Journal ArticleDOI
A Robust Shadow Matching Algorithm for GNSS Positioning: Particle Filter for Shadow Matching
Roi Yozevitch,Boaz Ben Moshe +1 more
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This article is published in Annual of Navigation.The article was published on 2015-09-01. It has received 27 citations till now. The article focuses on the topics: Shadow & Matching (statistics).read more
Citations
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Journal ArticleDOI
LTE receiver design and multipath analysis for navigation in urban environments
Kimia Shamaei,Zaher M. Kassas +1 more
TL;DR: A computationally-efficient receiver, which uses a phase-locked loop (PLL)-aided delay- Locked loop (DLL) to track the received LTE signals is presented, demonstrating robust multipath mitigation for high transmission LTE bandwidths.
Journal ArticleDOI
Analysis and modeling GPS NLOS effect in highly urbanized area
TL;DR: An algorithm to detect NLOS signals from the pseudorange measurements by using a 3D building model, ray-tracing simulation, and known receiver position is developed and an innovative NLOS model using two variables, the elevation angle and the distance between the receiver and building that reflect the NLOS is proposed.
Journal ArticleDOI
Intelligent GNSS/INS integrated navigation system for a commercial UAV flight control system
Guohao Zhang,Li-Ta Hsu +1 more
TL;DR: The results show that the proposed adaptive Kalman filter using random forest with fuzzy logic can achieve a better classification of GNSS accuracy compared to the others.
Journal ArticleDOI
Likelihood-based GNSS positioning using LOS/NLOS predictions from 3D mapping and pseudoranges
Paul D. Groves,Mounir Adjrad +1 more
TL;DR: A likelihood-based 3D-mapping-aided GNSS ranging algorithm is demonstrated that enables signals predicted to be non-line-of-sight (NLOS) to contribute to the position solution without explicitly computing the additional path delay due to NLOS reception, which is computationally expensive.
Journal ArticleDOI
A Robust GNSS LOS/NLOS Signal Classifier
TL;DR: In this article, a novel approach for LOS/NLOS classification utilizing supervised machine learning algorithms is presented, which is able to predict with high certainty (>85 percent) the satellites visibility status in dense urban regions.
References
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Proceedings ArticleDOI
Shadow algorithms for computer graphics
TL;DR: A classification of shadow algorithms delineates three approaches: shadow computation during scanout; division of object surfaces into shadowed and unshadowed areas prior to removal of hidden surfaces; and inclusion of shadow volumes in the object data.
Journal ArticleDOI
Shadow Matching: A New GNSS Positioning Technique for Urban Canyons
TL;DR: In this article, the authors used 3D building models to improve cross-track positioning accuracy in urban canyons by predicting which satellites are visible from different locations and comparing this with the measured satellite visibility to determine position.
Journal ArticleDOI
Multi-Constellation GNSS Performance Evaluation for Urban Canyons Using Large Virtual Reality City Models
TL;DR: In this paper, the authors used 3D building models to predict satellite visibility in urban canyons and evaluated the performance of current and future GNSS in London with decimetre-level accuracy.
Journal ArticleDOI
About Non-Line-Of-Sight Satellite Detection and Exclusion in a 3D Map-Aided Localization Algorithm
Sébastien Peyraud,David Betaille,Stéphane Renault,Miguel Ortiz,Florian Mougel,Dominique Meizel,François Peyret +6 more
TL;DR: Using a 3D urban model to forecast satellite visibility in urban contexts in order to improve GPS localization is the main topic of the present article, with very first promising full-scale test results.
Proceedings ArticleDOI
Urban multipath detection and mitigation with dynamic 3D maps for reliable land vehicle localization
TL;DR: A general and lightweight probabilistic positioning algorithm with integrated multipath detection through 3D environmental building models is presented and it is shown that the proposed system outperforms-in terms of accuracy and integrity-existing methods without introducing additional hardware sensors.