Improved urban navigation with shadow matching and specular matching

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.