GLOBAL NAVIGATION SATELLITE SYSTEMS (GNSS) LOCALIZATION WITH RESIDUAL GRID REPRESENTATION

    公开(公告)号:US20250004142A1

    公开(公告)日:2025-01-02

    申请号:US18630717

    申请日:2024-04-09

    Abstract: In some implementations, a global navigation satellite system (GNSS) device may determine its approximate location, and, for each pseudorange measurement of a plurality of pseudorange measurements performed by the GNSS device: determine a location of a respective satellite vehicle (SV) that transmits a respective GNSS signal of which the pseudorange measurement is performed, and determine a respective residual grid, where the respective residual grid is based on respective information from the pseudorange measurement and the location of the respective SV, and the respective residual grid is indicative of possible locations of the GNSS device within a geographical region including the approximate location of the GNSS device. The GNSS device may aggregate the residual grids corresponding to at least a portion of the plurality of pseudorange measurements and may determine a location estimate of the GNSS device based on the aggregation of the residual grids.

    OPTIMIZING WEIGHTED LEAST SQUARE (WLS) INPUTS TO IMPROVE GLOBAL NAVIGATION SATELLITE SYSTEMS (GNSS) LOCALIZATION

    公开(公告)号:US20240295661A1

    公开(公告)日:2024-09-05

    申请号:US18177713

    申请日:2023-03-02

    CPC classification number: G01S19/07 G01S19/06 G01S19/20

    Abstract: A method of determining a position of a device includes obtaining an initial position of the device without using Global Navigation Satellite System (GNSS) satellites. GNSS measurements are taken of radio frequency (RF) signals transmitted by the GNSS satellites. Initial residuals are determined based, at least in part, on GNSS measured distances determined from the at least a portion of the GNSS measurements and expected distances determined from the initial position. Errors of the GNSS measurements based on the RF signals are estimated. An optimization is performed using some of the estimated errors to produce a modified set of residuals, wherein the optimization is further based on H, wherein H represents a matrix with trigonometric functions of a geometry of the GNSS satellites. A cost minimization method of the modified set of residuals and actual geometry of the GNSS satellites (H) to determine an improved position of the device.

    INTEGER AMBIGUITY VALIDATION WITH MACHINE LEARNING

    公开(公告)号:US20240418869A1

    公开(公告)日:2024-12-19

    申请号:US18334837

    申请日:2023-06-14

    Abstract: 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.

    ERROR MITIGATION IN DOPPLER BASED SATELLITE POSITIONING SYSTEM MEASUREMENTS

    公开(公告)号:US20190353800A1

    公开(公告)日:2019-11-21

    申请号:US16108019

    申请日:2018-08-21

    Abstract: 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.

    SPACE VEHICLE GEOMETRY BASED MACHINE LEARNING FOR MEASUREMENT ERROR DETECTION AND CLASSIFICATION

    公开(公告)号:US20230382565A1

    公开(公告)日:2023-11-30

    申请号:US17804849

    申请日:2022-05-31

    CPC classification number: B64G1/28 B64G1/244 B64G2001/245

    Abstract: 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.

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