Abstract

Global Navigation Satellites Systems (GNSS) is frequently used for positioning services in various applications, e.g., pedestrian and vehicular navigation. However, it is well-known that GNSS positioning performs unreliably in urban environments. GNSS shadow matching is a method of improving accuracy in the cross-street direction. Initial position and classification of observed satellite visibility between line-of-sight (LOS) and non-line-of-sight (NLOS) are essential for its performance. For the conventional LOS/NLOS classification, the classifiers are based on a single feature, extracted from raw GNSS measurements, such as signal noise ratio, pseudorange, elevation angle, etc. Especially in urban canyons, these measurements are unstable and unreliable due to the signal reflection and refraction from the surrounding buildings. Besides, the conventional least square approach for positioning is insufficient to provide accurate initialization for shadow matching in urban areas. In our study, shadow matching is improved using the initial position from robust estimator and the satellite visibility determined by support vector machine (SVM). The robust estimator has an improved positioning accuracy and the classification rate of SVM classification can reach 91.5% in urban scenarios. An important issue is related to satellites with ultra-high or low elevation angles and satellites near the building boundary that are very likely to be misclassified. By solving this problem, the SVM classification shows the potential of about 90% classification accuracy for various urban cases. With the help of these approaches, the shadow matching has a mean error of 10.27 m with 1.44 m in the cross-street direction; these performances are suitable for urban positioning.

Highlights

  • Positioning has become a part of our everyday life

  • After collected the raw data from above locations, features for training approach are derived as Signal Noise Ratio (SNR), Normalized Pseudorange Residual (NPR), Elevation Angle (EA) and Pseudorange Rate Consistency (PRC)

  • The model of classification is based on the given training data, and different kind of Global Navigation Satellites Systems (GNSS) receiver has its unique setting like bandwidth, antenna gain and satellite constellation, which make features of the same signal variously

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Summary

Introduction

Positioning has become a part of our everyday life. People heavily rely on GNSS-enabled applications to navigate himself or herself to a destination. The shadow matching technique compares the visible GNSS satellites, from the hypothesized locations on 3D map, with the measurements, classified by received signal strength. The proposed LOS/NLOS classification for shadow matching, is tested on measurements provided by a smartphone GNSS receiver firstly.

Results
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