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

Abstract

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

RETRACTED ARTICLE: Likelihood-based GNSS positioning using LOS/NLOS predictions from 3D mapping and pseudoranges

“…It is thus logical to integrate the two techniques. Preliminary results presented in Adjrad and Groves (2016) show that integrated 3DMA GNSS positioning is consistently more accurate than either shadow matching or 3DMA ranging on their own. Optimization of the integration algorithms and further data collection is currently ongoing.…”

“…A brief summary of the approach and some preliminary results have been included in a conference paper alongside other related work (Adjrad and Groves 2016). Here, the final version of the algorithm is described in full, together with the tuning process, and results are presented based on new experimental data collected using 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.…”

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

“…For best performance, both pseudorange and C / N 0 measurements should be used. Therefore, the shadow‐matching algorithm from the previous research is combined with an improved version of the likelihood‐based 3DMA‐ranging algorithm from the previous study using the position‐domain integration algorithm from another study and an improved version of the hypothesis‐domain integration algorithm from . The 3DMA least‐squares GNSS ranging algorithm from an existing research is used for initialization in order to reduce the number of candidate positions that subsequent algorithms must handle.…”

“…Projected coordinates (eastings and northings) are used for the 3D‐mapping while Cartesian ECEF (Earth Centered, Earth Fixed) coordinates are used for the least‐squares position solution. Conversion between Cartesian ECEF and projected coordinates can be simplified using a nearby reference point …”

“…In dense urban environments, 3DMA ranging algorithms substantially improve the positioning accuracy in the along‐street direction, but shadow matching is typically more accurate in the across‐street direction. Therefore, best performance is usually obtained by combining the two techniques together, which can be done with a suitable direction‐dependent weighting of the two position solutions …”