Qualcomm Incorporated

Songwon Jee

William J Morrison

Min Wang

Gautam Nirula

Yunxiang Liu

An integer ambiguity validation method includes: obtaining, at an apparatus in conjunction with the one or more receivers, a plurality of feature values that are based on satellite signals received by a mobile device; determining, at the apparatus, an integer ambiguity vector indicative of integer numbers of carrier phase cycles of the satellite signals between the apparatus and respective satellites; determining, at the apparatus, a probability of the integer ambiguity vector being correct by using the integer ambiguity vector in a machine learning algorithm; and determining, at the apparatus, whether the integer ambiguity vector is correct based on the probability of the integer ambiguity vector being correct and an integer ambiguity vector probability threshold.

Integer ambiguity validation with machine learning

Aspects presented herein may enable a positioning device or entity to perform PR measurement error detection and classification based on SV geometry via ML. In one aspect, a UE or a location server determines for each SV of a set of SVs at least a geometric orientation with respect to the UE. The UE or the location server determines, based on an ML classifier and the determined geometric orientation with respect to the UE for each SV of at least a subset of the set of SVs, a relative PR weight for each SV of the set of SVs. The UE or the location server estimates a position of the UE based on PR measurements of each SV of the set of SVs and the relative PR weight for each SV of the set of SVs.

Space vehicle geometry based machine learning for measurement error detection and classification

Aspects presented herein may improve the performance and accuracy of GNSS-based positioning, where a position-grid based ML may be implemented by a UE or a location server to improve the accuracy of identifying a warm-start position of the UE. In one aspect, a UE or a location server determines, for each grid point within a range of an initial position of a UE, a set of PR residuals based on PRs for each SV of a set of SVs. The UE or the location server determines an estimated position of the UE based on the sets of determined PR residuals.

Position-grid based machine learning for GNSS warm-start position accuracy improvement

Disclosed embodiments facilitate accuracy and decrease error in terrestrial positioning systems, including errors induced by multipath (e.g. ground reflections) in doppler based measurements of SVs. In some embodiments, one or more Global Navigation Satellite System (GNSS) doppler measurements and one or more corresponding GNSS pseudorange measurements for one or more satellites may be obtained. One or more GNSS doppler estimates corresponding to the one or more GNSS doppler measurements may be determined, wherein for a GNSS doppler measurement, the corresponding GNSS doppler estimate may be determined based, in part, on the GNSS doppler measurement and a GNSS pseudorange measurement corresponding to the GNSS doppler measurement. A speed of the UE may be determined based, in part, on the one or more GNSS doppler estimates.

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Sunnyvale, CA, United States of America

Songwon Jee