Qualcomm Incorporated

Sirf Technology, Inc.

Csr Technology Inc.

Magellan Corporation

Onenav, Inc.

Sirf Technology Holdings, Inc.

Other

Panasonic Corporation

Matsushita Electric Works, Ltd.

Nemerix Sa

Sirf Technologies, Inc.

Qualcomm Iskoot, Inc.

Lionel Jacques Garin

Gengsheng Zhang

Ashutosh Pande

Sai Pradeep Venkatraman

Gregory Turetzky

Kanwar Chadha

Sundar Raman

Ju-Yong Do

Jubin Jose

Xinzhou Wu

Urs Niesen

Nicolas Patrick Vantalon

Leon Kuo-Liang Peng

Chiayee Steve Chang

Marlene Wan

Makarand Phatak

Rajarshi Gupta

Venkatesan Nallampatti Ekambaram

Victor Abramovich Veitsel

Kurt Christian Schmidt

Stanislov Sila-Novatisky

Mark Zhodzicshsky

Mahdi Maaref

Steve Chang Chiayee

Stephen William Edge

Weihua Gao

Andreas Klaus Wachter

Mark Leo Moeglein

Praveen Dua

Junichi Suzuki

Xiangdong Lin

Ikuo Tsujimoto

Randall J Silva

Ayman Fawzy Naguib

Vinay Sridhara

Xiaoxin Zhang

Javad Ashjaee

Paul W McBurney

Mangesh M Chansarkar

Daniel Babitch

Grant Alexander Marshall

Steve Chang

SaiPradeep Venkatraman

Farbod Kamgar

Weiyi Liu

Kurt Schmidt

Hemali Vyas

Stephane Joseph Beauregard

Pramod Gupta

Jim Brown

Hong Cheng

Saumitra Mohan Das

Carlos Horacio Aldana

Leonid Sheynblat

Douglas Neal Rowitch

Kirk Allan Burroughs

Arnold Jason Gum

Sanjai Kohli

Behrooz Khorashadi

Steven A Gronemeyer

Per Kristian Enge

Carlos Manuel Puig

Charles Alfred Bergan

Faraz Mohammad Mirzaei

Chandrakant Mehta

Amir Abbas Emadzadeh

Mayur Nitinbhai Shah

Qiyue Zhang

Qingwen Zhang

Bruce Everett Wilson

Norman Krasner

Quanwei Liu

Arie Rahmat

Lars Boeryd

Bernal A Arroyo

Nagaraj Shivaramaiah

Daniel Liao

Reza Abtahi

Ayman Naquib

Stefan Witanis

Mangesh Chambarkar

Geugsheng Zhang

Robert Shaw Harvey

Wendell Edwin Innis

Vijayaraghavan Venkatasubramanian

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.

Multipath mitigation in GNSS receivers with machine learning models

Machine learning techniques can be used to mitigate multipath in a GNSS receiver that includes a first trained model that provides extra path length (EPL) corrections in the GNSS receiver. The first trained model can be updated using an updated and trained model from one or more assistance servers that are in communication with the GNSS receiver. The GNSS receiver can provide, for a particular computed position and time, extracted features from received GNSS signals to the one or more assistance servers. The assistance servers can then use the extracted features and a source of true EPL corrections (e.g., from a 3D building map database for the particular computed position and time) to train a server model. The server model, once trained to a desired level of accuracy, can be transmitted to the GNSS receiver to replace the first trained model.

User-aided signal line-of-sight (LOS) machine learning classifier

Machine learning techniques are used to compute predicted range rate errors in a GNSS receiver. In one embodiment, training data is computed to provide true range rate error data 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 true range rate error data to train a model (e.g., a set of one or more neural networks) that can produce predicted range rate errors for use in correcting measurements. The trained set of one or more neural networks can be deployed in GNSS receivers and used in the GNSS receivers to correct Doppler measurements using the predicted range rate errors provided by the trained set of one or more neural networks.

Machine learning in GNSS receivers for improved velocity outputs

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.

Matching for GNSS signals

A method of determining a position of a mobile platform includes obtaining a plurality of pseudorange measurements from multiple time epochs of a satellite navigation system (SPS) and obtaining a plurality of visual-inertial odometry (VIO) velocity measurements from a VIO system. Each time epoch of the SPS includes at least one pseudorange measurement corresponding to a first satellite and at least one pseudorange measurement corresponding to a second satellite. The method also includes combining the plurality of pseudorange measurements with the plurality of VIO velocity measurements to identify one or more outlier pseudorange measurements in the plurality of pseudorange measurements. The one or more outlier pseudorange measurements are then discarded from the plurality of pseudorange measurements to generate a remaining plurality of pseudorange measurements. The position of the mobile platform is then computed based on the remaining plurality of pseudorange measurements and the plurality of VIO velocity measurements.

Outlier detection for satellite positioning system using visual inertial odometry

A method of determining a trajectory of a mobile platform includes obtaining a satellite positioning system (SPS) measurement from one or more SPS signals acquired by an SPS receiver of the mobile platform. The method also includes obtaining a visual-inertial odometry (VIO) measurement of the mobile platform from a VIO system of the mobile platform. A first position estimate of the mobile platform is determined based, at least in part, on the SPS measurement and the VIO measurement. The method then includes adjusting the first position estimate to generate a smoothed position estimate based, in part, on a smoothing parameter that controls a smoothness of the trajectory. The trajectory of the mobile platform is then determined, at least in part, using the smoothed position estimate.

Mobile platform positioning using satellite positioning system and visual-inertial odometry

The disclosure generally relates to position sensors, and more particularly to repair of carrier-phase cycle slips using displacement data. An apparatus for use in position sensing may include a displacement sensor, a positioning signal receiver, a memory, and a processor coupled to the displacement sensor, the positioning signal receiver, and the memory. The processor and memory may be configured to processor and memory are configured to detect a loss of lock of a first carrier tracking loop associated with the first set of carrier-phase measurements, wherein the first carrier tracking loop is associated with a first integer ambiguity, estimate, based on the displacement data, an ambiguity increment to the first integer ambiguity subsequent to the detected loss of lock, and resolve a second integer ambiguity associated with the second set of positioning signals based on the first integer ambiguity and the estimated ambiguity increment.

Repair of carrier-phase cycle slips using displacement data

Disclosed embodiments pertain to a method on a UE may comprise determining a first absolute position of the UE at a first time based on GNSS measurements from a set of satellites. At a second time subsequent to the first time, the UE may determine a first estimate of displacement of the UE relative to the first absolute position using non-GNSS measurements. Further, at the second time, the UE may also determine a second estimate of displacement relative to the first absolute position and/or a second absolute position of the UE based, in part, on: the GNSS carrier phase measurements at the first time from the set of satellites, and GNSS carrier phase measurements at the second time from a subset comprising two or more satellites of the set of satellites, and the first estimate of displacement of the UE.

Visual inertial odometry attitude drift calibration

Determining a position of a device using a signal received from a reference emitter includes: receiving the signal; determining a state of a first filter, the state of the first filter including a first carrier phase ambiguity estimate that includes a floating value; determining a state of a second filter, the state of the second filter including a second carrier phase ambiguity estimate that includes a fixed value; determining whether the state of the second filter is consistent with one other filter state or measurement; maintaining the state of the second filter in response to the device determining that the state of the second filter is consistent with the other filter state; changing the state of the second filter to the state of the first filter in response to the device determining that the state of the second filter is not consistent with the other filter state.

Fast recovery from incorrect carrier phase integer locking

A station (STA) receives a schedule from a reference device in a wireless network. The schedule provides a measurement time for each of one or more wireless channels. The STA tunes to a first channel of the one or more wireless channels in accordance with the schedule. While tuned to the first channel in accordance with the schedule, the STA receives a first message from a first access point (AP). The first message, which includes a time-of-departure timestamp from the reference device, is time-stamped with a time-of-arrival timestamp. The STA receives an indication of a time at which the reference device received the first message from the first AP. The STA determines its position based in part on the time-of-departure timestamp, the time-of-arrival timestamp, and the time at which the reference device received the first message.

Wireless positioning using scheduled transmissions

A method for aligning visual-inertial odometry (VIO) and satellite positioning system (SPS) reference frames includes obtaining a plurality of range-rate measurements of a mobile platform from an SPS. The range-rate measurements are with respect to a global reference frame of the SPS. The method also includes obtaining a plurality of VIO velocity measurements of the mobile platform from a VIO system. The VIO velocity measurements are with respect to a local reference frame of the VIO system. At least one orientation parameter is then determined to align the local reference frame with the global reference frame based on the range-rate measurements and the VIO velocity measurements.

Alignment of visual inertial odometry and satellite positioning system reference frames

Disclosed are systems, methods and devices for application of determining position information for mobile devices. In specific implementations, measurement of a signal travel time and a signal's strength may be combined to characterize a transmission power of the signal's transmitter. The characterized transmission power may be applied to affect expected signal strength signature values for use of the signal's transmitter may be updated in order to enhance a location based service where location may be effected by accuracy of a transmitter's power.

Method, systems and/or device for adjusting expected received signal strength signature values

A method, in a mobile device, of controlling region determination by the mobile device, includes: determining a present pressure at the mobile device; determining, based on the present pressure and a reference pressure, that the mobile device moved from a first region to a second region within a structure, the first region and the second region being different regions of the structure and vertically displaced from each other; and performing region determination in response to determining that the mobile device moved from the first region to the second region.

Region determination control

Various methods, apparatuses and/or articles of manufacture are provided which may be implemented using one or more fixed electronic devices to generate a reference data report corresponding to a particular environment. Various methods, apparatuses and/or articles of manufacture are provided which may be implemented using one or more mobile electronic devices to generate an environment report corresponding to a particular environment.

Techniques for generating environment and reference data reports for particular environments on behalf of mobile devices

Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate or support one or more operations and/or techniques for enhanced passive positioning with adaptive round trip time (RTT)-type ranging, such as for use in or with a mobile communication device, for example.

Enhanced passive positioning with adaptive active positioning

Growing community of inventors

Palo Alto, CA, United States of America

Lionel Jacques Garin