Direct Position Estimation Utilizing Non-Line-of-Sight (NLOS) GPS Signals

Ng, Yuting; Gao, Grace Xingxin

doi:10.33012/2016.14855

Cited by 18 publications

(21 citation statements)

References 16 publications

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“…3DMA GNSS ranging has also been combined with “direct positioning,” which uses the receiver correlator outputs to score an array of position hypothesis . A similar approach determines position by matching the predicted path delay(s) of the reflected component(s) at each candidate position with the outputs of a signal correlator bank …”

Section: Review Of 3d‐mapping–aided Gnssmentioning

confidence: 99%

Performance assessment of 3D‐mapping–aided GNSS part 1: Algorithms, user equipment, and review

Groves

Adjrad

2019

NAVIGATION

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A full performance assessment of 3D‐mapping–aided (3DMA) GNSS in dense urban areas is presented. This first part of a two‐part paper focuses on the effects of algorithm design and user equipment, based on data collected in London using Leica Viva GS15 and u‐blox EVK‐M8T GNSS receivers and a Nexus 9 Android tablet. Best performance is obtained by combining shadow matching with likelihood‐based 3DMA GNSS ranging using hypothesis‐domain integration. Improved versions of the algorithms, together with a comprehensive tuning process, are described. Root mean square horizontal position errors obtained using data from Leica, u‐blox, and Nexus receivers are 3.5, 4.7, and 4.9 m, respectively, compared with 23.6, 26.4, and 31.0 m using conventional GNSS positioning, about a factor of six improvement. Optimal algorithm tuning depends on the environment and the impact of varying grid spacing of the candidate positions is assessed. Algorithms have also been shown to operate in real time on both a Raspberry Pi 3 and a Samsung Galaxy S8+ Android smartphone.

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Section: Review Of 3d‐mapping–aided Gnssmentioning

confidence: 99%

Performance assessment of 3D‐mapping–aided GNSS part 1: Algorithms, user equipment, and review

Groves

Adjrad

2019

NAVIGATION

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“…3DMA GNSS ranging has also been combined with 'direct positioning' which uses the receiver correlator outputs to score an array of position hypothesis [30].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Urban Positioning using Shadow Matching and GNSS Ranging Aided by 3D Mapping

Adjrad¹,

Groves²

2016

Proceedings of the 29th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 201

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His current research interests are on Intelligent Urban Positioning (IUP) exploiting conventional GNSS technology augmented by 3D models of urban environments. He has multidisciplinary research experience in industry and academic institutions working on topics such as ultrawideband technology, GNSS, satellite engineering, radar, biomedical engineering and transport engineering He holds a State Engineering degree in electronics, Magister in signal and communication, and State Doctorate in signal and systems from Ecole Nationale Polytechnique d'Alger, Algeria.

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“…Also, during multipath, instead of estimating the GPS satellite location that agrees with the broadcast ephemeris, our framework intelligently localizes the landmark to a virtual location that complies with the received GPS pseudorange. The concept of estimating virtual satellite location, which corresponds to the reflection of an actual GPS satellite across a building surface, has been demonstrated in existing literature (Ng & Gao, 2016). Therefore, unlike traditional GPS algorithms, the flexibility provided by our SLAM-based FDI in localizing the GPS satellites improves the number of reliable measurements available for navigation.…”

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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Direct Position Estimation Utilizing Non-Line-of-Sight (NLOS) GPS Signals

Abstract: Her research interests are systems, signals, control, and robotics. She is a senior member of IEEE and a member of ION.

Cited by 18 publications

References 16 publications

Performance assessment of 3D‐mapping–aided GNSS part 1: Algorithms, user equipment, and review

Performance assessment of 3D‐mapping–aided GNSS part 1: Algorithms, user equipment, and review

Intelligent Urban Positioning using Shadow Matching and GNSS Ranging Aided by 3D Mapping

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

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