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Latest Results and Developments in GNSS Ionosphere Theory, Methods, Technologies, Applications and Future Challenges

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 37303

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Special Issue Editors

Dr. M Mainul Hoque

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Institute of Solar-Terrestrial Physics, German Aerospace Center (DLR), Kalkhorstweg 53, 17235 Neustrelitz, Germany
Interests: GNSS ionosphere sounding; space weather; space climate; satellite navigation; geodesy; remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Shuanggen Jin

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Guest Editor

Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
Interests: GNSS meteorology; remote sensing; space/planetary exploration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ionosphere is considered as one of the biggest error sources for space-based Global Navigation Satellite Systems (GNSS) positioning, navigation and timing applications. The use of multi-frequency and multi-GNSS observations from America’s GPS, Russia's GLONASS, China's BeiDou and EU's Galileo and regional systems such as Japan's QZSS and India's IRNSS enable precise remote sensing of the ionosphere and thus mitigation of ionospheric effects in numerous applications. This leads to unprecedented accuracy improvements in GNSS applications.

This Special Issue aims to provide a platform for addressing GNSS ionosphere theory, methods, technologies, applications and future challenges. The Special Issue is open to all scientists who may have the latest results and developments in GNSS ionosphere, including ionospheric delay estimating theory, algorithms, modelling and applications in engineering and Earth/space science, as well as combining multi-sensors observations. Manuscripts on new advances in GNSS ionosphere and space weather are also welcome.

Dr. M Mainul Hoque
Prof. Dr. Shuanggen Jin
Guest Editors

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Published Papers (7 papers)

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Research

18 pages, 6719 KiB

Open AccessArticle

Satellite Formation Flight Simulation Using Multi-Constellation GNSS and Applications to Ionospheric Remote Sensing

by YuXiang Peng and Wayne A. Scales

Remote Sens. 2019, 11(23), 2851; https://doi.org/10.3390/rs11232851 - 30 Nov 2019

Cited by 8 | Viewed by 5153

Abstract

The Virginia Tech Formation Flying Testbed (VTFFTB) is a global navigation satellite system (GNSS)-based hardware-in-the-loop (HIL) simulation testbed for spacecraft formation flying with ionospheric remote sensing applications. Past applications considered only the Global Positioning System (GPS) constellation. The rapid GNSS modernization offers more signals from other constellations, including the growing European system—Galileo. This study presents an upgrade of VTFFTB with the incorporation of Galileo and the associated enhanced capabilities. By simulating an ionospheric plasma bubble scenario with a pair of LEO satellites flying in formation, the GPS-based simulations are compared to multi-constellation GNSS simulations including the Galileo constellation. A comparison between multi-constellation (GPS and Galileo) and single-constellation (GPS) shows the absolute mean and standard deviation of vertical electron density measurement errors for a specific Equatorial Spread F (ESF) scenario are decreased by 32.83% and 46.12% with the additional Galileo constellation using the 13 July 2018 almanac. Another comparison based on a simulation using the 8 March 2019 almanac shows the mean and standard deviation of vertical electron density measurement errors were decreased further to 43.34% and 49.92% by combining both GPS and Galileo data. A sensitivity study shows that the Galileo electron density measurements are correlated with the vertical separation of the formation configuration. Lower C/N

_{0}

level increases the measurement errors and scattering level of vertical electron density retrieval. Relative state estimation errors are decreased, as well by utilizing GPS L1 plus Galileo E1 carrier phase instead of GPS L1 only. Overall, superior performance on both remote sensing and relative navigation applications is observed by adding Galileo to the VTFFTB. Full article

(This article belongs to the Special Issue Latest Results and Developments in GNSS Ionosphere Theory, Methods, Technologies, Applications and Future Challenges)

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15 pages, 5956 KiB

Open AccessArticle

An Enhanced Mapping Function with Ionospheric Varying Height

by Yan Xiang and Yang Gao

Remote Sens. 2019, 11(12), 1497; https://doi.org/10.3390/rs11121497 - 25 Jun 2019

Cited by 33 | Viewed by 5672

Abstract

Mapping function (MF) converts the line-of-sight slant total electron content (STEC) into the vertical total electron content (VTEC), and vice versa. In an MF, an essential parameter is the ionospheric effective height. However, the inhomogeneous ionosphere makes this height vary spatially and temporally, meaning it is not a global constant. In the paper, we review several mapping functions and propose a mapping function that utilizes the ionospheric varying height (IVH). We investigate impacts of the IVH on mapping errors and on the ionospheric modeling, as well as on the satellite and receiver differential code biases (DCBs). Our analysis results indicate that the mapping errors using IVH are smaller than those from the fixed height of 450 km. The integral height achieves smaller mapping errors than using a fixed height of 450 km, an improvement of about 8% when compared with the fixed height of 450 km. And 35% smaller mapping errors were found using HmF2 at the lower latitude. Also, the effects of IVH on the satellite DCBs are about 0.1 ns, and larger impacts on the receiver DCBs at 1.0 ns. Full article

(This article belongs to the Special Issue Latest Results and Developments in GNSS Ionosphere Theory, Methods, Technologies, Applications and Future Challenges)

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21 pages, 4383 KiB

Open AccessArticle

A New Empirical Model of NmF2 Based on CHAMP, GRACE, and COSMIC Radio Occultation

by Zhendi Liu, Hanxian Fang, M. M. Hoque, Libin Weng, Shenggao Yang and Ze Gao

Remote Sens. 2019, 11(11), 1386; https://doi.org/10.3390/rs11111386 - 11 Jun 2019

Cited by 9 | Viewed by 4753

Abstract

To facilitate F2-layer peak density (NmF2) modeling, a nonlinear polynomial model approach based on global NmF2 observational data from ionospheric radio occultation (IRO) measurements onboard the CHAMP, GRACE, and COSMIC satellites, is presented in this paper. We divided the globe into 63 slices from 80°S to 80°N according to geomagnetic latitude. A Nonlinear Polynomial Peak Density Model (NPPDM) was constructed by a multivariable least squares fitting to NmF2 measurements in each latitude slice and the dependencies of NmF2 on solar activity, geographical longitude, universal time, and day of year were described. The model was designed for quiet and moderate geomagnetic conditions (Ap ≤ 32). Using independent radio occultation data, quantitative analysis was made. The correlation coefficients between NPPDM predictions and IRO data were 0.91 in 2002 and 0.82 in 2005. The results show that NPPDM performs better than IRI2016 and Neustrelitz Peak Density Model (NPDM) under low solar activity, while it undergoes performance degradation under high solar activity. Using data from twelve ionosonde stations, the accuracy of NPPDM was found to be better than that of NPDM and comparable to that of IRI2016. Additionally, NPPDM can well simulate the variations and distributions of NmF2 and describe some ionospheric features, including the equatorial ionization anomaly, the mid-latitude trough, and the wavenumber-four longitudinal structure. Full article

(This article belongs to the Special Issue Latest Results and Developments in GNSS Ionosphere Theory, Methods, Technologies, Applications and Future Challenges)

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15 pages, 5366 KiB

Open AccessArticle

Ionospheric Rayleigh Wave Disturbances Following the 2018 Alaska Earthquake from GPS Observations

by Yuhan Liu and Shuanggen Jin

Remote Sens. 2019, 11(8), 901; https://doi.org/10.3390/rs11080901 - 13 Apr 2019

Cited by 32 | Viewed by 4090

Abstract

Big earthquakes often excite the acoustic resonance between the earth’s surface and the lower atmosphere. The perturbations can propagate upward into the ionosphere and trigger ionospheric anomalies detected by dual-frequency GPS observations, but coseismic ionospheric disturbance (CID) directivity and mechanism are not clear. In this paper, the ionospheric response to the Mw = 7.9 Alaska earthquake on 23 January 2018 is investigated from about 100 continuous GPS stations near the epicenter. The fourth-order zero-phase Butterworth band-pass filter with cutoffs of 2.2 mHz and 8 mHz is applied to obtain the ionospheric disturbances. Results show that the CIDs with an amplitude of up to 0.06 total electron content units (TECU) are detected about 10 min after the Alaska earthquake. The CIDs are as a result of the upward propagation acoustic waves triggered by the Rayleigh wave. The propagation velocities of TEC disturbances are around 2.6 km/s, which agree well with the wave propagation speed of 2.7 km/s detected by the bottom pressure records. Furthermore, the ionospheric disturbances following the 2018 Mw = 7.9 Alaska earthquake are inhomogeneous and directional which is rarely discussed. The magnitude of ionospheric disturbances in the western part of the epicenter is more obvious than in the eastern part. This phenomenon also corresponds to the data obtained from the seismographs and bottom pressure records (BPRs) at the eastern and western side of the epicenter. Full article

(This article belongs to the Special Issue Latest Results and Developments in GNSS Ionosphere Theory, Methods, Technologies, Applications and Future Challenges)

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19 pages, 24306 KiB

Open AccessArticle

A New Global Total Electron Content Empirical Model

by Jiandi Feng, Baomin Han, Zhenzhen Zhao and Zhengtao Wang

Remote Sens. 2019, 11(6), 706; https://doi.org/10.3390/rs11060706 - 24 Mar 2019

Cited by 27 | Viewed by 4849

Abstract

Research on total electron content (TEC) empirical models is one of the important topics in the field of space weather services. Global TEC empirical models based on Global Ionospheric Maps (GIMs) TEC data released by the International GNSS Service (IGS) have developed rapidly in recent years. However, the accuracy of such global empirical models has a crucial restriction arising from the non-uniform accuracy of IGS TEC data in the global scope. Specifically, IGS TEC data accuracy is higher on land and lower over the ocean due to the lack of stations in the latter. Using uneven precision GIMs TEC data as a whole for model fitting is unreasonable. Aiming at the limitation of global ionospheric TEC modelling, this paper proposes a new global ionospheric TEC empirical model named the TECM-GRID model. The model consists of 5183 sections, corresponding to 5183 grid points (longitude 5°, latitude 2.5°) of GIM. Two kinds of single point empirical TEC models, SSM-T1 and SSM-T2, are used for TECM-GRID. According to the locations of grid points, the SSM-T2 model is selected as the sub-model in the Mid-Latitude Summer Night Anomaly (MSNA) region, and SSM-T1 is selected as the sub-model in other regions. The fitting ability of the TECM-GRID model for modelling data was tested in accordance with root mean square (RMS) and relative RMS values. Then, the TECM-GRID model was validated and compared with the NTCM-GL model and Center for Orbit Determination in Europe (CODE) GIMs at time points other than modelling time. Results show that TECM-GRID can effectively describe the Equatorial Ionization Anomaly (EIA) and the MSNA phenomena of the ionosphere, which puts it in good agreement with CODE GIMs and means that it has better prediction ability than the NTCM-GL model. Full article

(This article belongs to the Special Issue Latest Results and Developments in GNSS Ionosphere Theory, Methods, Technologies, Applications and Future Challenges)

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20 pages, 2058 KiB

Open AccessArticle

Cycle Slip Detection during High Ionospheric Activities Based on Combined Triple-Frequency GNSS Signals

by Dongsheng Zhao, Craig M. Hancock, Gethin Wyn Roberts and Shuanggen Jin

Remote Sens. 2019, 11(3), 250; https://doi.org/10.3390/rs11030250 - 26 Jan 2019

Cited by 15 | Viewed by 4840

Abstract

The current cycle slip detection methods of Global Navigation Satellite System (GNSS) were mostly proposed on the basis of assuming the ionospheric delay varying smoothly over time. However, these methods can be invalid during active ionospheric periods, e.g., high Kp index value and scintillations, due to the significant increase of the ionospheric delay. In order to detect cycle slips during high ionospheric activities successfully, this paper proposes a method based on two modified Hatch–Melbourne–Wübbena combinations. The measurement noise in the Hatch–Melbourne–Wübbena combination is minimized by employing the optimally selected combined signals, while the ionospheric delay is detrended using a smoothing technique. The difference between the time-differenced ambiguity of the combined signal and this estimated ionospheric trend is adopted as the detection value, which can be free from ionospheric effect and hold the high precision of the combined signal. Five threshold determination methods are proposed and compared to decide the cycle slip from the magnitude aspect. This proposed method is tested with triple-frequency Global Navigation Satellite System observations collected under high ionospheric activities. Results show that the proposed method can correctly detect and fix cycle slips under disturbed ionosphere. Full article

(This article belongs to the Special Issue Latest Results and Developments in GNSS Ionosphere Theory, Methods, Technologies, Applications and Future Challenges)

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Figure 1
Process of eliminating the outliers in the first-order time-differenced ambiguities. Full article ">Figure 2
The changes of the detection value given by the two combined signals proposed in Zhao et al. [<a href="#B13-remotesensing-11-00250" class="html-bibr">13</a>] with the increase in the strength of ionospheric activities. The top panel shows the elevation angle and the S4 value of satellite PRN 25 from GPS, while the middle and bottom panels show the detection value given by the combined signal (1, −6, 5) and (4, 0, −5), respectively. Full article ">Figure 3
Performance of the proposed method for eliminating outliers. The epochs where the outliers are eliminated are highlighted with gray background. Full article ">Figure 4
Performance of the proposed smoothing technique. The top panel shows ionospheric trend given by LOWESS and RLOWESS, while the bottom panel shows the residuals given by RLOWESS. Full article ">Figure 5
Detection value of the combined signal (−3, 1, 3) and the thresholds determined by the sample variation method (A), GARCH (B), GARCH with the partly fixed method (C), local backward method (D) and local backward with the partly fixed method (E). Full article ">Figure 6
Kp index of 8–11 September 2017. Full article ">Figure 7
PPP coordinate errors using the dataset with artificial cycle slips (green dots) and the dataset where the cycle slips are corrected with the proposed method (red dots). Full article ">

19 pages, 3157 KiB

Open AccessArticle

Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance

by Ke Su, Shuanggen Jin and M. M. Hoque

Remote Sens. 2019, 11(2), 171; https://doi.org/10.3390/rs11020171 - 17 Jan 2019

Cited by 69 | Viewed by 6876

Abstract

Ionospheric delay is a significant error source in multi-GNSS positioning. We present different processing strategies to fully exploit the ionospheric delay effects on multi-frequency and multi-GNSS positioning performance, including standard point positioning (SPP) and precise point positioning (PPP) scenarios. Datasets collected from 10 stations over thirty consecutive days provided by multi-GNSS experiment (MGEX) stations were used for single-frequency SPP/PPP and dual-frequency PPP tests with quad-constellation signals. The experimental results show that for single-frequency SPP, the Global Ionosphere Maps (GIMs) correction achieves the best accuracy, and the accuracy of the Neustrelitz TEC model (NTCM) solution is better than that of the broadcast ionospheric model (BIM) in the E and U components. Eliminating ionospheric parameters by observation combination is equivalent to estimating the parameters in PPP. Compared with the single-frequency uncombined (UC) approach, the average convergence time of PPP with the external ionospheric models is reduced. The improvement in BIM-, NTCM- and GIM-constrained quad-constellation L2 single-frequency PPP was 15.2%, 24.8% and 28.6%, respectively. The improvement in convergence time of dual-frequency PPP with ionospheric models was different for different constellations and the GLONASS-only solution showed the least improvement. The improvement in the convergence time of BIM-, NTCM- and GIM-constrained quad-constellation L1/L2 dual-frequency PPP was 5.2%, 6.2% and 8.5%, respectively, compared with the UC solution. The positioning accuracy of PPP is slightly better with the ionosphere constraint and the performance of the GIM-constrained PPP is the best. The combination of multi-GNSS can effectively improve the positioning performance. Full article

(This article belongs to the Special Issue Latest Results and Developments in GNSS Ionosphere Theory, Methods, Technologies, Applications and Future Challenges)

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