Smartphone Shadow Matching for Better Cross-street GNSS Positioning in Urban Environments

Wang, Lei; Groves, Paul D.; Ziebart, Marek

doi:10.1017/s0373463314000836

Cited by 137 publications

(148 citation statements)

References 25 publications

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“…This enables across-street position accuracies of a few meters to be achieved in dense urban areas (Groves 2011;Ben-Moshe et al 2011;Suzuki and Kubo 2012;Wang et al 2013Wang et al , 2015Yozevitch and BenMoshe 2015). However, the focus here is on 3D-mappingaided GNSS ranging.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Likelihood-based GNSS positioning using LOS/NLOS predictions from 3D mapping and pseudoranges

Groves

Adjrad

2017

GPS Solut

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The accuracy of conventional global navigation satellite systems (GNSS) positioning in dense urban areas is severely degraded due to blockage and reflection of the signals by the surrounding buildings. By using 3D mapping of the buildings to aid GNSS positioning, the accuracy can be substantially improved. However, positioning performance must be balanced against computational load. Here, a likelihood-based 3D-mapping-aided (3DMA) 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. Likelihoods for an array of candidate positions are computed based on the difference between the measured and predicted pseudoranges. However, a skewed distribution is assumed for those signals predicted to be NLOS on the basis that the ensuing ranging errors are always positive. An overall position solution is then extracted from the likelihood surface. GNSS measurement data have been collected at several locations in both traditional and modern dense urban environments. Horizontal root-mean-square single-epoch position accuracies of 4.7, 5.6 and 6.5 m are obtained using, respectively, a Leica Viva geodetic receiver, a u-blox EVK M8T consumer-grade receiver and a Nexus 9 tablet incorporating a smartphone GNSS antenna and a GNSS chipset that outputs pseudoranges. The corresponding accuracies using single-epoch conventional GNSS positioning are 20.5, 23.0 and 28.4 m, about a factor of four larger. The 3DMA GNSS algorithms have also been implemented in real time on a Raspberry Pi 3 at a 1-Hz update rate.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Likelihood-based GNSS positioning using LOS/NLOS predictions from 3D mapping and pseudoranges

Groves

Adjrad

2017

GPS Solut

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“…This paper presents the first integration of GNSS shadow matching with 3DMA ranging, demonstrating its benefit. The shadow matching algorithm, based on the design presented in Wang et al (2015), uses the SNR measurements to compute a position solution. The 3DMA ranging algorithm, described in Adjrad and Groves (2017), uses the pseudo-ranges to compute the position and incorporates the following techniques:…”

Section: O U N I R a D J R A D A N D Pa U L D G R O V E S Vol 71mentioning

confidence: 99%

Intelligent Urban Positioning: Integration of Shadow Matching with 3D-Mapping-Aided GNSS Ranging

Adjrad

Groves

2017

J. Navigation

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In dense urban areas, conventional Global Navigation Satellite Systems (GNSS) positioning can exhibit errors of tens of metres due to the obstruction and reflection of the signals by the surrounding buildings. By using Three-Dimensional (3D) mapping of the buildings, the accuracy can be significantly improved. This paper demonstrates the first integration of GNSS shadow matching with 3D-mapping-aided GNSS ranging. The integration is performed in the position domain, whereby separate ranging and shadow matching position solutions are computed, then combined using direction-dependent weighting. Two weighting strategies are compared, one based on the computation of ranging-based and shadow matching position error covariance matrices, and a deterministic approach based on the street azimuth. Using experimental data collected from a u-blox GNSS receiver, it is shown that both integrated position solutions are significantly more accurate than either shadow matching or 3D-mapping-aided ranging on their own. The overall Root Mean Square (RMS) horizontal accuracy obtained using covariance-based weighting was 6·1 m, a factor of four improvement on the 25·9 m obtained using conventional GNSS positioning. Results are also presented using smartphone data, where shadow matching is integrated with conventional GNSS positioning. K E Y

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“…Street height climbing generate building boundaries to predict satellite visibility [25,26]. Its application in land vehicle localization is discussed and improved in [27].…”

Section: Viaduct Heightmentioning

confidence: 99%

Intelligent Viaduct Recognition and Driving Altitude Determination Using GPS Data

Hsu

Kamijo

2017

IEEE Trans. Intell. Veh.

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Abstract-The rapid development of road networks in highly urbanized cities requires a substantial number of viaducts to reduce the increasing traffic burden on urban highways. Unfortunately, this road design deteriorates the performance of global positioning system (GPS) navigators due to the signal blockage between satellite and receiver. As a result, it is difficult for GPS navigators to determine whether the vehicle is driving on the street or viaduct. Misleading information could confuse drivers and lead them to drive irregularly, which is dangerous in heavy traffic. This paper proposes a novel classification algorithm based on the fact that different satellite signals and conditions can be observed in the on-street and on-viaduct cases by the implementation of dynamic Bayesian network (DBN) to distinguish the driving area of a vehicle. In addition, the proposed DBN can also accurately estimate the driving altitude of the vehicle according to the experiment results.

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Smartphone Shadow Matching for Better Cross-street GNSS Positioning in Urban Environments

Cited by 137 publications

References 25 publications

RETRACTED ARTICLE: Likelihood-based GNSS positioning using LOS/NLOS predictions from 3D mapping and pseudoranges

RETRACTED ARTICLE: Likelihood-based GNSS positioning using LOS/NLOS predictions from 3D mapping and pseudoranges

Intelligent Urban Positioning: Integration of Shadow Matching with 3D-Mapping-Aided GNSS Ranging

Intelligent Viaduct Recognition and Driving Altitude Determination Using GPS Data

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