This publication describes apparatuses, methods, and techniques for performing Global Navigation Satellite System (GNSS) shadow matching in a changing urban environment. To do so, a user equipment (e.g., a smartphone) utilizes a comprehensive positioning algorithm. The smartphone can measure a signal strength of satellites of the GNSS. When the signal strength matches an expected shadow, the comprehensive positioning algorithm can utilize GNSS data, area network data, inertial data, and an Urban Canyon Positioning Algorithm. The Urban Canyon Positioning Algorithm uses GNSS shadow matching data to increase user location accuracy in the urban environment. When the signal strength does not match the expected shadow, the comprehensive positioning algorithm can estimate user position using GNSS data, area network data, inertial data, and other optional localization signals (e.g., step counting, visual matches against a known model of a street-level visual map). Then, the comprehensive positioning algorithm can compare and quantify differences between the signal strength with the expected shadows, and quantify discrepancies between an estimated user location from various localization signals. Based on the differences between the signal strength and the expected shadows, the comprehensive positioning algorithm can determine and map changes in the urban environment. When the GNSS shadow matching determines user location with a high degree of confidence and accuracy, the comprehensive positioning algorithm can use this information to find discrepancies in other localization signals that rely on a map model (e.g., terrain height data, street-level visual maps, WiFi® hot spots). Lastly, the comprehensive positioning algorithm can adjust updates from the Urban Canyon Positioning Algorithm near unmodeled physical features (e.g., buildings, bridges, tunnels) in the urban environment.
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Watts, Kevin and Delibaltov, Diana, "GNSS Shadow Matching in a Changing Urban Environment", Technical Disclosure Commons, (April 23, 2020)