Improving GNSS Positioning in Dense Urban Areas by Diffraction Feature Matching

<title>Abstract</title> The performance of global navigation satellite systems (GNSSs) in urban canyons is degraded by signal scattering from buildings. Three-dimensional mapping-aided (3DMA) GNSS methods have been developed using feature matching to mitigate non-line-of-sight (NLOS) reception errors, but are ineffective when NLOS features are limited. Moreover, diffraction can attenuate signal strength, introducing faulty matches. Diffraction attenuation depends on the signal propagation geometry, with potential to serve as a new location-dependent feature for positioning. In this study, the carrier-to-noise density ratio attenuation of diffracted signals is modeled by the uniform geometrical theory of diffraction, as a new feature for positioning. The performance of this approach is validated by assessing location-dependent capability within a feature-matching framework. The proposed method is validated in dense urban canyons, reducing the two-dimensional root-mean-square error from the 10-m level of shadow matching to less than 5 m by using only three or four satellites with diffracted signals, enabling meter-level accuracy applications for pedestrian positioning and future integration with existing 3DMA GNSS methods.