scholarly journals Regional Ionosphere Mapping Using Zero Difference GPS Carrier Phase

2018 ◽  
Author(s):  
Heba Tawfeek ◽  
Ahmed Sedeek ◽  
Mostafa Rabah ◽  
Gamal El-Fiky
Keyword(s):  
Carrier Phase ◽  
Nile Delta ◽  
Mean Difference ◽  
Least Square ◽  
Electron Content ◽  
Dual Frequency ◽  
Total Electron ◽  
Phase Measurements ◽  
Total Electron Contents ◽  
The Mean

Abstract. Ionospheric delay, can be derived from dual frequency GNSS signals, and then converted into the Vertical Total Electron Contents (VTEC) along the signal path. Various models were devised to calculate VTEC. Examples of such models are the polynomial function model and spherical harmonics model. A common hypothesis of these models is that they are constructed based on the assumption that the entire electron content in the ionosphere is concentrated in a single thin shell at a selected height above Earth. The primary goal of the current research to develop an algorithm capable of producing VTEC maps on an hourly basis, using carrier phase observations from dual frequency GPS receiver. The developed algorithm uses a single GPS station (Zero-difference) to map VTEC over a regional area. The carrier phase measurements are much more precise than the code pseudorange measurements, but they contain an ambiguous term. If such ambiguities are fixed, thence the carrier phase measurements become as unambiguous pseudoranges, but accurate at the level of few millimeters. In current research Sequential Least Square Adjustment (SLSA) was considered to fix ambiguity term in carrier phase observations. The proposed algorithm was written using MATLAB and Called (ZDPID). Two GPS stations (ANKR and BSHM) were used from IGS network to evaluate the developed code, VTEC values were estimated over these two stations. Results of the proposed algorithm were compared with the Global Ionosphere Maps (GIMs), which is generally used as a reference. The results show that the mean difference between VTEC from GIM and estimated VTEC at ANKR station is ranging from −2.1 to 3.67 TECU and its RMS is 0.44. The mean difference between VTEC from GIM and estimated VTEC at BSHM station is ranging from −0.29 to 3.65 TECU and its RMS is 0.38. Another three GPS stations in Egypt were used to generate regional ionosphere maps over Nile Delta, Egypt. The mean differences between VTEC from GIM and estimated VTEC at SAID station is ranging from −1.1 to 3.69 TECU and its RMS is 0.37, from −1.29 to 3.27 TECU for HELW station with RMS equal 0.39, and from 0.2 to 4.2 TECU for BORG station with RMS equal 0.46. Therefore, the proposed algorithm can be used to estimate VTEC efficiently.

scholarly journals Vertical Ionosphere Delay Estimation using Zero Difference GPS Phase Observation

2018 ◽  
Author(s):  
Ahmed Elsayed ◽  
Ahmed Sedeek ◽  
Mohamed Doma ◽  
Mostafa Rabah
Keyword(s):  
Carrier Phase ◽  
Free Carrier ◽  
Error Modeling ◽  
Ionospheric Delay ◽  
Electron Content ◽  
Gps Receiver ◽  
Dual Frequency ◽  
Total Electron ◽  
Longitudinal Gradients ◽  
Least Squares Adjustment

Abstract. An apparent delay is occurred in GPS signal due to both refraction and diffraction caused by the atmosphere. The second region of the atmosphere is the ionosphere. The ionosphere is significantly related to GPS and the refraction it causes in GPS signal is considered one of the main source of errors which must be eliminated to determine accurate positions. GPS receiver networks have been used for monitoring the ionosphere for a long time. The ionospheric delay is the most predominant of all the error sources. This delay is a function of the total electron content (TEC). Because of the dispersive nature of the ionosphere, one can estimate the ionospheric delay using the dual frequency GPS. In the current research our primary goal is applying Precise Point Positioning (PPP) observation for accurate ionosphere error modeling, by estimating Ionosphere delay using carrier phase observations from dual frequency GPS receiver. The proposed algorithm was written using MATLAB. The proposed Algorithm depends on the geometry-free carrier-phase observations after detecting cycle slip to estimates the ionospheric delay using a spherical ionospheric shell model, in which the vertical delays are described by means of a zenith delay at the station position and latitudinal and longitudinal gradients. Geometry-free carrier-phase observations were applied to avoid unwanted effects of pseudorange measurements, such as code multipath. The ionospheric estimation in this algorithm is performed by means of sequential least-squares adjustment. Finally, an adaptable user interface MATLAB software are capable of estimating ionosphere delay, ambiguity term and ionosphere gradient accurately.

Vertical ionospheric delay estimation for single-receiver operation

2019 ◽  
Vol 13 (2) ◽  
pp. 81-91 ◽  
Author(s):  
Ahmed Elsayed ◽  
Ahmed Sedeek ◽  
Mohamed Doma ◽  
Mostafa Rabah
Keyword(s):  
Carrier Phase ◽  
Free Carrier ◽  
Error Modeling ◽  
Ionospheric Delay ◽  
Electron Content ◽  
Gps Receiver ◽  
Dual Frequency ◽  
Total Electron ◽  
Longitudinal Gradients ◽  
Least Squares Adjustment

Abstract An apparent delay is occurred in GPS signal due to both refraction and diffraction caused by the atmosphere. The second region of the atmosphere is the ionosphere. The ionosphere is significantly related to GPS and the refraction it causes in GPS signal is considered one of the main source of errors which must be eliminated to determine accurate positions. GPS receiver networks have been used for monitoring the ionosphere for a long time. The ionospheric delay is the most predominant of all the error sources. This delay is a function of the total electron content (TEC). Because of the dispersive nature of the ionosphere, one can estimate the ionospheric delay using the dual frequency GPS. In the current research our primary goal is applying Precise Point Positioning (PPP) observation for accurate ionosphere error modeling, by estimating Ionosphere delay using carrier phase observations from dual frequency GPS receiver. The proposed algorithm was written using MATLAB and was named VIDE program. The proposed Algorithm depends on the geometry-free carrier-phase observations after detecting cycle slip to estimates the ionospheric delay using a spherical ionospheric shell model, in which the vertical delays are described by means of a zenith delay at the station position and latitudinal and longitudinal gradients. Geometry-free carrier-phase observations were applied to avoid unwanted effects of pseudorange measurements, such as code multipath. The ionospheric estimation in this algorithm is performed by means of sequential least-squares adjustment. Finally, an adaptable user interface MATLAB software are capable of estimating ionosphere delay, ambiguity term and ionosphere gradient accurately.

scholarly journals The local ionospheric modeling by integration ground GPS observations and satellite altimetry data

2017 ◽  
Vol 59 (6) ◽  
Author(s):  
Mohammad Ali Sharifi ◽  
Saeed Farzaneh
Keyword(s):  
Satellite Altimetry ◽  
Low Earth Orbit ◽  
Least Square ◽  
Normal Equation ◽  
Electron Content ◽  
Strong Impact ◽  
Dual Frequency ◽  
Total Electron ◽  
Ionosphere Model ◽  
Gps Observations

<p>The free electrons in the ionosphere have a strong impact on the propagation of radio waves. When the signals pass through the ionosphere, both their group and phase velocity are disturbed. Several space geodetic techniques such as satellite altimetry, low Earth orbit (LEO) satellite and very long baseline interferometry (VLBI) can be used to model the total electron content. At present, the classical input data for development of ionospheric models are based on dual-frequency GPS observations, However, a major problem with this observation type is the nonuniform distribution of the terrestrial GPS reference stations with large gaps notably over the sea surface and ocean where only some single stations are located on islands, leading to lower the precision of the model over these areas. In these regions the dual-frequency satellite altimeters provide precise information about the parameters of the ionosphere. Combination of GPS and satellite altimetry observations allows making best use of the advantages of their different spatial and temporal distributions. In this study, the local ionosphere modeling was done by the combination of space geodetic observations using spherical Slepian function. The combination of the data from ground GPS observations over the western part of the USA and the altimetry mission Jason-2 was performed on the normal equation level in the least-square procedure and a least-square variance component estimation (LS-VCE) was applied to take into account the different accuracy levels of the observations. The integrated ionosphere model is more accurate and more reliable than the results derived from the ground GPS observations over the oceans.</p>

scholarly journals Estimating Satellite and Receiver Differential Code Bias Using Relative GPS Network

2018 ◽  
Author(s):  
Alaa A. Elghazouly ◽  
Mohamed I. Doma ◽  
Ahmed A. Sedeek
Keyword(s):  
Weighting Function ◽  
Elevation Angle ◽  
Least Square ◽  
Gps Data ◽  
Electron Content ◽  
Total Electron ◽  
Positioning Systems ◽  
Gps Network ◽  
The Mean ◽  
Mean Differences

Abstract. Precise Total Electron Content (TEC) are required to produce accurate spatial and temporal resolution of Global Ionosphere Maps (GIMs). Receivers and Satellites Differential Code Biases (DCBs) are one of the main error sources in estimating precise TEC from Global Positioning Systems (GPS) data. Recently, researchers are interested in developing models and algorithms to compute DCBs of receivers and satellites close to those computed from the Ionosphere Associated Analysis Centers (IAAC). Here we introduce a MATLAB code called Multi Station DCB Estimation (MSDCBE) to calculate satellites and receivers DCBs from GPS data. MSDCBE based on spherical harmonic function and geometry free combination of GPS carrier phase and pseudo-range code observations and weighted least square were applied to solve observation equations, to improve estimation of DCBs values. There are many factors affecting estimated value of DCBs. The first one is the observations weighting function which depending on the satellite elevation angle. The second factor concerned with estimating DCBs using single GPS Station Precise Point Positioning (PPP) or using GPS network. The third factor is the number of GPS receivers in the network. Results from MSDCBE were evaluated and compared with data from IAAC and other codes like M_DCB and ZDDCBE. The results of weighted (MSDCBE) least square shows an improvement for estimated DCBs, where mean differences from CODE less than 0.746 ns. DCBs estimated from GPS network shows a good agreement with IAAC than DCBs estimated from PPP where the mean differences are less than 0.1477 ns and 1.1866 ns, respectively. The mean differences of computed DCBs improved by increasing number of GPS stations in the network.

scholarly journals Functional model modification of precise point positioning considering the time-varying code biases of a receiver

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Baocheng Zhang ◽  
Chuanbao Zhao ◽  
Robert Odolinski ◽  
Teng Liu
Keyword(s):  
Precise Point Positioning ◽  
Functional Model ◽  
Geodetic Network ◽  
Temporal Variations ◽  
Gps Data ◽  
Electron Content ◽  
Time Varying ◽  
Dual Frequency ◽  
Total Electron ◽  
Point Positioning

AbstractPrecise Point Positioning (PPP), initially developed for the analysis of the Global Positing System (GPS) data from a large geodetic network, gradually becomes an effective tool for positioning, timing, remote sensing of atmospheric water vapor, and monitoring of Earth’s ionospheric Total Electron Content (TEC). The previous studies implicitly assumed that the receiver code biases stay constant over time in formulating the functional model of PPP. In this contribution, it is shown this assumption is not always valid and can lead to the degradation of PPP performance, especially for Slant TEC (STEC) retrieval and timing. For this reason, the PPP functional model is modified by taking into account the time-varying receiver code biases of the two frequencies. It is different from the Modified Carrier-to-Code Leveling (MCCL) method which can only obtain the variations of Receiver Differential Code Biases (RDCBs), i.e., the difference between the two frequencies’ code biases. In the Modified PPP (MPPP) model, the temporal variations of the receiver code biases become estimable and their adverse impacts on PPP parameters, such as ambiguity parameters, receiver clock offsets, and ionospheric delays, are mitigated. This is confirmed by undertaking numerical tests based on the real dual-frequency GPS data from a set of global continuously operating reference stations. The results imply that the variations of receiver code biases exhibit a correlation with the ambient temperature. With the modified functional model, an improvement by 42% to 96% is achieved in the Differences of STEC (DSTEC) compared to the original PPP model with regard to the reference values of those derived from the Geometry-Free (GF) carrier phase observations. The medium and long term (1 × 104 to 1.5 × 104 s) frequency stability of receiver clocks are also significantly improved.

scholarly journals Accuracy Analysis of International Reference Ionosphere 2016 and NeQuick2 in the Antarctic

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1551
Author(s):  
Zihuai Guo ◽  
Yibin Yao ◽  
Jian Kong ◽  
Gang Chen ◽  
Chen Zhou ◽  
...  
Keyword(s):  
Solar Radiation ◽  
International Reference Ionosphere ◽  
Electron Content ◽  
Observation Data ◽  
Dual Frequency ◽  
Total Electron ◽  
International Reference ◽  
Antarctic Continent ◽  
The Antarctic ◽  
Frequency Observation

Global navigation satellite system (GNSS) can provide dual-frequency observation data, which can be used to effectively calculate total electron content (TEC). Numerical studies have utilized GNSS-derived TEC to evaluate the accuracy of ionospheric empirical models, such as the International Reference Ionosphere model (IRI) and the NeQuick model. However, most studies have evaluated vertical TEC rather than slant TEC (STEC), which resulted in the introduction of projection error. Furthermore, since there are few GNSS observation stations available in the Antarctic region and most are concentrated in the Antarctic continent edge, it is difficult to evaluate modeling accuracy within the entire Antarctic range. Considering these problems, in this study, GNSS STEC was calculated using dual-frequency observation data from stations that almost covered the Antarctic continent. By comparison with GNSS STEC, the accuracy of IRI-2016 and NeQuick2 at different latitudes and different solar radiation was evaluated during 2016–2017. The numerical results showed the following. (1) Both IRI-2016 and NeQuick2 underestimated the STEC. Since IRI-2016 utilizes new models to represent the F2-peak height (hmF2) directly, the IRI-2016 STEC is closer to GNSS STEC than NeQuick2. This conclusion was also confirmed by the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) occultation data. (2) The differences in STEC of the two models are both normally distributed, and the NeQuick2 STEC is systematically biased as solar radiation increases. (3) The root mean square error (RMSE) of the IRI-2016 STEC is smaller than that of the NeQuick2 model, and the RMSE of the two modeling STEC increases with solar radiation intensity. Since IRI-2016 relies on new hmF2 models, it is more stable than NeQuick2.

scholarly journals Real-time GNSS precise point positioning for low-cost smart devices

GPS Solutions ◽  
2021 ◽  
Vol 25 (2) ◽  
Author(s):  
Liang Wang ◽  
Zishen Li ◽  
Ningbo Wang ◽  
Zhiyu Wang
Keyword(s):  
Carrier Phase ◽  
Measurement Errors ◽  
Precise Point Positioning ◽  
Low Cost ◽  
Smart Devices ◽  
Dual Frequency ◽  
Static Mode ◽  
Phase Measurements ◽  
Positioning Errors ◽  
Point Positioning

AbstractGlobal Navigation Satellite System raw measurements from Android smart devices make accurate positioning possible with advanced techniques, e.g., precise point positioning (PPP). To achieve the sub-meter-level positioning accuracy with low-cost smart devices, the PPP algorithm developed for geodetic receivers is adapted and an approach named Smart-PPP is proposed in this contribution. In Smart-PPP, the uncombined PPP model is applied for the unified processing of single- and dual-frequency measurements from tracked satellites. The receiver clock terms are parameterized independently for the code and carrier phase measurements of each tracking signal for handling the inconsistency between the code and carrier phases measured by smart devices. The ionospheric pseudo-observations are adopted to provide absolute constraints on the estimation of slant ionospheric delays and to strengthen the uncombined PPP model. A modified stochastic model is employed to weight code and carrier phase measurements by considering the high correlation between the measurement errors and the signal strengths for smart devices. Additionally, an application software based on the Android platform is developed for realizing Smart-PPP in smart devices. The positioning performance of Smart-PPP is validated in both static and kinematic cases. Results show that the positioning errors of Smart-PPP solutions can converge to below 1.0 m within a few minutes in static mode and the converged solutions can achieve an accuracy of about 0.2 m of root mean square (RMS) both for the east, north and up components. For the kinematic test, the RMS values of Smart-PPP positioning errors are 0.65, 0.54 and 1.09 m in the east, north and up components, respectively. Static and kinematic tests both show that the Smart-PPP solutions outperform the internal results provided by the experimental smart devices.

scholarly journals LEAST SQUARE SUPPORT VECTOR MACHINE FOR DETECTION OF TECSEISMO- IONOSPHERIC ANOMALIES ASSOCIATED WITH THE POWERFUL NEPAL EARTHQUAKE (M<sub>w</sub> = 7.5) OF 25 APRIL 2015

2016 ◽  
Vol III-8 ◽  
pp. 3-11
Author(s):  
M. Akhoondzadeh
Keyword(s):  
Support Vector Machine ◽  
Main Shock ◽  
Warning System ◽  
Least Square ◽  
Support Vector ◽  
Electron Content ◽  
Total Electron ◽  
Strong Earthquakes ◽  
Nepal Earthquake ◽  
New Challenges

Due to the irrepalable devastations of strong earthquakes, accurate anomaly detection in time series of different precursors for creating a trustworthy early warning system has brought new challenges. In this paper the predictability of Least Square Support Vector Machine (LSSVM) has been investigated by forecasting the GPS-TEC (Total Electron Content) variations around the time and location of Nepal earthquake. In 77 km NW of Kathmandu in Nepal (28.147° N, 84.708° E, depth&thinsp;=&thinsp;15.0 km) a powerful earthquake of M&lt;sub&gt;w&lt;/sub&gt;&thinsp;=&thinsp;7.8 took place at 06:11:26 UTC on April 25, 2015. For comparing purpose, other two methods including Median and ANN (Artificial Neural Network) have been implemented. All implemented algorithms indicate on striking TEC anomalies 2 days prior to the main shock. Results reveal that LSSVM method is promising for TEC sesimo-ionospheric anomalies detection.

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