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The ionospheric effect on radio wave propagation error has a significant impact on navigation, positioning, and real-time measurements. It is the primary source of errors in high precision aviation and measurement systems. A set of eight coefficients is used to correct ionospheric errors for Indian Regional Navigation Satellite System (IRNSS) users. Currently, the Klobuchar model is an effective low computational mathematical model for ionospheric range error mitigation through a set of eight coefficients. It has low accuracy in Equatorial Ionization Anomaly (EIA) regions and can correct up to ∼60% of the ionospheric range error. This paper proposes an ionospheric error correction model that can be applied to such measurements as well as an associated error model to compute accuracy with a differential interval of observations 5 m, 30 m and 60 m. The numerical simulation results show that compared to widely used algorithms, the proposed strategy can enhance the 83.13% ionospheric correction with Klobuchar- like coefficients at a sampling rate of 5 m. The proposed solution could be effective for Regional Navigational Satellite Systems (RNSS) for ionospheric delay corrections in low-latitude regions. [ABSTRACT FROM AUTHOR]

Abstract In the last 20 years, and in particular in the last decade, the availability of propagation data for GNSS has increased substantially. In this sense, the ionosphere has been sounded with a large number of receivers that provide an enormous amount of ionospheric data. Moreover, the maturity of the models has also been increased in the same period of time. As an example, IGS has ionospheric maps from GNSS data back to 1998, which would allow for the correlation of these data with other quantities relevant for the user and space weather (such as Solar Flux and Kp). These large datasets would account for almost half a billion points to be analyzed. With the advent and explosion of Big Data algorithms to analyze large databases and find correlations with different kinds of data, and the availability of open source code libraries (for example, the TensorFlow libraries from Google that are used in this paper), the possibility of merging these two worlds has been widely opened. In this paper, a proof of concept for a single frequency correction algorithm based in GNSS GIM vTEC and Fully Connected Neural Networks is provided. Different Neural Network architectures have been tested, including shallow (one hidden layer) and deep (up to five hidden layers) Neural Network models. The error in training data of such models ranges from 50% to 1% depending on the architecture used. Moreover, it is shown that by adjusting a Neural Network with data from 2005 to 2009 but tested with data from 2016 to 2017, Neural Network models could be suitable for the forecast of vTEC for single frequency users. The results indicate that this kind of model can be used in combination with the Galileo Signal-in-Space (SiS) NeQuick G parameters. This combination provides a broadcast model with equivalent performances to NeQuick G and better than GPS ICA for the years 2016 and 2017, showing a 3D position Root Mean Squared (RMS) error of approximately 2 m. [ABSTRACT FROM AUTHOR]

Ionospheric disturbances are the phenomena which adversely affect the performance of precise positioning. This holds true even for multi-constellation relative positioning supported with network-derived ionospheric corrections. In such scenario the unfavorable effect is caused by a poor accuracy of corrections, which, in turn, is driven by the deterioration of the spatial interpolation process. The positioning becomes even more challenging in a wide-area scenario with baselines over 100 km. In this paper, we assess the methodology which aims at reliable and accurate wide-area RTK and rapid static positioning in the presence of severe ionospheric conditions. The approach takes advantage of multi-constellation network ionospheric corrections and an algorithm which allows the elimination of the temporal variations of the ionospheric delay. The experimental evaluation was performed on the basis of multi-station RTK and static positioning using GPS, BDS and Galileo data collected at high latitudes during the ionospheric storm on August 25–26, 2018. The results confirmed the deterioration of the accuracy of the network ionospheric corrections and consequently a decline in the positioning performance with routine models such as ionosphere-float and ionosphere-weighted. On the other hand, the results obtained with the application of the developed methodology demonstrated a very distinctive improvement in the ambiguity resolution domain and thus proved the advantage over benchmark models. In this case, the developed methodology allowed up to 20% enhancement of the ambiguity success rate with respect to benchmark strategies. [ABSTRACT FROM AUTHOR]

The Program of the Antarctic Syowa Mesosphere–Stratosphere–Troposphere/Incoherent Scatter (PANSY) radar is a large atmospheric radar located at the Antarctic Syowa Station (69.01°S, 39.59°E). The PANSY radar performed the first incoherent scatter (IS) measurements in the Antarctic region in 2015. Several specific observations were undertaken in 2017 including a 24-h observation of the ionosphere using a peripheral antenna array to suppress interference from the field-aligned irregularities (FAIs). This paper presents the preliminary results derived from the IS measurements using the PANSY radar and the adaptive signal processing techniques to suppress FAIs. The norm-constrained and directionally constrained minimization of power (NC-DCMP) algorithm was applied to the 24-h ionosphere observations by the PANSY radar with a weighting applied to the directional constraint based on the gain differences of the subarrays. When compared with the conventional nonadaptive approach, the number of usable power profiles was increased by about 24% by the gain-weighted NC-DCMP algorithm, suggesting its effectiveness for FAI clutter suppression in ionosphere observations. Furthermore, detection of FAIs using the dedicated antenna array was found valuable in assessing the reliability of estimations of electron density based on VHF-band IS radar data. [ABSTRACT FROM AUTHOR]

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of “good” features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms. [ABSTRACT FROM AUTHOR]

Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI. [ABSTRACT FROM AUTHOR]

The HF propagation model, North Ionospheric Model and Ray Tracing (NIM-RT) was developed and tested for a number of years by comparing measured vertical and oblique ionograms over a number of radio links (especially in high latitude area) with the simulated ionograms. The present paper extends the model in order to include: (a) Implementation of the data retrieved from the International Reference Ionosphere (IRI-2012) model into the software for radio channel modeling. (b) The algorithm for IRI data optimization to the real time condition. (c) Results of comparison between simulated and measured ionograms. Based on these updates, a new software tool called North Ionospheric Model with IRI and Ray Tracing (NIMIRI-RT) was developed, and a number of vertical ionograms corresponding to multiple ionospheric reflections was simulated. The vertical ionograms observed at various ionosondes were compared with the synthesized ionograms, generated by applying NIM-RT in conjunction with initial and optimized IRI data. The ionogram structure simulated by NIMIRI-RT based on the data retrieved from optimized IRI is more reminiscent to the observations than ionograms synthesized with the initial NIMIRI-RT without parameters optimization. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer (foF2) one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years’ foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years’ data were used as a training dataset and the second eleven years’ data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32MHz, which is better than 0.34MHz from BPNN, 0.35MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78MHz, which is better than 0.81MHz from BPNN, 0.81MHz from SVR and 1.37MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper. [Copyright &y& Elsevier]

We analyze the methods for measuring azimuth deformation with the L-band Advanced Land Observing Satellite-2 (ALOS-2) scanning synthetic aperture radar (ScanSAR) interferometry. To implement the methods, we extract focused bursts from the ALOS-2 full-aperture product, which is the only product available for ScanSAR interferometry at present. The extracted bursts are properly processed to measure azimuth deformation using interferometric phase. We apply the range split-spectrum method to ScanSAR to estimate the differential ionospheric phase of the interferogram, and take the azimuth derivative of the differential ionospheric phase to mitigate the relative azimuth shift caused by ionosphere. For the first time, azimuth deformation of a large earthquake (April 25, 2015 Nepal earthquake) is nearly completely measured by the L-band ScanSAR interferometry with moderate precision. The result is validated by the azimuth deformation measured by incoherent cross correlation using a pair of high-resolution RADARSAT-2 images. In addition to the final azimuth deformation, we show the possibility of processing full-aperture ScanSAR product using a burst-by-burst approach to form regular interferograms. We also show the recent strong large-scale ionospheric effects on the L-band ALOS-2 ScanSAR interferograms. Other possible applications of this paper include measuring the movement of glaciers. [ABSTRACT FROM PUBLISHER]

In this work, results obtained by investigating the application of different neural network backpropagation training algorithms are presented. This was done to assess the performance accuracy of each training algorithm in total electron content (TEC) estimations using identical datasets in models development and verification processes. Investigated training algorithms are standard backpropagation (SBP), backpropagation with weight delay (BPWD), backpropagation with momentum (BPM) term, backpropagation with chunkwise weight update (BPC) and backpropagation for batch (BPB) training. These five algorithms are inbuilt functions within the Stuttgart Neural Network Simulator (SNNS) and the main objective was to find out the training algorithm that generates the minimum error between the TEC derived from Global Positioning System (GPS) observations and the modelled TEC data. Another investigated algorithm is the MatLab based Levenberg-Marquardt backpropagation (L-MBP), which achieves convergence after the least number of iterations during training. In this paper, neural network (NN) models were developed using hourly TEC data (for 8 years: 2000-2007) derived from GPS observations over a receiver station located at Sutherland (SUTH) (32.38° S, 20.81° E), South Africa. Verification of the NN models for all algorithms considered was performed on both "seen" and "unseen" data. Hourly TEC values over SUTH for 2003 formed the "seen" dataset. The "unseen" dataset consisted of hourly TEC data for 2002 and 2008 over Cape Town (CPTN) (33.95° S, 18.47° E) and SUTH, respectively. The models' verification showed that all algorithms investigated provide comparable results statistically, but differ significantly in terms of time required to achieve convergence during input-output data training/learning. This paper therefore provides a guide to neural network users for choosing appropriate algorithms based on the availability of computation capabilities used for research. [ABSTRACT FROM AUTHOR]

Vertical sounding is a widely used technique to obtain ionosphere measurements, such as an estimation of virtual height versus frequency scanning. It is performed by high frequency radar for geophysical applications called “ionospheric sounder” (or “ionosonde”). Radar detection depends mainly on targets characteristics. While several targets behavior and correspondent echo detection algorithms have been studied, a survey to address a suitable algorithm for ionospheric sounder has to be carried out. This paper is focused on automatic echo detection algorithms implemented in particular for an ionospheric sounder, target specific characteristics were studied as well. Adaptive threshold detection algorithms are proposed, compared to the current implemented algorithm, and tested using actual data obtained from the Advanced Ionospheric Sounder (AIS-INGV) at Rome Ionospheric Observatory. Different cases of study have been selected according typical ionospheric and detection conditions. [ABSTRACT FROM AUTHOR]

Among all types of unwanted signals in high-frequency (HF) surface wave (HFSW) over-the-horizon (OTH) radars, radio-frequency interference (RFI) is dominant since HF band is shared by many radio services. In observation data, there are two types of common RFI. The most common one is the conventional RFI which presents vertical stripe paralleling to range axis in range-Doppler spectrum (RDS) and has been exhaustively reported by previous papers. Meanwhile, a new type of RFI characterized by sloping stripes (called \RFISS) in RDS is also frequently observed. This work concentrates on the new \RFISS and establishes a unified model for the above two types of RFI. Based on this generalized model, a time-domain RFI suppression algorithm is proposed here. Benefiting from a closed-form approximate maximum likelihood estimator, the proposed algorithm exhibits excellent performance and is computationally efficient. Its operational performance is evaluated using the field data recorded by experimental HFSW OTH radar of Wuhan University. [ABSTRACT FROM PUBLISHER]

Abstract: Auroral nighttime infrared emission observed by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument onboard the Thermosphere–Ionosphere–Mesosphere Energetics and Dynamics (TIMED) satellite is used to develop an empirical model of geomagnetic storm enhancements to E-region peak electron densities. The empirical model is called STORM-E and will be incorporated into the 2012 release of the International Reference Ionosphere (IRI). The proxy for characterizing the E-region response to geomagnetic forcing is NO+(v) volume emission rates (VER) derived from the TIMED/SABER 4.3μm channel limb radiance measurements. The storm-time response of the NO+(v) 4.3μm VER is sensitive to auroral particle precipitation. A statistical database of storm-time to climatological quiet-time ratios of SABER-observed NO+(v) 4.3μm VER are fit to widely available geomagnetic indices using the theoretical framework of linear impulse-response theory. The STORM-E model provides a dynamic storm-time correction factor to adjust a known quiescent E-region electron density peak concentration for geomagnetic enhancements due to auroral particle precipitation. Part II of this series describes the explicit development of the empirical storm-time correction factor for E-region peak electron densities, and shows comparisons of E-region electron densities between STORM-E predictions and incoherent scatter radar measurements. In this paper, Part I of the series, the efficacy of using SABER-derived NO+(v) VER as a proxy for the E-region response to solar-geomagnetic disturbances is presented. Furthermore, a detailed description of the algorithms and methodologies used to derive NO+(v) VER from SABER 4.3μm limb emission measurements is given. Finally, an assessment of key uncertainties in retrieving NO+(v) VER is presented. [Copyright &y& Elsevier]

This paper summarizes the main results obtained during the development of an Enhanced Precise Point Positioning (EPPP) Global Navigation Satellite Systems multifrequency user algorithm. The main innovations include the application of precise ionospheric corrections to facilitate the resolution of undifferenced carrier phase ambiguities, ambiguity validation, and integrity monitoring. The performance of the EPPP algorithm in terms of accuracy, convergence time, and integrity is demonstrated with actual GPS and simulated Galileo data. This can be achieved with very limited bandwidth requirements for EPPP users (less than 300 b/s for dual-frequency GPS data). [ABSTRACT FROM PUBLISHER]

Abstract: The high frequency management system with backscatter radar supplies the real time ionosphere channel conditions to high frequency users, which leads to the demand for the ground range between the radar location and the scatters on the distant ground. The ionosphere electron density profile is usually inversed to obtain the ground range. An inversion algorithm, with which the ground range on the leading edge of the backscatter ionograms can be obtained without electron density, is presented in this paper. The ray path geometry of the backscatter sounding and the change in the group path on the leading edge with operating frequency are used to derive the ground range. Synthesized backscatter ionogram and experimental backscatter ionograms are processed to validate the algorithm. The results indicate that the algorithm is usable for high frequency management system. [Copyright &y& Elsevier]

Abstract: The Ionospheric Total Electron Content is responsible for the group delay of the signals from the Navigation satellites. This delay causes ranging error, which in turn degrades the accuracy of position estimated by the receivers. For critical applications, single frequency receivers resort to Satellite Based Augmentation Systems in order to have improved accuracy and integrity. The performance of these systems in terms of accuracy can be improved if predictions of the delays are available simultaneously with real measurements. This paper attempts to predict the Total Electron Content using adaptive recurrent Neural Network at three different locations of India. These locations are selected at the magnetic equator, at the equatorial anomaly crest and outside the anomaly range, respectively. In-situ Learning Algorithm has been used for tracking the non-stationary nature of the variation. Prediction is done for different prediction intervals. It is observed that, for each case, the mean and root mean square values of prediction errors remain small enough for all practical applications. Analysis of Variance is also done on the results. [Copyright &y& Elsevier]

In this paper, a hybrid reconstruction algorithm (HRA) is presented to solve the ill-posed inverse problem associated with 3-D ionospheric stochastic tomography. In this new method, the ionospheric electron density (IED) can be inverted by using two steps. First, a truncated singular value decomposition (TSVD) method, whose value is independent on any initial estimation, is used to resolve the ill-posed problem of the tomography system. Second, taking into account the "approximation" of its solution, an iterative improvement process of the solution is then implemented by utilizing the conventional algebraic reconstruction algorithm (ART). The HRA, therefore, offers a more reasonable approach to choose an initial approximate for the ART and to improve the quality of the final reconstructed image. A simulated experiment demonstrates that the HRA method is superior to the TSVD or the ART alone for the tomographic inversion of LED. Finally, the HRA is used to perform GPS-based tomographic reconstruction of the IED at mid- and low-latitude regions. [ABSTRACT FROM AUTHOR]

In this paper, we report a preliminary analysis of the impact of Global Navigation Satellite System Reflections (GNSS-R) data on ionospheric monitoring over the oceans. The focus will be on a single polar Low Earth Orbiter (LEO) mission exploiting GNSS-R as well as Navigation (GNSS-N) and Occulttation (GNSS-O) total electron content (TEC) measurements. In order to assess impact of the data, we have simulated GNSS-R/O/N TEC data as would be measured from the LEO and from Inter- national Geodesic Service (IGS) ground stations, with an electron density (ED) field generated using a climatic ionospheric model. We have also developed a new tomographic approach inspired by the physics of the hydrogen atom and used it to effectively retrieve the ED field from the simulated TEC data near the orbital plane. The tomographic inversion results demonstrate the significant impact of GNSS-R: three dimensional ionospheric ED fields are retrieved over the oceans quite accurately, even as, in the spirit of this initial study, the simulation and inversion approaches avoided intensive computation and sophisticated algorithmic elements (such as spatio-temporal smoothing). We conclude that GNSS-R data over the oceans can contribute significantly to a GIobaI/GNSS Ionospheric Observation System (GIOS). [ABSTRACT FROM AUTHOR]