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This paper introduces a Triangle Network‐Joint Slope (TN‐JS) approach to characterize the spatial and temporal dynamics of Equatorial Plasma Bubbles (EPBs) during geomagnetic storms. To collaboratively determine the EPB drift directions from multiple stations, a Delaunay triangle network is constructed, utilizing the distribution of Ionospheric Piercing Points (IPPs). The Time Difference of Arrival (TDOA) is extracted through cross‐correlating the Rate of Total Electron Content (ROT). The EPB drift direction can be approximately calculated by considering TDOA and IPP distances within each individual triangle of the network. This calculation is then refined through a joint statistical analysis. Using a reference station as the origin, the remaining stations within the network are projected along the estimated EPB drift direction. A spatial‐temporal color map illustrating regional ionospheric anomaly ROT observations is constructed. The EPB drift velocity among multiple stations can be collectively estimated by fitting the slope of this map, facilitating outlier exclusion. Accounting for satellite dynamic effects and the diverse orbit characteristics of GPS and BDS, corresponding IPP scan velocity compensation is performed and analyzed for EPB dynamic estimation. Using the geomagnetic storm event that occurred on September 8 as a case study, the spatial‐temporal kinetic properties of EPBs is characterized by analyzing Global Navigation Satellite System (GNSS) observations from 17 Hong Kong monitoring stations with the proposed TN‐JS approach. The results indicate during this magnetic event, that EPBs exhibit a westward drift trend with velocities ranging from a few tens to hundreds of meters per second in GPS and BDS observations. Plain Language Summary: Total Electron Content (TEC) is a path integrated electron density and its rate (ROT) of change reflect the ionospheric disturbance during magnetic storms. This article introduces a new method called Triangle Network‐Joint Slope (TN‐JS) to study the movement of Equatorial Plasma Bubbles (EPBs). TN‐JS uses a network of GNSS monitoring stations to determine the drift velocity of EPBs. By resampling ROT correlation using triangulation along the drift direction, TN‐JS transforms traditional EPB dynamic estimation into image processing of the color‐coded ROT maps. The TN‐JS algorithm is tested with data collected from 17 monitoring stations around Hong Kong during a geomagnetic storm on 8 September 2017 to show EPBs drifting westward at speeds ranging from tens to hundreds of meters per second. Key Points: A Delauny Triangle Network is built for statistically inferring EPB drift velocity by cross‐correlating and slope fitting multi‐sites' ROTThe orbit diversity offered by GPS MEO and BDS GEO/IGSO satellites provides measures of ionospheric irregularities inhomogeneityThe analysis unveiled a significant EPB westward drift event with a speed exceeding 500 m/s during the 2017 geomagnetic storm over Hong Kong [ABSTRACT FROM AUTHOR]

We present two case studies of traveling ionospheric disturbances (TIDs) triggered by the 2022 Tonga volcanic eruption observed by low‐cost CubeSat‐based global navigation satellite system reflectometry (GNSS‐R) measurements. The GNSS‐R data used in this work are from Spire Global CubeSats. Our analysis shows that coherent GNSS signals reflected over the ocean can be used to derive precise ionospheric total electron content (TEC) measurements. The first case shows clear TID structures with a TEC disturbance magnitude of ∼1 TEC unit (TECu) and horizontal wavelength of ∼330 km over Northwest Australia on the day of Tonga volcanic eruption. The second case shows the TIDs with ∼0.05 TECu disturbance magnitude and horizontal wavelength of ∼240 km over East Russia the day after the eruption. The second case is likely a TIDs propagated outward from Tonga for the second time after it traversed around the Earth. The GNSS‐R observed TID may be associated with the incident or reflection signal ray path. In this paper, the appropriate ray path was identified using simultaneous observations from ground receiver networks in the areas of both ray paths. Plain Language Summary: The Tonga volcanic eruption on 15 January 2022 triggered various atmospheric and ionospheric waves which have been observed globally. There have been several studies showing traveling ionospheric disturbances (TIDs) triggered by this event using ground global navigation satellite system (GNSS) networks and GNSS radio occultation measurements. This study shows for the first time that the TIDs can also be observed using GNSS signals reflected from ocean surface and received by a side‐looking antenna onboard a low‐cost CubeSat. The dual‐frequency GNSS reflection (GNSS‐R) signals contain sufficient coherent energy to enable precise total electron content (TEC) estimations. This work presents two case studies of TIDs captured by receivers onboard a Spire Global CubeSat. The first case shows a TID with horizontal wavelength of ∼330 km and TEC disturbance magnitude of ∼1 TECu through the GNSS‐R signals received over Northwest Australia. The second case shows a TID observed over East Russia with TEC perturbation magnitude of ∼0.05 TECu and horizontal wavelength of ∼240 km. The two case results are validated by nearly identical wave characteristics observed by local ground‐based GNSS receivers in Australia and Japan respectively. Key Points: Global navigation satellite system reflections over sea ice and calm oceans have sufficient coherent energy to offer precise total electron content (TEC) measurementsThe global navigation satellite system reflectometry (GNSS‐R) derived TEC is the combination of TEC from incident ray and reflected rayThe GNSS‐R signal scans captured the traveling ionospheric disturbances on the event day and the day after of Tonga eruption [ABSTRACT FROM AUTHOR]

This paper investigates concentric traveling ionospheric disturbances (CTIDs) associated with the Tonga volcanic eruption. Results show that: (a) two types of CTIDs (CTID #1 and CTID #2) were identified that traveled radially from Tonga at the speed of 610–880 m/s (acoustic‐mode) and 300–380 m/s (Lamb‐mode), respectively. CTID #1 reached 3,800 and 5,000 km away from the eruption location toward the directions of New Zealand and Australia, respectively. CTID #2 propagated persistently for ∼9 hr over New Zealand and Australia. (b) The CTID #2 wavefront changed after 08:35 UT over New Zealand, possibly due to a combination of factors including the anisotropic propagation of CTID #2, the regional geomagnetic declination, and westward‐moving Lamb waves. (c) Topside total electron content (TEC) enhancement with a magnitude over two TECu was observed from COSMIC‐2 measurements. The enhancement agrees with CTID #1 peak from nearby ground‐based TEC observations and could be related to the upward propagation of the F layer's CTID #1 signatures. Plain Language Summary: The Tonga volcanic eruption on 15 January triggered various atmospheric waves that propagate from the Earth's surface and throughout the atmosphere and ionosphere. In this study, we discuss two types of concentric traveling ionospheric disturbances (CTIDs, #1 and #2) propagating outward from the Tonga site based on measurements collected by ∼1,000 ground‐based global navigation satellite system receivers. Our analysis based on the CTID propagation speed showed that CTIDs #1 and #2 traveled at acoustic and Lamb wave modes, respectively. We also analyzed COSMIC‐2 satellite radio occultation observations and showed that CTID #1‐related enhancement signatures were observed at the topside ionosphere near the eruption site. Moreover, it is interesting to note that CTID #2 wavefront changed over New Zealand after 08:35 UT on 15 January likely followed the regional geomagnetic declination and westward‐moving Lamb waves. Key Points: Two distinctive types of concentric traveling ionospheric disturbances (CTIDs #1 and #2) were identified, and they propagated radially outward from Tonga at the speed of 610–880 m/s (acoustic‐mode) and 300–380 m/s (Lamb‐mode), respectivelyThe wavefront of the long‐lasting CTID #2 changed after 08:35 UT over New Zealand, possibly due to the regional geomagnetic declination and westward‐moving Lamb wavesDistinctive total electron content (TEC) enhancement of over 2 TECu magnitude observed above 530 km near the eruption site could be associated with the upward propagation of the acoustic‐mode CTID #1 signatures in the F layer [ABSTRACT FROM AUTHOR]

This paper applies the convolutional long short‐term memory (convLSTM)‐based machine learning models to forecast global ionospheric total electron content (TEC) maps with up to 24 hr of lead time at a 1‐hr interval. Four convLSTM‐based models were investigated, and the one that implements the L1 loss function and the residual prediction strategy demonstrates the best performance. The convLSTM models are trained and evaluated using Center for Orbit Determination in Europe (CODE) global TEC maps over a period of nearly seven years from 19 October 2014 to 21 July 2021. Results show that the best convLSTM model outperforms the 1‐day predicted global TEC products released by CODE analysis center (c1pg) and persistence models under various levels of solar and geomagnetic activities, except for a lead time beyond 8 hr during the storm time where the c1pg has slightly better performance. The convLSTM forecasting performance degrades as the lead time increases. Plain Language Summary: Reliable specification and prediction of ionospheric total electron content (TEC) are not only helpful for mitigating uncertainties in global navigation satellite system‐based position, navigation, and timing services, but also for timely warning of space weather activities. We apply convolutional long short‐term memory (convLSTM)‐based machine learning models to forecast global ionospheric TEC maps with up to 24 hr of lead time at a 1‐hr interval. Four convLSTM‐based models were investigated, and the one that implements the L1 loss function and residual prediction strategy demonstrates the best performance. Moreover, our developed convLSTM model shows competitive performance when compared to two conventional models under various levels of solar and geomagnetic activities. Key Points: Four convolutional long short‐term memory (convLSTM)‐based models are investigated to forecast global ionospheric total electron content maps with up to 24 hr of lead time at a 1‐hr intervalThe one that implements the L1 loss function and residual strategy demonstrates the best performance among four convLSTM‐based modelsThis best performing convLSTM model also shows more accurate prediction compared to c1pg and persistence models [ABSTRACT FROM AUTHOR]

Positioning with cellular signals has been gaining attention in urban and indoor environments, where global navigation satellite system signals have limited availability due to interference, blockage, or multipath. However, accurate and reliable tracking of cellular signals under highly dynamic urban channel conditions remains a challenging task. This article presents a cellular long-term evolution (LTE) signal tracking algorithm implemented by an adaptive multipath estimating delay lock loop (AMEDLL) to achieve carrier phase synchronization and time-of-arrival (TOA) tracking under severe multipath propagation conditions. The analytical expression of the coherently integrated correlation result over multiple slots with the LTE cell-specific reference signal is derived. A multipath estimator along with a simple yet efficient multipath estimation monitoring approach is developed to estimate the parameters of all detected multipath signals. Several heuristic monitoring criteria based on historical multipath parameter estimations are established to enable adaptive adjustment of the estimated path number. Real LTE signals are collected in an urban environment for the signal tracking performance evaluation. This article presents two case studies with varying levels of multipath effects and signal power to illustrate the effectiveness of the developed signal tracking algorithm. Instead of a TOA truth reference, open-loop carrier phase estimations are used to analyze the TOA tracking error. Our analyses demonstrate that the AMEDLL-based tracking algorithm provides improved TOA estimation accuracy over the existing super-resolution-algorithm-based and delay-lock-loop-based tracking schemes. [ABSTRACT FROM AUTHOR]

Ionospheric total electron content (TEC) maps with high spatial and temporal resolutions are essential for depicting the state of the ionosphere and for performing ionospheric delay corrections associated with satellite navigation applications. Low Earth orbit (LEO) CubeSat-based global navigation satellite system (GNSS) reflectometry (GNSS-R) measurements provide a promising opportunity for retrieval of ionospheric TEC over sea ice and calm waters, which offers a potential new data source to fill the gaps of ground-based GNSS networks. However, the GNSS-R slant TEC (sTEC) estimations include contributions from the incident and reflection ray paths, whose ionospheric piercing points (IPPs) can be separated by hundreds of kilometers. This article presents an algorithm that integrates sTEC measurements from the GNSS-R CubeSats and available ground-based GNSS receivers to derive Arctic vertical TEC (vTEC) maps. A simulation study using the model ionosphere constructed from the NeQuick-2 is conducted to assess the performance of the algorithm. Varying levels of temporal resolutions and solar activities, and the number of CubeSats and the number of maximum simultaneously tracked reflection signals by a CubeSat are implemented in the simulation. The results show that the inclusion of coherent GNSS-R measurements improves the accuracy of the vTEC maps under all levels of solar activities. The RMSE improvement percentage is most obvious when the update interval is the shortest. Increasing the number of CubeSats further improves the accuracy. However, no significant improvement in vTEC map accuracy is observed when the number of maximum simultaneously tracked GNSS-R satellites is higher than 4. Quantitative measures and analyses of the algorithm performances are presented in this article. [ABSTRACT FROM AUTHOR]

This paper presents a random forest-based machine learning algorithm to automatically detect satellite oscillator anomalies using dual-or triple-frequency GPS carrier phase measurements. The algorithm can distinguish satellite oscillator anomalies from other GPS carrier phase disturbances including ionospheric scintillation and receiver oscillator anomalies. Carrier phase power spectral density and carrier phase ratios between carriers are extracted from measurements and applied as input features to the random forest algorithm. The method is trained using data collected at seven GNSS monitoring stations located in Alaska, Ascension Island, Greenland, Hong Kong, Peru, Puerto Rico, and Singapore. The overall detection accuracies of 98.4% and 99.0% are achieved for dual- and triple-frequency signals, respectively. The method outperforms other machine learning algorithms. The preliminary detection results demonstrate that the method presented can be employed on a global satellite oscillator anomaly monitoring system. [ABSTRACT FROM AUTHOR]

In this article, we investigate coherent global navigation satellite system reflectometry (GNSS-R) measurements obtained at the low earth orbits (LEOs) as a potential new data source for ionospheric total electron content (TEC) and ionospheric disturbance observations. Current global ionospheric TEC maps (GIMs) have limited spatial and temporal resolutions and accuracy due to lack of GNSS observations over oceans, polar caps, and inaccessible terrains. Our analysis of Spire Global’s CubeSat data indicates that coherent GNSS signals reflected off sea ice, inland water bodies, and calm ocean surface can be processed to achieve cm-level precision carrier phase estimations. Signal coherency is especially prevalent over sea ice where 41.7% reflections are coherent, compared to 4.3% in the overall dataset. This article presents the methodology to estimate slant TEC along the reflection signal ray path using coherent dual-frequency GNSS-R pseudoranage and carrier phase estimations obtained from low-cost CubeSats. The methodology is applied to Spire Global’s CubeSat data. The results show that the slant TEC retrieved from GNSS-R measurements and from GIM follow a similar trend. Moreover, the GNSS-R-based TEC time series offer a nearly “frozen in time” observation of the ionospheric structures due to the rapid scan velocity of GNSS-R rays across the ionosphere. The study demonstrates the potential of GNSS-R observations to fill the data gaps over the polar regions where space weather activities and TEC disturbances are most frequent and intense. Potential error sources and mitigation techniques are also discussed. [ABSTRACT FROM AUTHOR]

Coherent reflections of global navigation satellite system (GNSS) signals have a measurable carrier phase, enabling higher precision for certain GNSS-based Earth remote sensing applications. In this letter, we explore the dependence of coherence on three land surface characteristics: surface water, topography, and soil moisture (SM). Carrier phase measurements are obtained by tracking raw intermediate frequency data collected by the cyclone GNSS (CYGNSS) mission. In total, several hundred data collections between 2017 and 2019 are analyzed. The phase coherence, quantified using statistics of the tracked carrier phase, is compared to the corresponding land characteristics on a per-track basis and across the entire dataset. On a per-track basis, we find that the level of coherence can often be explained by the presence of surface water, with no obvious dependence on topography or SM. However, by analyzing the entire dataset, we show that topography and SM have a weak but noticeable impact on the coherence. [ABSTRACT FROM AUTHOR]

This study presents an image‐based convolutional long short‐term memory (convLSTM) machine learning algorithm to predict storm‐time ionospheric irregularities. Unlike existing methods that are either focused on irregularities at individual locations or treat the irregularity prediction as a classification problem, the convLSTM‐based architecture forecasts an entire regions' ionospheric irregularity occurrence and intensity values. We implemented the convLSTM‐based model with a custom‐designed loss function (convLSTM‐Lc) that includes a dynamic penalty on the difference between the truth and the predicted rate of total electron content index (ROTI) maps. The convLSTM‐Lc is trained with real ROTI data collected during January 1–August 7, 2015 from ∼550 global navigation satellite system receivers located in (45°–90°N, 0°–180°W). Test results show that the convLSTM‐Lc algorithm can forecast irregularity structures more accurately than a convLSTM model that implements conventional loss functions. The model also outperforms the convLSTM‐L1, convLSTM‐L2, and persistence models according to statistical classification metrics with a lead time of up to 60 min. Plain Language Summary: Forecasting the occurrence of ionospheric irregularities is a challenging task due to their dynamic nature driven by solar‐geomagnetic activities and multi‐scale ionospheric processes. We present an image‐based machine learning (ML) approach to predict storm‐time two‐dimensional (2D) ionospheric irregularity occurrence and intensity using ground‐based global navigation satellite system receiver measurements. The new approach can forecast 2D spatial structures of the irregularities more accurately than conventional ML approaches because a custom‐designed loss function is used. The improvement is particularly evident with up to a 30‐min lead time. The new approach also significantly outperforms the state‐of‐the‐art model according to statistical classification metrics with a lead time of up to 60 min. This encouraging model could play a critical role in the advanced warning for space weather and satellite navigation communities. Key Points: The convolutional long short‐term memory (convLSTM)‐Lc model is developed to predict ionospheric irregularities using global navigation satellite system‐derived rate of total electron content index (ROTI) mapsThe custom‐designed novel loss function improves the prediction performance of ROTI maps compared to conventional approachesThe convLSTM‐Lc performs well in predicting short‐term ionospheric irregularities under geomagnetic active conditions at mid‐ and high latitudes [ABSTRACT FROM AUTHOR]

Deep signal fading due to ionospheric scintillation severely impacts global navigation satellite system (GNSS)‐based applications. GNSS receivers run the risk of signal loss under deep fading, which directly leads to a significant decrease in navigation availability. The impact of scintillation on GNSS‐based applications can be mitigated via dual‐frequency signals which provide a backup channel. However, the benefit of dual‐frequency diversity highly depends on the correlation of fading processes between signals at different frequencies. This paper proposes a Markov chain‐based model that simulates the actual behavior of correlated fading processes in dual‐frequency channels. A set of recorded scintillation data was used to capture transitions among all fading states based on the fading and recovery of each signal frequency. A statistical study of deep fading characteristics in this data revealed that the Markov chain‐based model accurately generates realistic correlated fading processes. Using the proposed model, aviation availability of localizer performance with vertical guidance down to a 200‐foot decision height ("LPV‐200") under a strong scintillation scenario is analyzed by considering the effects of signal outages due to deep fading. A parametric analysis of the availability resulting from variations in mean time to loss of lock, mean time to reacquisition, and ionospheric delay uncertainty was conducted to investigate the performance standards on GNSS‐based aviation under scintillation. The analysis results demonstrate a significant benefit of frequency diversity on aviation availability during scintillation. This model will further enable the assessment of GNSS‐based availability for aviation and other applications under a full range of scintillation conditions. Plain Language Summary: One of the most detrimental impacts of space weather on GNSS‐based navigation applications is ionospheric scintillation in equatorial region, which can cause deep and frequent signal fading on GNSS signals. In particular, GNSS navigation may be lost when multiple satellites are briefly unusable for GNSS receiver calculations due to the signal fades caused by scintillation. The use of dual‐frequency signals can diminish the impact of scintillation by providing a backup ranging source on one frequency. However, frequency diversity is only partially helpful because the effect of scintillation is correlated across frequencies, meaning that a receiver may still lose satellites when their signals on both frequencies are simultaneously plagued by deep fading. Here we propose a new stochastic model to represent a more accurate description of correlated fading processes observed in actual scintillation data. We incorporate scintillation effects into a simulation of aviation availability. The simulation results provide estimates of the impact of scintillation on L1 single and L1/L5 dual‐frequency aviation availability and quantify the benefit from the use of dual‐frequency signals during strong scintillation. Further development of this model will enable the assessment of effects from other scintillation conditions and scenarios on availability for aviation and other applications of GNSS. Key Points: Deep fades observed in a Global Positioning System (GPS) L1/L5 strong scintillation data set have been characterized in terms of availability for global navigation satellite system (GNSS) navigationA stochastic model based on a Markov chain accurately generates realistic correlated fading processes of GNSS signals under scintillationNew Markov chain model confirms that use of dual‐frequency GNSS signals significantly enhances aviation availability during scintillation [ABSTRACT FROM AUTHOR]

This article presents an adaptive hybrid-tracking (AHT) algorithm designed to process GNSS-R signals with a sufficient coherent component. Coherent GNSS-R signals have the potential to enable high-precision and high-resolution carrier-phase measurements for altimetry, sea-level monitoring, soil-moisture monitoring, flood mapping, snow–water equivalent measurements, and so on. The AHT algorithm incorporates the model inputs typically used in the master–slave open-loop (MS-OL) architecture into a closed-phase lock loop. Raw IF data recorded by the CYGNSS satellites over in-land water, land, and open-ocean surface are used to demonstrate the performance of the AHT. The results show that the AHT algorithm achieves comparable robustness with the MS-OL implementation while maintaining centimeter-level accuracy and excellent carrier-phase continuity that can be achieved with a fine-tuned Kalman filter (KF)-based adaptive closed-loop (ACL) system. Moreover, the AHT is suitable for real-time implementation and is applicable to other radio signals-of-opportunity. [ABSTRACT FROM AUTHOR]

Long term evolution (LTE) signals have the potential for use in positioning, especially in challenging environments. The time‐of‐arrival (TOA)‐based technique supported by LTE is attractive due to its high positioning accuracy. However, it is vulnerable to multipath propagation effects in typical LTE channels. This paper will summarize several existing advanced TOA estimators for LTE signals, i.e., first peak detection, information theoretic criteria, super‐resolution algorithm, and delay‐lock loop (DLL). Later, the paper will evaluate the TOA estimation performances of these techniques with multipath propagation effects and varying signal conditions using simulations. For the DLL, the multipath error envelope metric is assessed for different signal bandwidths. The root mean square errors of the TOA estimations are compared to evaluate suitable TOA estimators under various conditions. Finally, some other performance characteristics of these techniques are also discussed. [ABSTRACT FROM AUTHOR]

Global navigation satellite system radio occultation (GNSS-RO) signals have been demonstrated as a viable means to retrieve atmospheric profiles. Current GNSS-RO observations rely on open-loop (OL) processing of the signals, especially for signals propagating through the lower troposphere. The reason being GNSS signals at low elevations are adversely affected by multipath effects due to propagation through lower troposphere structures and reflections and scattering from the Earth surface. The low-elevation RO signals are characterized by deep and fast amplitude fading and rapid signal carrier phase fluctuations, collectively referred to as signal scintillation. The conventional phase-lock loop may lose lock of these signals. While OL tracking is known for its robustness, its accuracy is determined by the climatological models used to create the reference for the GNSS signal carrier tracking loop. The wide bandwidth typically associated with OL tracking also introduces large errors in signal parameters estimations. In this article, we present an adaptive Kalman filter-based closed-loop (KFC) tracking method, which takes into consideration the tropospheric scintillation, platform vibration, and real-time C/N$_0$ estimation of the RO signals. The KFC method has comparable robustness with and improved accuracy over the OL tracking, which are demonstrated through comparison using real global positioning system RO data collected on an airborne platform. Analysis of the excess Doppler estimation, retrieved bending angles, and impact parameters also confirms the improved performances of the proposed algorithm over OL tracking. [ABSTRACT FROM AUTHOR]

In this paper, we propose a machine‐learning‐based approach to automatically detect a satellite oscillator anomaly. A major challenge is to differentiate an oscillator anomaly from ionospheric scintillation. Although both scintillation and oscillator anomalies cause phase disturbances, their underlying physics are different and, therefore, show different carrier‐frequency dependency. By using triple‐frequency signals, distinct features are extracted from the disturbed signals and applied to the radial basis function (RBF) support vector machine (SVM) classifier to identify an oscillator anomaly. The results show that the proposed RBF SVM displays superior performance and outperforms several other classification methods. The proposed approach is applied to an extensive GNSS database to conduct automatic satellite oscillator anomaly detection. Preliminary detection results validate the effectiveness of the proposed method. On average, one‐to‐three satellite oscillator anomaly events are detected daily at each receiver location. [ABSTRACT FROM AUTHOR]

Long-term evolution (LTE) signals are potential signals-of-opportunity for position and navigation, especially in challenging urban and indoor environments. A major challenge is that the LTE signal time-of-arrival (TOA) estimations are susceptible to the multipath propagation effects. In this paper, the multipath estimating delay lock loop (MEDLL), which is originally designed for global positioning system receivers, is applied to LTE signal TOA estimation in multipath environments. We derive the analytical expression of the correlation function for LTE signals and present the procedure for estimating parameters of the detected multipath components. Two initialization methods without and with super-resolution algorithm (SRA) are developed for the MEDLL. Our analyses show that the MEDLL with SRA-based initialization can achieve better multipath resolution, while the one without SRA has less complexity. Extensive simulations involving static multipath scenarios are conducted to examine the statistical TOA estimation performance of the proposed MEDLL with LTE cell-specific reference signal. The simulation results and computational complexity analysis indicate that the proposed MEDLL outperforms the conventional delay lock loop and SRA in term of multipath mitigation performance and computational complexity. Experimental results using real collected LTE signals in urban environments are also provided to demonstrate the effectiveness of the proposed technique for realistic scenarios. [ABSTRACT FROM AUTHOR]

This paper applies a machine learning (ML) algorithm based on the multi-hidden layer neural network (MHL-NN) for ocean surface wind speed estimation using global navigation satellite system (GNSS) reflection measurements. Unlike conventional wind speed retrieval methods that often depend on limited scalar delay-Doppler map (DDM) observables, the proposed MHL-NN makes use of information captured by the entire DDM. Both simulated and real data sets are used to train and evaluate the performance of the MHL-NN and compare it to a conventional wind speed retrieval method and other prevailing ML algorithms. The results show that the MHL-NN algorithm outperforms the other methods in terms of the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the wind speed estimation. [ABSTRACT FROM AUTHOR]

A Global Navigation Satellite System (GNSS) spaced‐receiver technique estimates ionospheric irregularity drift velocity by correlating the received GNSS signals across a closely spaced receiver array during ionospheric scintillations. This paper focuses on the correlation models accounting for the topology of the received diffraction pattern. Space‐time correlation schematics are developed to analyze and compare several prevalent models, including the classic isotropy model, the front velocity model, and the anisotropy model. Based on the merits and drawbacks of each model, a hybrid correlation model is proposed, integrating the front velocity model and the anisotropy model. To validate the hybrid model, the corresponding drift velocity estimates are cross compared with the measurements from a colocated all‐sky imager and incoherent scatter radar. A case study was conducted for a geomagnetic storm event on 20 December 2015. Favorable agreement was found in terms of direction and magnitude of the drift motion, orientation of the irregularity, temporal and spatial features of the irregularity, and the statistical behavior of the drift velocity estimates. In addition, the root‐mean‐square velocity magnitude and orientation against the incoherent scatter radar measurements demonstrate the superior performance of the hybrid model. Key Points: This study established space‐time correlation schematics for analyzing and comparing correlation models in the context of spaced‐receiver techniquesA hybrid correlation model is proposed to estimate ionospheric drift velocity based on the front velocity model and the anisotropy modelGNSS estimated drift velocities achieved favorable comparisons against ASI and PFISR measurements during a geomagnetic storm event [ABSTRACT FROM AUTHOR]

This study for the first time presents an investigation into the exploitation of multi‐Global Navigation Satellite System (GNSS) observations (Global Positioning System, Globalnaya Navigazionnaya Sputnikovaya Sistema, Galileo, and BeiDou) to characterize ionospheric plasma irregularities, based on the rate of change of total electron content index (ROTI) sampled at 1 s. It is demonstrated that the multi‐GNSS ROTIs can represent temporal evolutions of ionospheric plasma irregularities during a large geomagnetic storm. However, an inconsistency in the magnitudes of multi‐GNSS ROTIs is found among a variety of GNSS receivers (i.e., Javad, Leica, Trimble, and Septentrio). Through cross comparisons between GNSS receivers installed at zero/short baselines and validations by receivers distributed separately, it is observed that the magnitude of ROTI corresponding to each system differs between closely installed and even collocated receivers of different models, as well as between the GNSS signals on the same frequency. From 1‐year (i.e., 2015) data analysis, it is found that the magnitudes of multi‐GNSS ROTIs exhibit the dependence on the receiver type. Among the four GNSS receiver types, the largest discrepancy in the multi‐GNSS ROTIs is observed from Septentrio receivers, while the smallest one is shown by Trimble receivers. A one‐to‐one comparison indicates that the ROTI difference is noticeable and can increase to 4–6 TECu/min under ionospheric irregularities conditions, that is, in the postsunset period of 18–02 local time. To investigate the inconsistency, the effect of adopting different equivalent noise bandwidths in the tracking loop design is discussed, via cross comparisons between a GNSS software‐defined receiver and a collocated Septentrio receiver. The result shows that adopting 15‐ and 2‐Hz noise bandwidths in the tracking loop can cause 0.2‐ to 0.5‐TECu/min differences in the magnitude of ROTI, suggesting that the diverse tracking techniques deployed by various receivers are very likely a major contributor to the inconsistency of multi‐GNSS ROTIs. Key Points: The first time exploitation of multiconstellation GNSS (multi‐GNSS) measurements of ionospheric plasma irregularities using 1‐s sampled ROTIMulti‐GNSS ROTIs well illustrate low‐latitude ionospheric responses of plasma density during the large storm on 17 March 2015, but an inconsistency in their magnitudes is foundThe diverse tracking techniques employed by various GNSS receivers are likely the major contributor to the inconsistency [ABSTRACT FROM AUTHOR]

Abstract: The spaced‐receiver technique using Global Navigation Satellite Systems (GNSS) receivers offers an inexpensive approach for estimating ionospheric irregularity velocity during ionospheric scintillations. Our previous work has demonstrated that correlative studies of the GNSS carrier phase variations can be used to derive irregularity drift velocity at high latitudes. This study expanded upon our previous projects by incorporating Global Navigation Satellite System (GLONASS) signals, investigation on ionospheric irregularity height assumption, and all‐sky imager measurements into the methodology. A case study is presented based on Global Positioning System, Galileo, and GLONASS measurements during a geomagnetic storm event on 20 December 2015, obtained from a closely spaced receiver array at Poker Flat Research Range near Fairbanks, Alaska. The GNSS‐estimated irregularity drift velocities are in general agreement with the measurements from the Poker Flat Incoherent Scatter Radar and the Poker Flat all‐sky imager. The study also shows that the irregularity altitude assumption will not lead to significant variations in the irregularity drift velocity estimates, especially for satellites with relatively high elevations. The techniques presented in this paper demonstrate that GNSS receiver arrays can be used as powerful means to monitor the ionospheric plasma dynamics during space weather events. [ABSTRACT FROM AUTHOR]

In this paper, we present a spatiotemporal deep learning (STDL) network to conduct binary phase scintillation forecasting at a high-latitude global navigation satellite systems (GNSS) station. Historical measurements from the target and surrounding GNSS stations are utilized. In addition, external features such as solar wind parameters and geomagnetic activity indices are also included. The results show that the STDL network can adaptively incorporate spatiotemporal and external information to achieve the best performance by outperforming a naive method, three conventional machine learning algorithms (logistic regression, gradient boosting decision tree, and fully connected neural network) and a machine learning algorithm known as long short-term memory that incorporates temporal information. [ABSTRACT FROM AUTHOR]