Prediction of Global Navigation Satellite System Positioning Errors with Guarantees

2021 ◽  
pp. 562-578
Author(s):  
Alejandro Kuratomi ◽  
Tony Lindgren ◽  
Panagiotis Papapetrou
Keyword(s):  
Global Navigation Satellite System ◽  
Satellite System ◽  
Navigation Satellite ◽  
Positioning Errors ◽  
Global Navigation ◽  
Global Navigation Satellite

Influence of Global Navigation Satellite System errors in positioning inventory plots for tree-height distribution studiesThis article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time.

2011 ◽  
Vol 41 (1) ◽  
pp. 11-23 ◽  
Author(s):  
F. Mauro ◽  
R. Valbuena ◽  
J. A. Manzanera ◽  
A. García-Abril
Keyword(s):  
Global Navigation Satellite System ◽  
Tree Height ◽  
Satellite System ◽  
Sensor Data ◽  
Data Sets ◽  
Navigation Satellite ◽  
Height Distribution ◽  
Positioning Errors ◽  
Global Navigation ◽  
Global Navigation Satellite

Validation of predictive models in remote sensing requires a good coregistration of field and sensor data sets. However, previous research has demonstrated that Global Navigation Satellite System survey techniques often produce large positioning errors when applied to areas under forest canopies. In this article, we present a repeatable methodology for analyzing the effect of such errors when validating models that predict tree-height distributions from LiDAR data sets. The method is based on conditional probability theory applied to error positioning and includes an error assessment of the surveying technique. A technical criterion for selecting the plot radius that avoids significant effects of positioning errors was proposed. We demonstrated that for a plot radius greater than 10 m, the effects of positioning errors introduced by a phase-differential device were insignificant when studying forest tree-height distributions.

scholarly journals Multi-Constellation GNSS Performance Evaluation for Urban Canyons Using Large Virtual Reality City Models

2012 ◽  
Vol 65 (3) ◽  
pp. 459-476 ◽  
Author(s):  
Lei Wang ◽  
Paul D Groves ◽  
Marek K Ziebart
Keyword(s):  
Global Navigation Satellite System ◽  
Urban Areas ◽  
Tall Buildings ◽  
Satellite System ◽  
Navigation Satellite ◽  
Positioning Errors ◽  
Building Models ◽  
Geometrical Constraints ◽  
Global Navigation ◽  
Global Navigation Satellite

Positioning using the Global Positioning System (GPS) is unreliable in dense urban areas with tall buildings and/or narrow streets, known as ‘urban canyons’. This is because the buildings block, reflect or diffract the signals from many of the satellites. This paper investigates the use of 3-Dimensional (3-D) building models to predict satellite visibility. To predict Global Navigation Satellite System (GNSS) performance using 3-D building models, a simulation has been developed. A few optimized methods to improve the efficiency of the simulation for real-time purposes were implemented. Diffraction effects of satellite signals were considered to improve accuracy. The simulation is validated using real-world GPS and GLObal NAvigation Satellite System (GLONASS) observations.The performance of current and future GNSS in urban canyons is then assessed by simulation using an architectural city model of London with decimetre-level accuracy. GNSS availability, integrity and precision is evaluated over pedestrian and vehicle routes within city canyons using different combinations of GNSS constellations. The results show that using GPS and GLONASS together cannot guarantee 24-hour reliable positioning in urban canyons. However, with the addition of Galileo and Compass, currently under construction, reliable GNSS performance can be obtained at most, but not all, of the locations in the test scenarios. The modelling also demonstrates that GNSS availability is poorer for pedestrians than for vehicles and verifies that cross-street positioning errors are typically larger than along-street due to the geometrical constraints imposed by the buildings. For many applications, this modelling technique could also be used to predict the best route through a city at a given time, or the best time to perform GNSS positioning at a given location.

scholarly journals Multipath modeling and mitigation by using sparse estimation in global navigation satellite system-challenged urban vehicular environments

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142096869
Author(s):  
Yue Yuan ◽  
Feng Shen ◽  
Dingjie Xu
Keyword(s):  
Global Navigation Satellite System ◽  
Satellite System ◽  
Urban Environments ◽  
Navigation Satellite ◽  
Multipath Mitigation ◽  
Sparse Estimation ◽  
Positioning Errors ◽  
Net Method ◽  
Global Navigation ◽  
Global Navigation Satellite

Multipath interference has been one of the most difficult problems when using global navigation satellite system-based vehicular navigation in urban environments. In this article, we develop a multipath mitigation algorithm exploiting the sparse estimation theory that improves the absolute positioning accuracy in urban environments. The navigation observation model is established by considering the multipath bias as additive positioning errors, and the assumption for the proposed method is that global navigation satellite system signals contaminated due to multipath are the minority among the received signals, which makes the unknown bias vector sparse. We investigated an improved elastic net method to estimate the sparse multipath bias vector, and the global navigation satellite system measurements can be corrected by subtracting the estimated multipath error. The positioning performance of the proposed method is verified by analytical and experimental results.

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.

Implementation of a Dual-Frequency GLONASS and GPS L1 C/A Software Receiver

2010 ◽  
Vol 63 (2) ◽  
pp. 269-287 ◽  
Author(s):  
S. Abbasian Nik ◽  
M. G. Petovello
Keyword(s):  
Global Navigation Satellite System ◽  
Critical Role ◽  
Satellite System ◽  
Navigation Satellite ◽  
Dual Frequency ◽  
Software Receiver ◽  
The Public ◽  
Global Navigation ◽  
Global Navigation Satellite

These days, Global Navigation Satellite System (GNSS) technology plays a critical role in positioning and navigation applications. Use of GNSS is becoming more of a need to the public. Therefore, much effort is needed to make the civilian part of the system more accurate, reliable and available, especially for the safety-of-life purposes. With the recent revitalization of Russian Global Navigation Satellite System (GLONASS), with a constellation of 20 satellites in August 2009 and the promise of 24 satellites by 2010, it is worthwhile concentrating on the GLONASS system as a method of GPS augmentation to achieve more reliable and accurate navigation solutions.

scholarly journals Computation Approach for Quantitative Dielectric Constant from Time Sequential Data Observed by CYGNSS Satellites

2021 ◽  
Vol 13 (11) ◽  
pp. 2032
Author(s):  
Junchan Lee ◽  
Sunil Bisnath ◽  
Regina S.K. Lee ◽  
Narin Gavili Kilane
Keyword(s):  
Dielectric Constant ◽  
Soil Moisture ◽  
Global Navigation Satellite System ◽  
Measurement Data ◽  
Satellite System ◽  
Computation Method ◽  
Navigation Satellite ◽  
Local Soil ◽  
Global Navigation ◽  
Global Navigation Satellite

This paper describes a computation method for obtaining dielectric constant using Global Navigation Satellite System reflectometry (GNSS-R) products. Dielectric constant is a crucial component in the soil moisture retrieval process using reflected GNSS signals. The reflectivity for circular polarized signals is combined with the dielectric constant equation that is used for radiometer observations. Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission, an eight-nanosatellite constellation for GNSS-R, are used for computing dielectric constant. Data from the Soil Moisture Active Passive (SMAP) mission are used to measure the soil moisture through its radiometer, and they are considered as a reference to confirm the accuracy of the new dielectric constant calculation method. The analyzed locations have been chosen that correspond to sites used for the calibration and validation of the SMAP soil moisture product using in-situ measurement data. The retrieved results, especially in the case of a specular point around Yanco, Australia, show that the estimated results track closely to the soil moisture results, and the Root Mean Square Error (RMSE) in the estimated dielectric constant is approximately 5.73. Similar results can be obtained when the specular point is located near the Texas Soil Moisture Network (TxSON), USA. These results indicate that the analysis procedure is well-defined, and it lays the foundation for obtaining quantitative soil moisture content using the GNSS reflectometry results. Future work will include applying the computation product to determine the characteristics that will allow for the separation of coherent and incoherent signals in delay Doppler maps, as well as to develop local soil moisture models.

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