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公开(公告)号:US20230050047A1
公开(公告)日:2023-02-16
申请号:US17836116
申请日:2022-06-09
申请人: oneNav, Inc.
发明人: Mahdi Maaref , Lionel Garin , Paul McBurney
摘要: Machine learning techniques are used, in one embodiment, to mitigate multipath in an L5 GNSS receiver. In one embodiment, training data is generated to provide ground truth data for excess path length (EPL) corrections for a set of received GNSS signals. A system extracts features from the set of received GNSS signals and uses the extracted features and the ground truth data to train a set of one or more neural networks that can produce EPL corrections for pseudorange measurements. The trained set of one or more neural networks can be deployed in GNSS receivers and used in the GNSS receivers to correct pseudorange measurements using EPL corrections provided by the trained set of neural networks.
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公开(公告)号:US20220137236A1
公开(公告)日:2022-05-05
申请号:US17068659
申请日:2020-10-12
申请人: oneNav, Inc.
发明人: Paul A. Conflitti , Paul McBurney , Mark Moeglein , Gregory Turetzky , Norman Krasner , Anthony Tsangaropoulos
摘要: GNSS receivers and systems within such receivers use improvements to reduce memory usage while providing sufficient processing resources to receive and acquire and track E5 band GNSS signals directly (without attempting in one embodiment to receive L1 GNSS signals). Other aspects are also described.
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公开(公告)号:US20210373179A1
公开(公告)日:2021-12-02
申请号:US17334477
申请日:2021-05-28
申请人: oneNav, Inc.
摘要: Global navigation satellite systems and methods use L5 GNSS signals to acquire secondary code phases of those signals without using L1 GNSS signals to aid in the acquisition of secondary code phases. Various embodiments are described to perform this acquisition.
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公开(公告)号:US20210325548A1
公开(公告)日:2021-10-21
申请号:US17230014
申请日:2021-04-14
申请人: oneNav, Inc.
发明人: Lionel Garin , Mahdi Maaref , Nagaraj Shivaramaiah , Paul McBurney , Mark Moeglein , Norman Krasner
摘要: This disclosure describes methods, systems and machine readable media that can provide position solutions using, for example, pattern matching with GNSS signals in urban canyons. In one method, based upon an approximate location in an urban canyon and a set of 3D data about building structures in the urban canyon, an expected signal reception data can be generated for both line of sight and non-line of sight GNSS signals from GNSS satellites, or other sources of GNSS signals, at each point in a set of points in a grid (or other model) in the vicinity of the approximate location). This expected signal reception data can be matched to a received set of GNSS signals that have been received by a GNSS receiver, and the result of the matching can produce an adjustment to the approximate location that is used in the position solution of the GNSS receiver.
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