Galileo: The Added Value for Integrity in Harsh Environments

Borio, Daniele; Gioia, Ciro

doi:10.3390/s16010111

Cited by 16 publications

(10 citation statements)

References 12 publications

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“…First of all, since obstacles in the urban environment can block GNSS Line-Of-Sight (LOS) signals, the number of satellites in view will be effectively reduced. Yet this situation can be improved by using a multi-constellation receiver in order to obtain sufficient direct-LOS signals for the computation of a position solution [69] [70]. This effect influences also the geometrical distribution of the satellites around the users, i.e., Dilution of Precision (DOP).…”

Section: A Complexity Of Gnss Signal Reception In Urban Environmentmentioning

confidence: 99%

See 1 more Smart Citation

GNSS Position Integrity in Urban Environments: A Review of Literature

Zhu¹,

Marais²,

Betaille³

et al. 2018

IEEE Trans. Intell. Transport. Syst.

350

159

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Section: A Complexity Of Gnss Signal Reception In Urban Environmentmentioning

confidence: 99%

“…Fortunately, this situation can be improved by using multiple GNSS constellations. [70] proposed a modified RAIM algorithm which include geometry and separability checks. This method allows us to detect and exclude erroneous range measurements with the help of GPS/Galileo multi-constellation.…”

Section: B Integrity Monitoring Approaches In Urban Environmentmentioning

confidence: 99%

GNSS Position Integrity in Urban Environments: A Review of Literature

Zhu¹,

Marais²,

Betaille³

et al. 2018

IEEE Trans. Intell. Transport. Syst.

350

159

View full text Add to dashboard Cite

“…Prior literature on GPS-based IM for urban navigation addresses the insufficient availability of GPS measurements by incorporating multiple GNSS constellations (Borio & Gioia, 2016) and characterizing the measurement errors associated with the Non-Line-Of-Sight (NLOS) satellite signals (Groves & Jiang, 2013). However, due to the dense building infrastructure, the geometry of satellites is skewed, which still degrades the confidence in the estimated integrity bounds.…”

Section: Related Workmentioning

confidence: 99%

Integrity monitoring of Graph‐SLAM using GPS and fish‐eye camera

Bhamidipati

Gao

2020

Navigation

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We propose a Simultaneous Localization and Mapping (SLAM)‐based Integrity Monitoring (IM) algorithm using GPS and fish‐eye camera to compute the protection levels while accounting for multiple faults in GPS and vision. We perform graph optimization using GPS pseudoranges, pixel intensities, vehicle dynamics, and satellite ephemeris to simultaneously localize the vehicle, GPS satellites, and key image pixels in the world frame. We estimate the fault mode vector by analyzing the temporal correlation across pseudorange residuals and spatial correlation across pixel intensity residuals. To isolate the vision faults, we develop a superpixel‐based piecewise random sample consensus. For the estimated fault mode, we compute the protection levels by performing worst‐case failure slope analysis on the batch realization of linearized Graph‐SLAM formulation. We perform real‐world experiments in an alleyway in Stanford, California and a semi‐urban area in Champaign, Illinois. We demonstrate higher localization accuracy and tighter protection levels as compared to GPS‐only SLAM‐based IM.

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“…In [16], the GPS/Galileo observations were combined and erroneous measurements were detected using the receiver autonomous integrity monitoring (RAIM) algorithm in an urban area. The results showed that the use of multiple global navigation satellite systems (GNSSs) improves positioning performance in terms of accuracy and availability.…”

Section: Introductionmentioning

confidence: 99%

Performance Enhancement of Land Vehicle Positioning Using Multiple GPS Receivers in an Urban Area

Song

Jee

2016

Sensors

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The Global Positioning System (GPS) is the most widely used navigation system in land vehicle applications. In urban areas, the GPS suffers from insufficient signal strength, multipath propagation and non-line-of-sight (NLOS) errors, so it thus becomes difficult to obtain accurate and reliable position information. In this paper, an integration algorithm for multiple receivers is proposed to enhance the positioning performance of GPS for land vehicles in urban areas. The pseudoranges of multiple receivers are integrated based on a tightly coupled approach, and erroneous measurements are detected by testing the closeness of the pseudoranges. In order to fairly compare the pseudoranges, GPS errors and terms arising due to the differences between the positions of the receivers need to be compensated. The double-difference technique is used to eliminate GPS errors in the pseudoranges, and the geometrical distance is corrected by projecting the baseline vector between pairs of receivers. In order to test and analyze the proposed algorithm, an experiment involving live data was performed. The positioning performance of the algorithm was compared with that of the receiver autonomous integrity monitoring (RAIM)-based integration algorithm for multiple receivers. The test results showed that the proposed algorithm yields more accurate position information in urban areas.

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Galileo: The Added Value for Integrity in Harsh Environments

Cited by 16 publications

References 12 publications

GNSS Position Integrity in Urban Environments: A Review of Literature

GNSS Position Integrity in Urban Environments: A Review of Literature

Integrity monitoring of Graph‐SLAM using GPS and fish‐eye camera

Performance Enhancement of Land Vehicle Positioning Using Multiple GPS Receivers in an Urban Area

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