August 2017. The results obtained after thresholding indicate that the model exhibits a flooding accuracy ranging from 60% to 80% for lower threshold values. We observed significant overestimation error in mapping inundation across the flooding period, resulting in an optimal critical success index of 0.22 for threshold values between 17–19.

Applicability and limitations of time domain reflectometry bridge scour monitoring system in general field conditions

A real-time and durable system for scour process monitoring with sufficient spatial precision is in pressing need for bridge safety management. In light of this, an innovative bundled time domain reflectometry sensing cable was recently proposed to enhance the time domain reflectometry technique for scour monitoring. However, current development only dealt with the construction of bundled sensing cable and the corresponding new data reduction method. Before it can be put into practical use, issues related to the effect of hydrological conditions, long-distance measurement, and actual field implementation are yet to be investigated. This study used both numerical simulations and laboratory experiments to examine the time domain reflectometry signals in response to both scour and deposition in different water-level conditions. As a result, the first guideline of waveform classification and interpretation is newly proposed to validly determine scour depth under various field conditions. Since field measurements often come with significant signal attenuation from resistance loss of long cable and dielectric and conductive loss in the sensing section, numerical simulations and a series of full-scale experiments were also conducted to assess the time domain reflectometry scour measurement range. The maximum measurement range of the latest time domain reflectometry scour sensing cable was found to be about 6 m. Within this range, the maximum error of scour estimation is within 0.2 m. Considering the new findings, a field time domain reflectometry scour monitoring system using the bundled time domain reflectometry sensing cable was designed accordingly and implemented at a bridge for the first time. The monitoring system successfully captured the scour process during a storm event and revealed some practical issues for future improvement as well.

Characterization and mitigation of interference between GNSS radio occultation and reflectometry signals for low‐altitude occultations

This research investigates the mutual interference between a GNSS signal refracted through the atmosphere and the same signal reflected from the surface below. For low-altitude occultations, the reflected signal can be present as multipath when tracking the direct signal. This poses a problem when making remote sensing measurements using GNSS-RO or GNSS-R techniques. Here, this issue is addressed in the context of GNSS data collected by a mountaintop receiver on the Hawaiian island of Maui. First, an elevation-angle threshold, below which the reflected signal can be present as multipath on the direct signal, is calculated for the mountaintop geometry. Next, the first steps are taken to mitigate the mutual interference between the direct and reflected signals. A frequency-domain filtering approach and a time-domain smoothing approach are applied to the signal intensity in an effort to separate the multipath contribution and the direct signal contribution.

Modernization of the filtration system for the Doppler reflectometry diagnostics of the L-2M stellarator for operation in regimes high-power ECR heating

The commissioning of the MIG-3 gyrotron complex at the L-2M stellarator has made it possible to reach a record power density input into plasma of 3.4 MW/m3 during the ECR heating of the plasma. This power input, however, resulted in noisy Doppler reflectometry signals (with a high-amplitude component at the gyrotron working frequency of 75.3 GHz), which could not be processed and analyzed. We modernized the filtering system of the Doppler reflectometry diagnostics, which had consisted of two pin-type band stop waveguide filters. Another pin-type filter was added to the filter system and a new compact bandstop filter was designed based on the Fabry-Perot resonator (BFFPR) to provide high suppression at the frequency of 75.3 GHz and low attenuation at the 30–40 GHz diagnostic frequency. We optimized the cavity length of the filter and calculated the characteristics of filters with different numbers of resonators by using numerical simulations. The filter layout was designed, and its characteristics were measured. The results of experimental measurements and numerical simulations have a good match. The redesigned filters were used to suppress the noise from the MIG-3 gyrotron complex.

High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks

This paper presents a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The goal of the proposed novel method is to advance CYGNSS-based SM estimations, exploiting the spatio-temporal resolution of the GNSS reflectometry (GNSS-R) signals to its highest potential within a machine learning framework. The methodology employs a fully connected Artificial Neural Network (ANN) regression model to perform SM predictions through learning the nonlinear relations of SM and other land geophysical parameters to the CYGNSS observables. In situ SM measurements from several International SM Network (ISMN) sites are used as reference labels; CYGNSS incidence angles, derived reflectivity and trailing edge slope (TES) values, as well as ancillary data, are exploited as input features for training and validation of the ANN model. In particular, the utilized ancillary data consist of normalized difference vegetation index (NDVI), vegetation water content (VWC), terrain elevation, terrain slope, and h-parameter (surface roughness). Land cover classification and inland water body masks are also used for the intermediate derivations and quality control purposes. The proposed algorithm assumes uniform SM over a 0.0833 ∘ × 0.0833 ∘ (approximately 9 km × 9 km around the equator) lat/lon grid for any CYGNSS observation that falls within this window. The proposed technique is capable of generating sub-daily and high-resolution SM predictions as it does not rely on time-series or spatial averaging of the CYGNSS observations. Once trained on the data from ISMN sites, the model is independent from other SM sources for retrieval. The estimation results obtained over unseen test data are promising: SM predictions with an unbiased root mean squared error of 0.0544 cm 3 /cm 3 and Pearson correlation coefficient of 0.9009 are reported for 2017 and 2018.

Intercomparison of Soil Moisture Retrieved from GNSS-R and from Passive L-Band Radiometry at the Valencia Anchor Station.

In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and a dual-pol down-looking antenna for receiving LHCP (left-hand circular polarization) and RHCP (right-hand circular polarization) reflected signals from the soil surface. Data were collected from the three different antennas through the two channels of Oceanpal GNSS-R receiver and, in addition, calibration was performed to reduce the impact from the differing channels. Reflectivity was thus measured, and soil moisture could be retrieved. The ESA (European Space Agency)-funded ELBARA-II (ESA L Band Radiometer II) is an L-band radiometer with two channels with 11 MHz bandwidth and respective center frequencies of 1407.5 MHz and 1419.5 MHz. The ELBARAII antenna is a large dual-mode Picket horn that is 1.4 m wide, with a length of 2.7 m with −3 dB full beam width of 12° (±6° around the antenna main direction) and a gain of 23.5 dB. By comparing GNSS-R and ELBARA-II radiometer data, a high correlation was found between the LHCP reflectivity measured by GNSS-R and the horizontal/vertical reflectivity from the radiometer (with correlation coefficients ranging from 0.83 to 0.91). Neural net fitting was used for GNSS-R soil moisture inversion, and the RMSE (Root Mean Square Error) was 0.014 m3/m3. The determination coefficient between the retrieved soil moisture and in situ measurements was R2 = 0.90 for Oceanpal and R2 = 0.65 for Elbara II, and the ubRMSE (Unbiased RMSE) were 0.0128 and 0.0734 respectively. The soil moisture retrievals by both L-band remote sensing methods show good agreement with each other, and their mutual correspondence with in-situ measurements and with rainfall was also good.