Abstract
As the dependence of Global Navigation Systems (GNSS) increases, so does a growing demand for GNSS accuracy in urban environments. This research aims to improve navigation in these environments by integrating non-line-of-sight signals, building models, and measured signal to noise ratios in ways not typically used in GNSS positioning. We propose a technique of combining elements of shadow matching, non-line-of-sight signal prediction through ray tracing, and collaborative navigation. A specularity metric is developed, which predicts the likelihood of building reflections resulting in non-line-of-sight signal reception, and is used in conjunction with shadow matching techniques to improve positioning. A framework for implementing these approaches is presented and demonstrated using improved positioning techniques built and tested using real-world data collected in urban surroundings.
Original language | English (US) |
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Pages (from-to) | 547-566 |
Number of pages | 20 |
Journal | Navigation, Journal of the Institute of Navigation |
Volume | 67 |
Issue number | 3 |
DOIs | |
State | Published - Sep 1 2020 |
Externally published | Yes |
Bibliographical note
Funding Information:This research was funded through a Draper Fellowship through Charles Stark Draper Laboratory.
Publisher Copyright:
© 2020 Institute of Navigation