scholarly journals An advanced residual error model for tropospheric delay estimation

GPS Solutions ◽  
2020 ◽  
Vol 24 (4) ◽  
Author(s):  
Szabolcs Rózsa ◽  
Bence Ambrus ◽  
Ildikó Juni ◽  
Pieter Bastiaan Ober ◽  
Máté Mile
Keyword(s):  
Geographical Location ◽  
Residual Error ◽  
Global Navigation Satellite Systems ◽  
Limiting Factors ◽  
Extreme Value Analysis ◽  
Tropospheric Delay ◽  
International Gnss Service ◽  
Value Analysis ◽  
Error Models ◽  
Satellite Systems

Abstract Global navigation satellite systems (GNSS) are widely used for safety-of-life positioning applications. Such applications require high integrity, availability, and continuity of the positioning service. Integrity is assessed by the definition of a protection level, which is an estimation of the maximum positioning error at extremely low probability levels. The emergence of multi-frequency civilian signals and the availability of satellite-based augmentation systems improve the modeling of ionospheric disturbances considerably. As a result, in many applications the tropospheric delay tends to become one of the limiting factors of positioning—especially at low elevation angles. The currently adopted integrity concepts employ a global constant to model the variance of the residual tropospheric delay error. We introduce a new approach to derive residual tropospheric delay error models using the extreme value analysis technique. Seventeen years of global numerical weather model fields are analyzed, and new residual error models are derived for some recently developed tropospheric delay models. Our approach provides models that consider both the geographical location and the seasonal variation of meteorological parameters. Our models are validated with a 17-year-long time series of zenith tropospheric delay estimates as provided by the International GNSS Service. The results show that the developed models are still conservative, while the maximal residual error of the tropospheric delay is still improved by 39–55%. This improvement yields higher service availability and continuity in safety-of-life applications of GNSS.

scholarly journals Assessment of Empirical Troposphere Model GPT3 Based on NGL’s Global Troposphere Products

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3631
Author(s):  
Junsheng Ding ◽  
Junping Chen
Keyword(s):  
Global Navigation Satellite Systems ◽  
Tropospheric Delay ◽  
Strongly Correlated ◽  
International Gnss Service ◽  
Total Delay ◽  
Average Deviation ◽  
Satellite Systems ◽  
Global Average ◽  
The North ◽  
Global Navigation Satellite

Tropospheric delay is one of the major error sources in GNSS (Global Navigation Satellite Systems) positioning. Over the years, many approaches have been devised which aim at accurately modeling tropospheric delays, so-called troposphere models. Using the troposphere data of over 16,000 global stations in the last 10 years, as calculated by the Nevada Geodetic Laboratory (NGL), this paper evaluates the performance of the empirical troposphere model GPT3, which is the latest version of the GPT (Global Pressure and Temperature) series model. Owing to the large station number, long time-span and diverse station distribution, the spatiotemporal properties of the empirical model were analyzed using the average deviation (BIAS) and root mean square (RMS) error as indicators. The experimental results demonstrate that: (1) the troposphere products of NGL have the same accuracy as the IGS (International GNSS Service) products and can be used as a reference for evaluating general troposphere models. (2) The global average BIAS of the ZTD (zenith total delay) estimated by GPT3 is −0.99 cm and the global average RMS is 4.41 cm. The accuracy of the model is strongly correlated with latitude and ellipsoidal height, showing obviously seasonal variations. (3) The global average RMS of the north gradient and east gradient estimated by GPT3 is 0.77 mm and 0.73 mm, respectively, which are strongly correlated with each other, with values increasing from the equator to lower latitudes and decreasing from lower to higher latitudes.

scholarly journals Global Assessment of the GNSS Single Point Positioning Biases Produced by the Residual Tropospheric Delay

2021 ◽  
Vol 13 (6) ◽  
pp. 1202
Author(s):  
Ling Yang ◽  
Jinfang Wang ◽  
Haojun Li ◽  
Timo Balz
Keyword(s):  
Single Point ◽  
Vertical Direction ◽  
Global Scale ◽  
Global Navigation Satellite Systems ◽  
Tropospheric Delay ◽  
Distributed Monitoring ◽  
International Gnss Service ◽  
Satellite Systems ◽  
Single Point Positioning ◽  
Point Positioning

The tropospheric delay is one of the main error sources that degrades the accuracy of Global Navigation Satellite Systems (GNSS) Single Point Positioning (SPP). Although an empirical model is usually applied for correction and thereby to improve the positioning accuracy, the residual tropospheric delay is still drowned in measurement noise, and cannot be further compensated by parameter estimation. How much this type of residual error would sway the SPP positioning solutions on a global scale are still unclear. In this paper, the biases on SPP solutions introduced by the residual tropospheric delay when using nine conventionally Zenith Tropospheric Delay (ZTD) models are analyzed and discussed, including Saastamoinen+norm/Global Pressure and Temperature (GPT)/GPT2/GPT2w/GPT3, University of New Brunswick (UNB)3/UNB3m, European Geostationary Navigation Overlay System (EGNOS) and Vienna Mapping Functions (VMF)3 models. The accuracies of the nine ZTD models, as well as the SPP biases caused by the residual ZTD (dZTD) after model correction are evaluated using International GNSS Service (IGS)-ZTD products from around 400 globally distributed monitoring stations. The seasonal, latitudinal, and altitudinal discrepancies are analyzed respectively. The results show that the SPP solution biases caused by the dZTD mainly occur on the vertical direction, nearly to decimeter level, and significant discrepancies are observed among different models at different geographical locations. This study provides references for the refinement and applications of the nine ZTD models for SPP users.

scholarly journals Estimation and Analysis of GNSS Differential Code Biases (DCBs) Using a Multi-Spacing Software Receiver

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 443
Author(s):  
Ye Wang ◽  
Lin Zhao ◽  
Yang Gao
Keyword(s):  
Primary Source ◽  
Intermediate Frequency ◽  
Global Navigation Satellite Systems ◽  
Electron Content ◽  
Software Receiver ◽  
International Gnss Service ◽  
Total Electron ◽  
Satellite Systems ◽  
Differential Code Biases ◽  
Gnss Observations

In the use of global navigation satellite systems (GNSS) to monitor ionosphere variations by estimating total electron content (TEC), differential code biases (DCBs) in GNSS measurements are a primary source of errors. Satellite DCBs are currently estimated and broadcast to users by International GNSS Service (IGS) using a network of GNSS hardware receivers which are inside structure fixed. We propose an approach for satellite DCB estimation using a multi-spacing GNSS software receiver to analyze the influence of the correlator spacing on satellite DCB estimates and estimate satellite DCBs based on different correlator spacing observations from the software receiver. This software receiver-based approach is called multi-spacing DCB (MSDCB) estimation. In the software receiver approach, GNSS observations with different correlator spacings from intermediate frequency datasets can be generated. Since each correlator spacing allows the software receiver to output observations like a local GNSS receiver station, GNSS observations from different correlator spacings constitute a network of GNSS receivers, which makes it possible to use a single software receiver to estimate satellite DCBs. By comparing the MSDCBs to the IGS DCB products, the results show that the proposed correlator spacing flexible software receiver is able to predict satellite DCBs with increased flexibility and cost-effectiveness than the current hardware receiver-based DCB estimation approach.

An Innovative Approach for Atmospheric Error Mitigation Using New GNSS Signals

2011 ◽  
Vol 64 (S1) ◽  
pp. S211-S232 ◽  
Author(s):  
Lei Yang ◽  
Zeynep Elmas ◽  
Chris Hill ◽  
Marcio Aquino ◽  
Terry Moore
Keyword(s):  
High Accuracy ◽  
Global Navigation Satellite Systems ◽  
Tropospheric Delay ◽  
Atmospheric Effects ◽  
Navigation Satellite ◽  
Navigation Systems ◽  
Satellite Systems ◽  
Error Mitigation ◽  
Satellite Navigation Systems ◽  
Global Navigation Satellite

New signals from the modernised satellite navigation systems (GPS and GLONASS) and the ones that are being developed (COMPASS and GALILEO) will present opportunities for more accurate and reliable positioning solutions. Successful exploitation of these new signals will also enable the development of new markets and applications for difficult environments where the current Global Navigation Satellite Systems (GNSS) cannot provide satisfying solutions. This research is aiming to exploit the improvement in monitoring, modelling and mitigating the atmospheric effects using the increased number of signals from the future satellite systems. Preliminary investigations were conducted on the numerical weather parameter based horizontal tropospheric delay modelling, as well as the ionospheric higher order and scintillation effects. Results from this research are expected to provide a potential supplement to high accuracy positioning techniques.

scholarly journals CODE IGS reference products including Galileo

2020 ◽  
Author(s):  
Lars Prange ◽  
Arturo Villiger ◽  
Stefan Schaer ◽  
Rolf Dach ◽  
Dmitry Sidorov ◽  
...  
Keyword(s):  
Rapid Analysis ◽  
Global Navigation Satellite Systems ◽  
Processing Strategy ◽  
International Gnss Service ◽  
Satellite Systems ◽  
The World ◽  
Product Generation ◽  
Clock Correction ◽  
Global Navigation Satellite ◽  
The Impact

<p>The International GNSS service (IGS) has been providing precise reference products for the Global Navigation Satellite Systems (GNSS) GPS and (starting later) GLONASS since more than 25 years. These orbit, clock correction, coordinate reference frame, troposphere, ionosphere, and bias products are freely distributed and widely used by scientific, administrative, and commercial users from all over the world. The IGS facilities needed for data collection, product generation, product combination, as well as data and product dissemination, are well established. The Center for Orbit Determination in Europe (CODE) is one of the Analysis Centers (AC) contributing to the IGS from the beginning. It generates IGS products using the Bernese GNSS Software.</p><p> </p><p>In the last decade new GNSS (European Galileo and Chinese BeiDou) and regional complementary systems to GPS (Japanese QZSS and Indian IRNSS/NAVIC) were deployed. The existing GNSS are constantly modernized, offering - among others - more stable satellite clocks and new signals. The exploitation of the new data and their integration into the existing IGS infrastructure was the goal of the Multi-GNSS EXtension (MGEX) when it was initiated in 2012. CODE has been participating in the MGEX with its own orbit and clock solution from the beginning. Since 2014 CODE’s MGEX (COM) contribution considers five GNSS, namely GPS, GLONASS, Galileo, BeiDou2 (BDS2), and QZSS. We provide an overview of the latest developments of the COM solution with respect to processing strategy, orbit modelling, attitude modelling, antenna calibrations, handling of code and phase biases, and ambiguity resolution. The impact of these changes on the COM products will be discussed.</p><p> </p><p>Recent assessment showed that especially the Galileo analysis within the MGEX has reached a state of maturity, which is almost comparable to GPS and GLONASS. Based on this finding the IGS decided to consider Galileo in its third reprocessing campaign, which will contribute to the next ITRF. Recognizing the demands expressed by the GNSS community, CODE decided in 2019 to go a step further and consider Galileo also in its IGS RAPID and ULTRA-RAPID reference products. We summarize our experiences from the first months of triple-system (ULTRA)-RAPID analysis including GPS, GLONASS, and Galileo. Finally we provide an outlook of CODE’s IGS analysis with the focus on the new GNSS.</p>

scholarly journals Towards FAIR GNSS data: challenges and open problems

2020 ◽  
Author(s):  
Anna Miglio ◽  
Carine Bruyninx ◽  
Andras Fabian ◽  
Juliette Legrand ◽  
Eric Pottiaux ◽  
...  
Keyword(s):  
Research Policy ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Open Problems ◽  
Data Repositories ◽  
Satellite Systems ◽  
Data Practices ◽  
Metadata Standards ◽  
Global Navigation Satellite ◽  
Gnss Data

<p>Nowadays, we measure positions on Earth’s surface thanks to Global Navigation Satellite Systems (GNSS) e.g. GPS, GLONASS, and Galileo. Activities such as navigation, mapping, and surveying rely on permanent GNSS tracking stations located all over the world.<br>The Royal Observatory of Belgium (ROB) maintains and operates a repository containing data from hundreds of GNSS stations belonging to the European GNSS networks (e.g. EUREF, Bruyninx et al., 2019). </p><p>ROB’s repository contains GNSS data that are openly available and rigorously curated. The curation data include detailed GNSS station descriptions (e.g. location, pictures, and data author) as well as quality indicators of the GNSS observations.</p><p>However, funders and research policy makers are progressively asking for data to be made <em>Findable, Accessible, Interoperable, and Reusable (FAIR)</em> and therefore to increase data transparency, discoverability, interoperability, and accessibility.</p><p>In particular, within the GNSS community, there is no shared agreement yet on the need for making data <em>FAIR</em>. Therefore, turning GNSS data <em>FAIR</em> presents many challenges and, although <em>FAIR</em> data has been included in EUREF’s strategic plan, no practical roadmap has been implemented so far. We will illustrate the specific difficulties and the need for an open discussion including also other communities working on <em>FAIR</em> data.</p><p>For example, making GNSS data easily <em>findable</em> and <em>accessibl</em>e would require to attribute persistent identifiers to the data. It is worth noting that the International GNSS Service (IGS) is only now beginning to consider the attribution of DOIs (Digital Object Identifiers) to GNSS data, mainly to allow data citation and acknowledgement of data providers. Some individual GNSS data repositories are using DOIs (such as UNAVCO, USA).  Are DOIs the only available option or are there more suitable types of URIs (Uniform Resource Identifiers) to consider?</p><p>The GNSS community would greatly benefit from <em>FAIR</em> data practices, as at present, (almost) no licenses have been attributed to GNSS data, data duplication is still an issue, historical provenance information is not available because of data manipulations in data centres, citation of the data providers is far from the rule, etc.</p><p>To move further along the path towards <em>FAIR</em> GNSS data, one would need to implement standardised metadata models to ensure data <em>interoperability</em>, but, as several metadata standards are already in use in various scientific disciplines, which one to choose?</p><p>Then, to facilitate the <em>reuse</em> (and long-term preservation) of GNSS data, all metadata should be properly linked to the corresponding data and additional metadata, such as provenance and license information. The latter is a good example up for discussion: despite the fact that ‘CC BY’ license is already assigned to some of the GNSS data, other licenses might need to be enabled.</p><p> </p><p>Bruyninx C., Legrand J., Fabian A., Pottiaux E. (2019) “GNSS Metadata and Data Validation in the EUREF Permanent Network”. GPS Sol., 23(4), https://doi: 10.1007/s10291-019-0880-9           </p>

Efficient multi-GNSS processing based on raw observations from large global station networks

2020 ◽  
Author(s):  
Sebastian Strasser ◽  
Torsten Mayer-Gürr
Keyword(s):  
Global Navigation Satellite Systems ◽  
Integer Ambiguity ◽  
Normal Equation ◽  
International Gnss Service ◽  
Unknown Parameters ◽  
Large Computer ◽  
Satellite Systems ◽  
Least Squares Adjustment ◽  
Raw Observations ◽  
Gnss Processing

<p>The year 2020 is going to mark the first time of four global navigation satellite systems (i.e., GPS, GLONASS, Galileo, and BeiDou) in full operational capability. Utilizing the various available observation types together in global multi-GNSS processing offers new opportunities, but also poses many challenges. The raw observation approach facilitates the incorporation of any undifferenced and uncombined code and phase observation on any frequency into a combined least squares adjustment. Due to the increased number of observation equations and unknown parameters, using raw observations directly is more computationally demanding than using, for example, ionosphere-free double-differenced observations. This is especially relevant for our contribution to the third reprocessing campaign of the International GNSS Service, where we process observations from up to 800 stations per day to three GNSS constellations at a 30-second sampling. For a single day, this results in more than 200 million raw observations, from which we estimate almost 5 million parameters.</p><p>Processing such a large number of raw observations together is computationally challenging and requires a highly optimized processing chain. In this contribution, we detail the key steps that make such a processing feasible in the context of a distributed computing environment (i.e., large computer clusters). Some of these steps are the efficient setup of observation equations, a suitable normal equation structure, a sophisticated integer ambiguity resolution scheme, automatic outlier downweighting based on variance component estimation, and considerations regarding the estimability of certain parameter groups.</p>

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