Apple Inc.

Qualcomm Incorporated

Kumar Gaurav Chhokra

Glenn Donald MacGougan

Robert William Mayor

Dawei Zhang

Tarik Tabet

Syed Aon Mujtaba

Lei Wang

Farouk Belghoul

Isaac Thomas Miller

Ronald Keryuan Huang

Guttorm Ringstad Opshaug

Wyatt Thomas Riley

Vaneet Pathak

Joseph Czompo

Michael P Dal Santo

Stephen J Rhee

Jouya Jadidian

Zoltan F Biacs

Richard B Warren

Stephen P Jackson

Aditya Narain Srivastava

Pejman Lotfali Kazemi

William J Bencze

Harsha Shirahatti

Sujeet Milind Patole

Changlin Ma

Bradley Joel Jensen

Christina Selle

Mikheil Tsiklauri

Glenn D Macgougan

Yi-Wen Liao

Alexandru Popovici

Shun Liu

Fatemeh Ghafoori

Cheng Jia

Glenn Donald Macgougan

Described herein are techniques to enable a mobile device to perform multi-source estimation of an altitude for a location. A baseline altitude may be determined at ground level for a location and used to calibrate a barometric pressure sensor on the mobile device. The calibrated barometric pressure sensor can then estimate changes in altitude relative to ground level based on detected pressure differentials, allowing a relative altitude to ground to be determined. Baseline calibration for the barometric sensor calibration can be performed to determine an ambient ground-level barometric pressure.

Location of interest altitude and determining calibration points

An electronic device may utilize various methods or systems to determine whether the electronic device is indoors or outdoors. The electronic device transmits wireless signals (e.g., radio detection and ranging (RADAR) signals). The electronic device receives reflections of the wireless signals. Using these received reflections of the wireless signals, the electronic device determines whether a power amplitude of the reflections is greater than or equal to a threshold value. In response to a determination that the power amplitude is not greater than or equal to the threshold value, the electronic device operates in an outdoor mode or an indoor mode.

Indoor/outdoor detection using wireless signals

Techniques for location source control for paired devices are described. Location source control selects a location source for a mobile device. The mobile device can receive, from an application program, a request for determining a location of the mobile device. The mobile device can determine a first location estimate using a location subsystem of the mobile device. The mobile device can determine whether to provide the first location estimate as input to the application program, or to provide a second location estimate as input to the application program. The second location estimate can be an estimated location of the companion device and determined by the companion device.

Location source control

The disclosed embodiments use 3D city models and shadow mapping to improve altitude fixes in urban environments. In an embodiment, a method comprises: generating a set of three-dimensional (3D) candidate positions in a geographic area of interest; predicting global navigation satellite system (GNSS) signal visibility at selected ones of the 3D candidate positions; receiving GNSS signals at a current location of the mobile device; determining observed satellite visibility based on the received GNSS signals; comparing the predicted satellite visibility with the observed satellite visibility; determining a position fix based on a result of the comparing; determining an indoor environment where the mobile device is located based at least on an altitude component of the position fix; obtaining structural data for the identified indoor environment; and determining a floor lower bound for the current location of the mobile device based on the altitude component and the structural data.

Three-dimensional city models and shadow mapping to improve altitude fixes in urban environments

Systems, methods, devices and computer-readable storage mediums are disclosed for assisted GNSS velocity estimation. In an implementation, a method comprises: obtaining, by a mobile device, a step-based speed measurement based on sensor data; obtaining, by the mobile device, a step-based speed uncertainty associated with the step-based speed measurement; determining, by the mobile device, that one or more assistance conditions are met; responsive to the determining, assisting a state estimator using the step-based speed measurement and the associated step-based speed uncertainty; and estimating at least one of the position, velocity or speed of the mobile device using the assisted state estimator.

Assisted GNSS velocity estimation

Techniques for GNSS positioning using three-dimensional (3D) building models are described. A processor can determine a probable path for a signal from a GNSS space vehicle (e.g., a satellite) to reach the GNSS receiver. The probable path can include one or more specular reflections. The processor can determine a Doppler correction based on the probable path, including inverting a sense of a vector of the Doppler correction for each reflection. The processor can then incorporate the Doppler correction in an estimated velocity of the mobile device, an estimated position of the mobile device, or both.

Doppler shift correction using three-dimensional building models

Techniques for GNSS positioning using three-dimensional (3D) building models are described. A processor of a mobile device can determine a lower bound of uncertainty for an estimated position of the mobile device. The processor can receive an estimated position from a GNSS receiver of the mobile device. The processor can acquire geographic feature data including 3D building models of buildings and other geographic features that are located near the estimated position and may reflect GNSS signals. The processor can then determine a lower bound of uncertainty of the estimated position, regardless of an estimated uncertainty provided by a GNSS estimator. The lower bound can be higher (e.g., have a greater error margin) than the uncertainty value provided by the GNSS estimator. The processor can then present the estimated position, in association with an error margin corresponding to the lower bound of uncertainty, on a map user interface of the mobile device.

GNSS positioning using three-dimensional building models

Methods, systems and computer program products for determining and filtering potential outliers in RF signals used in radionavigation are described. A radionavigation subsystem of a mobile device can determine a first location estimate of the mobile device. The mobile device can determine a free direction from the first location estimate. The free direction can be a direction along which RF signals may cause greater position errors than RF signals from other directions may cause. The mobile device can determine a potential outlier among the received RF signals, the potential outlier being an RF signal from a signal source in the free direction. The mobile device can indicate to the radionavigation subsystem that a weight of the potential outlier shall be reduced when determining a second location estimate of the mobile device using the RF signals.

De-weighting of outlier signals in a radio frequency navigation system

An apparatus and method for providing an improved heading estimate of a mobile device in a vehicle is presented. First, the mobile device determines if it is mounted in a cradle attached to the vehicle; if so, inertia sensor data may be valid. While in a mounted stated, the mobile device determines whether it has been rotated in the cradle; if so, inertia sensor data may no longer be reliable and a recalibration to determine a new relative orientation between the vehicle and the mobile device is needed. If the mobile device is mounted and not recently rotated, heading data from multiple sensors (e.g., GPS, gyroscope, accelerometer) may be computed and combined to form the improved heading estimate. This improved heading estimate may be used to form an improved velocity estimate. The improved heading estimate may also be used to compute a bias to correct a gyroscope.

Inertial sensor aided heading and positioning for GNSS vehicle navigation

Apparatus and methods for estimating a location of a wireless device in communication with a wireless network, such as an LTE/LTE-A network, based at least in part on WLAN/WPAN AP measurements and/or barometric measurements are disclosed. The wireless device responds to a location capability inquiry from the wireless network by providing a response that indicates the wireless device is configurable to estimate its location based on WLAN/WPAN AP and/or barometric measurements. The wireless network sends WLAN/WPAN AP and/or barometric reference information to the wireless device to assist in estimating its location. The wireless device measures one or more WLAN/WPAN APs, and the wireless device uses the WLAN/WPAN AP and/or barometric measurements to estimate its location. In some embodiments, GPS/GNSS information is used in conjunction with WLAN/WPAN AP and/or barometric measurements to estimate the location of the wireless device.

Method and apparatus for indoor location determination with WLAN/WPAN/sensor support

Apparatus and methods for estimating a location of a wireless device in communication with a wireless network, such as a UMTS network, based at least in part on WLAN/WPAN AP measurements and/or barometric measurements are disclosed. The wireless device responds to a location capability inquiry from the wireless network by providing a response that indicates the wireless device is configurable to estimate its location based on WLAN/WPAN AP and/or barometric measurements. The wireless network sends WLAN/WPAN AP and/or barometric reference information to the wireless device to assist in estimating its location. The wireless device measures one or more WLAN/WPAN APs, and the wireless device uses the WLAN/WPAN AP and/or barometric measurements to estimate its location. In some embodiments, GPS/GNSS information is used in conjunction with WLAN/WPAN AP and/or barometric measurements to estimate the location of the wireless device.

Method and apparatus for location determination with WLAN/WPAN/sensor support

A mobile device adjusts the extents to which it depends on various satellites to estimate its global position based on the predicted probability for each such satellite that a ray extending from that satellite to the mobile device is obstructed by a building. The mobile device can predict the probabilities of building obstructions based on a digital model of the environment in which the mobile device is estimated to be. The mobile device weights the extent of uncertainty for each satellite's global positioning data based on the predicted probability of obstruction for that satellite. Using these weighted uncertainties, the mobile device selects the extents to which it relies on each satellite's global positioning data when estimating the mobile device's current global position.

Positioning accuracy using 3D building models

Growing community of inventors

San Jose, CA, United States of America

Kumar Gaurav Chhokra