Sequential Likelihood Updates for User Position Estimation with GNSS Signal Strength Matching

This publication describes apparatuses, methods, and techniques for performing Global Navigation Satellite System (GNSS) shadow matching that can increase user location accuracy in an urban environment by appropriately calculating and reporting sequential likelihood (probability) updates for user location estimation. To do so, an electronic device (e.g., a smartphone) utilizes an Urban Canyon Positioning Algorithm, which uses particle filters to solve filtering problems arising in Bayesian statistical inference. The Urban Canyon Positioning Algorithm performs sequential likelihood updates for user position estimation by weighting various particles of the particle filters to account for numerous factors, such as the number of GNSS signals, the observed shape of a “constellation” of satellites of the GNSS, unmeasured physical features (e.g., buildings), local physical features, and so forth