3D-mapping-aided GNSS exploiting Galileo for better accuracy in dense urban environments
Abstract: Conventional single-epoch GNSS positioning in dense urban areas can exhibit errors of tens of meters due to blockage and reflection of signals by the surrounding buildings. Here, we present the first implementation of 3D-mapping-aided (3DMA) GNSS to use Galileo signals as well as GPS and GLONASS. Our intelligent urban positioning (IUP) concept combines conventional ranging-based GNSS positioning enhanced by 3D mapping with the GNSS shadow-matching technique. Shadow matching (SM) determines position by comparing the measured signal availability with that predicted over a grid of candidate positions using 3D mapping. Thus, IUP uses both pseudo-range and signal-to-noise measurements to determine position. All algorithms incorporate terrain-height aiding and use measurements from a single epoch in time.
Summary (2 min read)
- Dense urban areas can exhibit errors of tens of meters due to blockage and reflection of signals by the surrounding buildings.
- Shadow matching (SM) determines position by comparing the measured signal availability with that predicted over a grid of candidate positions using 3D mapping.
- NLOS signals exhibit positive ranging errors corresponding to the path delay (the difference between the reflected and direct paths).
- In dense urban areas where the signal geometry is poor, it can improve the horizontal accuracy by almost a factor of two .
- 3DMA GNSS ranging has also been combined with ‘direct positioning’ which uses the receiver correlator outputs to score an array of position hypothesis .
A. Least-Squares 3DMA GNSS Ranging
- The LS-3DMA algorithm comprises six steps: 1) A search area is determined using the conventional GNSS position solution on the first iteration and the previous solution on subsequent iterations, together with an appropriate confidence interval.
- 3) A consistency-checking process is applied to the ranging measurements, using the direct LOS probabilities from the 3D mapping.
- 4) The set of signals resulting from the consistency checking process is subjected to a weighting strategy based on the previously determined LOS probabilities and carrierpower-to-noise-density ratio, C/N0.
- The algorithm is then iterated several times to improve the position solution.
- Full details are presented in  (final version) and  (preliminary version).
B. Likelihood-based 3DMA GNSS Ranging
- In LB-3DMA, an array of candidate position hypotheses are scored according to the correspondence between the predicted and measured pseudo-ranges.
- Elsewhere, a conventional symmetric normal distribution is assumed.
- Other LB-3DMA algorithms based on candidate position hypothesis scoring have been described in the literature.
- In  and , pseudo-ranges predicted to be NLOS are corrected using path delays predicted from the 3D mapping.
- 3) At each candidate position, the direct LOS range to each satellite is computed.
D. Hypothesis-Domain Integration
- Both shadow matching and LB-3DMA can produce multimodal position distributions where there is a good match between predictions and measurements in more than one part of the search area.
- The sites were paired with data collected on opposite sides of the street on the edge of the footpath next to the road .
- The second dataset was then used for testing the positioning algorithms.
- Overall, these results show that the number of available satellites is not the main factor limiting shadow matching performance.
- The intelligent urban positioning algorithms were tested using data recorded exploiting a u-blox EVK M8T consumergrade GNSS receiver, collecting concurrent GPS, GLONASS and Galileo signals, at 18 locations in the City of London area.
- This work is funded by the Engineering and Physical Sciences Research Council project EP/L018446/1, Intelligent Positioning in Cities using GNSS and Enhanced 3D Mapping.
- The project is also supported by Ordnance Survey, ublox and the Royal National Institute for Blind People.
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Cites background from "3D-mapping-aided GNSS exploiting Ga..."
...…buildings create conditions for multiple conflicting surveillance signals (e.g. GPS multipath) and the loss of communication due to line-of-sight blockages (Adjrad and Groves, 2017); and the high population density increases the risk of fatalities if a drone should fail (Weibel and Hansman, 2004)....
...GPS multipath) and the loss of communication due to line-of-sight blockages (Adjrad and Groves, 2017); and the high population density increases the risk of fatalities if a drone should fail (Weibel and Hansman, 2004)....
Cites background or methods from "3D-mapping-aided GNSS exploiting Ga..."
...In other words, they can only mitigate multipath signals and detect NLoS ....
...are totally blocked and unavailable for navigation use ....
...Consistency check  adopted in , ,  Greedy & Exhaustive FDE C/No -based weighting, chi-square threshold 29% and 31% improvement for Greedy and Exhaustive FDE (mean error) 8% in deep urban (Exhaustive FDE) Low...
...The work in  was extended in  by using a...
...3D Mapping 3DMA + Shadow matching Terrain height aiding, Hypothesis-domain integration Use of building boundaries, Galileo signals 3D Maps for satellite visibility, Consistency checks 86% improvement 3-constellation 86% improvement 2-constellation (RMS horizontal accuracy) Med...
"3D-mapping-aided GNSS exploiting Ga..." refers methods in this paper
...The shadow-matching technique  determines position by comparing the measured signal availability and strength with predictions made using a 3D city model over a range of candidate positions....
"3D-mapping-aided GNSS exploiting Ga..." refers background or methods in this paper
...Several groups have extended 3D-mapping-aided GNSS ranging by using the 3D city model to predict the path delay of the NLOS signals across an array of candidate positions ....
...A single-epoch positioning accuracy of 4m has been reported ....
...In  and , pseudo-ranges predicted to be NLOS are corrected using path delays predicted from the 3D mapping....
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Q1. What are the contributions mentioned in the paper "3d-mapping-aided gnss exploiting galileo for better accuracy in dense urban environments" ?
Here, the authors present the first implementation of 3D-mapping-aided ( 3DMA ) GNSS to use Galileo signals as well as GPS and GLONASS. The 3DMA ranging algorithms presented in this work are based on computing the likelihoods of a grid of candidate position hypotheses. A strategy for integrating LB3DMA with shadow matching is presented.