scholarly journals Kinematic Precise Point Positioning Using Multi-Constellation Global Navigation Satellite System (GNSS) Observations

2017 ◽  
Vol 6 (1) ◽  
pp. 6 ◽  
Author(s):  
Xidong Yu ◽  
Jingxiang Gao
Keyword(s):  
Global Navigation Satellite System ◽  
Precise Point Positioning ◽  
Satellite System ◽  
Navigation Satellite ◽  
Global Navigation ◽  
Point Positioning ◽  
Global Navigation Satellite ◽  
Gnss Observations

Improvement of Multi-GNSS Precise Point Positioning Performances with Real Meteorological Data

2018 ◽  
Vol 71 (6) ◽  
pp. 1363-1380 ◽  
Author(s):  
Ke Su ◽  
Shuanggen Jin
Keyword(s):  
Global Navigation Satellite System ◽  
Precise Point Positioning ◽  
Meteorological Data ◽  
Satellite System ◽  
Navigation Satellite ◽  
Positioning Accuracy ◽  
Global Pressure ◽  
Global Navigation ◽  
Point Positioning ◽  
Global Navigation Satellite

Tropospheric delay is one of the main error sources in Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP). Zenith Hydrostatic Delay (ZHD) accounts for 90% of the total delay. This research focuses on the improvements of ZHD from tropospheric models and real meteorological data on the PPP solution. Multi-GNSS PPP experiments are conducted using the datasets collected at Multi-GNSS Experiments (MGEX) network stations. The results show that the positioning accuracy of different GNSS PPP solutions using the meteorological data for ZHD correction can achieve an accuracy level of several millimetres. The average convergence time of a PPP solution for the BeiDou System (BDS), the Global Positioning System (GPS), Global Navigation Satellite System of Russia (GLONASS), BDS+GPS, and BDS+GPS+GLONASS+Galileo are 55·89 min, 25·88 min, 33·30 min, 20·50 min and 15·71 min, respectively. The results also show that atmospheric parameters provided by real meteorological data have little effect on the horizontal components of positioning compared to the meteorological model, while in the vertical component, the positioning accuracy is improved by 90·6%, 33·0%, 22·2% and 19·8% compared with the standard atmospheric model, University of New Brunswick (UNB3m) model, Global Pressure and Temperature (GPT) model, and Global Pressure and Temperature-2 (GPT2) model and the convergence times are decreased 51·2%, 32·8%, 32·5%, and 32·3%, respectively.

scholarly journals Multi-GNSS Combined Precise Point Positioning Using Additional Observations with Opposite Weight for Real-Time Quality Control

2019 ◽  
Vol 11 (3) ◽  
pp. 311 ◽  
Author(s):  
Wenju Fu ◽  
Guanwen Huang ◽  
Yuanxi Zhang ◽  
Qin Zhang ◽  
Bobin Cui ◽  
...  
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Precise Point Positioning ◽  
Satellite System ◽  
Convergence Time ◽  
Navigation Satellite ◽  
Positioning Accuracy ◽  
Global Navigation ◽  
Point Positioning ◽  
Global Navigation Satellite

The emergence of multiple global navigation satellite systems (multi-GNSS), including global positioning system (GPS), global navigation satellite system (GLONASS), Beidou navigation satellite system (BDS), and Galileo, brings not only great opportunities for real-time precise point positioning (PPP), but also challenges in quality control because of inevitable data anomalies. This research aims at achieving the real-time quality control of the multi-GNSS combined PPP using additional observations with opposite weight. A robust multiple-system combined PPP estimation is developed to simultaneously process observations from all the four GNSS systems as well as single, dual, or triple systems. The experiment indicates that the proposed quality control can effectively eliminate the influence of outliers on the single GPS and the multiple-system combined PPP. The analysis on the positioning accuracy and the convergence time of the proposed robust PPP is conducted based on one week’s data from 32 globally distributed stations. The positioning root mean square (RMS) error of the quad-system combined PPP is 1.2 cm, 1.0 cm, and 3.0 cm in the east, north, and upward components, respectively, with the improvements of 62.5%, 63.0%, and 55.2% compared to those of single GPS. The average convergence time of the quad-system combined PPP in the horizontal and vertical components is 12.8 min and 12.2 min, respectively, while it is 26.5 min and 23.7 min when only using single-GPS PPP. The positioning performance of the GPS, GLONASS, and BDS (GRC) combination and the GPS, GLONASS, and Galileo (GRE) combination is comparable to the GPS, GLONASS, BDS and Galileo (GRCE) combination and it is better than that of the GPS, BDS, and Galileo (GCE) combination. Compared to GPS, the improvements of the positioning accuracy of the GPS and GLONASS (GR) combination, the GPS and Galileo (GE) combination, the GPS and BDS (GC) combination in the east component are 53.1%, 43.8%, and 40.6%, respectively, while they are 55.6%, 48.1%, and 40.7% in the north component, and 47.8%, 40.3%, and 34.3% in the upward component.

scholarly journals Precise Point Positioning Algorithm for Pseudolite Combined with GNSS in a Constrained Observation Environment

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1120 ◽  
Author(s):  
Chuanzhen Sheng ◽  
Xingli Gan ◽  
Baoguo Yu ◽  
Jingkui Zhang
Keyword(s):  
High Precision ◽  
Global Navigation Satellite System ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Satellite System ◽  
Navigation Satellite ◽  
Global Navigation ◽  
Point Positioning ◽  
Global Navigation Satellite ◽  
Better Than

In urban canyon environments, Global Navigation Satellite System (GNSS) satellites are heavily obstructed with frequent rise and fall and severe multi-path errors induced by signal reflection, making it difficult to acquire precise, continuous, and reliable positioning information. To meet imperative demands for high-precision positioning of public users in complex environments, like urban canyons, and to solve the problems for GNSS/pseudolite positioning under these circumstances, the Global Navigation Satellite System (GNSS) Precision Point Positioning (PPP) algorithm combined with a pseudolite (PLS) was introduced. The former problems with the pseudolite PPP technique with distributed pseudo-satellites, which relies heavily on known points for initiation and prerequisite for previous high-precision time synchronization, were solved by means of a real-time equivalent clock error estimation algorithm, ambiguity fixing, and validation method. Experiments based on a low-cost receiver were performed, and the results show that in a weak obstructed environment with low-density building where the number of GNSS satellites was greater than seven, the accuracy of pseudolite/GNSS PPP with fixed ambiguity was better than 0.15 m; when there were less than four GNSS satellites in severely obstructed circumstances, it was impossible to obtain position by GNSS alone, but with the support of a pseudolite, the accuracy of PPP was able to be better than 0.3 m. Even without GNSS, the accuracy of PPP could be better than 0.5 m with only four pseudolites. The pseudolite/GNSS PPP algorithm presented in this paper can effectively improve availability with less GNSS or even without GNSS in constrained environments, like urban canyons in cities.

scholarly journals Estimating the Impact of Global Navigation Satellite System Horizontal Delay Gradients in Variational Data Assimilation

2018 ◽  
Vol 11 (1) ◽  
pp. 41 ◽  
Author(s):  
Florian Zus ◽  
Jan Douša ◽  
Michal Kačmařík ◽  
Pavel Václavovic ◽  
Galina Dick ◽  
...  
Keyword(s):  
Global Navigation Satellite System ◽  
Precise Point Positioning ◽  
Satellite System ◽  
Horizontal Resolution ◽  
Navigation Satellite ◽  
Weather Model ◽  
Single Station ◽  
Global Navigation ◽  
Global Navigation Satellite ◽  
The Impact

We developed operators to assimilate Global Navigation Satellite System (GNSS) Zenith Total Delays (ZTDs) and horizontal delay gradients into a numerical weather model. In this study we experiment with refractivity fields derived from the Global Forecast System (GFS) available with a horizontal resolution of 0.5°. We begin our investigations with simulated observations. In essence, we extract the tropospheric parameters from the GFS analysis, add noise to mimic observation errors and assimilate the simulated observations into the GFS 24h forecast valid at the same time. We consider three scenarios: (1) the assimilation of ZTDs (2) the assimilation of horizontal delay gradients and (3) the assimilation of both ZTDs and horizontal delay gradients. The impact is measured by utilizing the refractivity fields. We find that the assimilation of the horizontal delay gradients in addition to the ZTDs improves the refractivity field around 800 hPa. When we consider a single station there is a clear improvement when horizontal delay gradients are assimilated in addition to the ZTDs because the horizontal delay gradients contain information that is not contained in the ZTDs. On the other hand, when we consider a dense station network there is not a significant improvement when horizontal delay gradients are assimilated in addition to the ZTDs because the horizontal delay gradients do not contain information that is not already contained in the ZTDs. Finally, we replace simulated by real observations, that is, tropospheric parameters from a Precise Point Positioning solution provided with the G-Nut/Tefnut software, in order to show that the GFS 24h forecast is indeed improved when GNSS horizontal delay gradients are assimilated in addition to GNSS ZTDs; for the considered station (Potsdam, Germany) and period (June and July, 2017) we find an improvement in the retrieved refractivity of up to 4%.

scholarly journals Empirical Model for Total Precipitable Water Retrieval from Ground-based GNSS Observations in Thailand

2020 ◽  
Author(s):  
Weeranat Phasamak ◽  
Seubson Soisuvarn ◽  
Yuttapong Rangsanseri
Keyword(s):  
Real Time ◽  
Global Navigation Satellite System ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Satellite System ◽  
Navigation Satellite ◽  
Total Precipitable Water ◽  
Global Navigation ◽  
Global Navigation Satellite ◽  
Gnss Observations

Retrieval of Total Precipitable Water (TPW) using ground-based Global Navigation Satellite System (GNSS) observations is a challenging task due to its real‐time and high temporal resolution. In this paper, we present a method for establishing an analytic model for retrieving the total precipitable water (TPW) based on Global Navigation Satellite System (GNSS) observations over one-year period from 12 distributed stations across Thailand. The derived zenith total delay (ZTD) at all stations agrees well with the TPW data available from Global Data Assimilation System (GDAS) Numerical Weather Prediction (NWP) model. At first, a unique relationship between the ZTD and the TPW was established by taking into account of the variation of station altitudes. In addition, the bias correction technique using probability distribution function (PDF) matching was also applied to improve the final model. The inversion model of TPW from ZTD is then easily obtained using a numerical technique. The performance of our method has been successfully evaluated on an independent test data. This model can be useful in the further near real-time TPW measurements from widely available GNSS receivers.

scholarly journals OutFin, a multi-device and multi-modal dataset for outdoor localization based on the fingerprinting approach

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fahad Alhomayani ◽  
Mohammad H. Mahoor
Keyword(s):  
Global Navigation Satellite System ◽  
Urban Areas ◽  
Satellite System ◽  
Reference Points ◽  
Navigation Satellite ◽  
Data Types ◽  
Entry Barrier ◽  
Promising Alternative ◽  
Global Navigation ◽  
Global Navigation Satellite

AbstractIn recent years, fingerprint-based positioning has gained researchers’ attention since it is a promising alternative to the Global Navigation Satellite System and cellular network-based localization in urban areas. Despite this, the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions constitutes a high entry barrier for studies. As an effort to overcome this barrier and foster new research efforts, this paper presents OutFin, a novel dataset of outdoor location fingerprints that were collected using two different smartphones. OutFin is comprised of diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 reference points. Each site is different in terms of its visibility to the Global Navigation Satellite System and reference points’ number, arrangement, and spacing. Before OutFin was made available to the public, several experiments were conducted to validate its technical quality.

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