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• W2283358568 abstract "The poor performance of global navigation satellitesystems (GNSS) user equipment in urban canyons is awell-known problem, especially in the cross-streetdirection. A new approach, shadow matching, has recentlybe proposed to improve the cross-street accuracy usingGNSS, assisted by knowledge derived from 3D models ofthe buildings close to the user of navigation devices. Inthis work, four contributions have been made. Firstly, anew scoring scheme, a key element of the algorithm toweight candidate user locations, is proposed. The newscheme takes account of the effects of satellite signaldiffraction and reflection by weighting the scores based ondiffraction modelling and signal-to-noise ratio (SNR).Furthermore, an algorithm similar to k-nearest neighbours(k-NN) is developed to interpolate the position solutionover an extensive grid. The process of generating this gridof building boundaries is also optimized. Finally, insteadof just testing at two locations as in the earlier work, realworldGNSS data has been collected at 22 differentlocations in this work, providing a more comprehensiveand statistical performance analysis of the new shadowmatchingalgorithm.In the experimental verification, the new scoring schemeimproves the cross street accuracy with an average bias of1.61 m, with a 9.4% reduction compared to the originalSS22 scoring scheme. Similarly, the cross street RMS is2.86 m, a reduction of 15.3%. Using the new scoringscheme, the success rate for determining the correct side ofa street is 89.3%, 3.6% better than using the previousscoring scheme; the success rate of distinguishing thefootpath from a traffic lane is 63.6% of the time, 6.8%better than using the previous scoring scheme." @default.
• W2283358568 created "2016-06-24" @default.
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• W2283358568 date "2012-09-30" @default.
• W2283358568 modified "2023-09-28" @default.
• W2283358568 title "GNSS Shadow Matching: Improving Urban Positioning Accuracy Using a 3D City Model with Optimized Visibility Prediction Scoring" @default.
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