Apple Inc.

Sirf Technology Holdings, Inc.

Changlin Ma

Isaac Thomas Miller

Robert William Mayor

Glenn Donald MacGougan

Shaowei Han

Kumar Gaurav Chhokra

Benjamin A Werner

Saurabh Godha

Christina Selle

Mark G Petovello

Xiaoji Niu

A device implementing a system for estimating device location includes at least one processor configured to receive a first and second set of signals, each set corresponding to location data and being received based on a sampling interval. The at least one processor is configured to, for each sampling period defined by the sampling interval, obtain sensor data corresponding to device motion during the sampling period, determine an orientation of the device relative to that of the vehicle based on the sensor data, calculate a non-holonomic constraint based on the orientation of the device relative to that of the vehicle such that the non-holonomic constraint is iteratively updated, and estimate a device state based on the non-holonomic constraint.

Iterative estimation of non-holonomic constraints in an inertial navigation system

A device implementing a system for estimating device location includes at least one processor configured to estimate a first position of a device based on a first set of parameters, the first set of parameters derived from first sensor data obtained on the device, the first set of parameters corresponding to device motion. The at least one processor is configured to estimate a second position of a user of the device based on a second set of parameters, the second set of parameters derived from second sensor data obtained on the device, the second set of parameters corresponding to user motion. Estimating the first and second positions is constrained by a predefined relationship between the device motion and the user motion. The at least one processor is configured to provide at least one of the first position of the device or the second position of the user.

User-specific motion model for location estimation

A device implementing a system for estimating device location includes at least one processor configured to receive a first and second set of signals at a sampling interval, each set corresponding to location data. For each sampling period defined by the sampling interval, the at least one processor is configured to obtain first sensor data corresponding to device motion during the sampling period, obtain second sensor data corresponding to atmospheric pressure sampled at a beginning and end of the sampling period, calculate a change in altitude based on a difference in the atmospheric pressure at the beginning and end of the sampling period, and estimate a device state based on the first sensor data and change in altitude.

Inertial navigation stabilization via barometer

An electronic device may include a pressure sensor for measuring barometric pressure. Pressure measurements may be calibrated using crowd-sourced pressure data to remove any weather bias or sensor bias associated with the pressure measurements. Altitude of the electronic device may be determined using the calibrated pressure measurement. When it is desired to estimate altitude, the electronic device may transmit a query to a server, which returns a local reference pressure value for the electronic device based on crowd-sourced pressure data from electronic devices in the vicinity of the electronic device making the query. To determine the local reference pressure value, the server may correlate the crowd-sourced pressure data with space, taking into account variations in terrain using digital elevation models to determine location-specific reference pressures. The local reference pressure value for a given electronic device is then determined using crowd-sourced reference pressures at nearby locations.

Altitude estimation using crowd-sourced pressure sensor data

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

Compensating for the misalignment of a navigation device with respect to a vehicle is described. In one example, the compensation is made by applying a high pass filter to a measured acceleration of the vehicle to produce a motion acceleration signal, weighting the motion acceleration signal with a measured steering rate of the vehicle, and deriving misalignment parameters for the navigation device with respect to the vehicle using the weighted motion acceleration signal.

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San Jose, CA, United States of America

Changlin Ma