Abstract:
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.
Abstract:
Methods, systems, and computer program product for prefetching location data based on predicted user behavior. A mobile device can request, from a user routine subsystem of the mobile device, a list of locations that a user of the mobile device routinely visits while the user carries the mobile device. The mobile device can determine a cluster of these locations that are within a specified distance between one another. The mobile device can request location data for these locations from a location server, even if the user is not at one of these locations. The location data can include a venue map and a venue location fingerprint. Upon detecting that the user entered a venue at one of these locations, the mobile device can determine a location of the user inside of the venue using the venue location fingerprint. The mobile device can then display the location on a venue map.
Abstract:
Methods, program products, and systems of using a mobile WAP for location and context purposes are disclosed. In general, in one aspect, a server can estimate an effective location of a wireless access gateway using harvested data. The server can harvest location data from multiple mobile devices. The harvested data can include a location of each mobile device and an identifier of a wireless access gateway that is located within a communication range of the mobile device. In some implementations, the server can identify a mobile wireless access gateway based on a distance comparison. Data indicating the mobility of a wireless access gateway can be used by a mobile device to initiate one or more actions, including managing power of the mobile device, modifying entrance and exit conditions of virtual fences and determining a context of the mobile device.
Abstract:
In some implementations, a method includes receiving, from a server, location data identifying locations of access points and mobile access points. A mobile device may determine an identifier of an access point within a communication range. The identifier is compared with the location data to identify parameters for the access point. The access point is determined to be a mobile access point based on the identified parameters included in the location data. In response to identifying the mobile access point, operating parameters executed by the mobile device are updated.
Abstract:
Methods, program products, and systems for using a location fingerprint database to determine a location of a mobile device are described. A mobile device can use location fingerprint data received from a server to determine a location of the mobile device at the venue. The mobile device can obtain, from a sensor of the mobile device, a vector of sensor readings, each sensor reading can measure an environment variable, e.g., a signal received by the sensor from a signal source. The mobile device can perform a statistical match between the vector and the location fingerprint data. The mobile device can then estimate a current location of the mobile device based on the statistical match.
Abstract:
Collocated access point (AP) harvest data is combined with accurate location-tagged harvest data to improve access point location estimates and to estimate the location of access points that could not be previously estimated.
Abstract:
Crowd-sourced location data from client devices is processed using enhanced filtering techniques in non-realtime by a centralized server process to improve the accuracy and reliability of georeferenced geodata. At a server computer, enhanced filtering techniques make use of future information to improve georeferencing of the geodata. In some implementations, the server computer can be configured to implement batch processing of raw GNSS data and other crowd-sourced location data (e.g., wireless scan data, inertial sensor data) using, for example, a smoothing filter or batch estimator. Augmentation data (e.g., satellite ephemeris data, clock offset and ionospheric delay) and sensor data independent of client devices can also be used by the server computer to augment the crowd-sourced location data to further improve georeferencing of geodata.
Abstract:
Techniques of delivering location data are described. A location server can receive, from a mobile device, a request for location data for determining a location of the mobile device at a venue. The request can include an estimated location of the mobile device. The location server can provide to the mobile device coarse location data for each venue that is located within a threshold distance to the estimated location of the mobile device. The coarse location data can include a list of coarse tiles at each venue, and parameters of a probability distribution function for determining in which tile of the venue the mobile device is located based on signals detected by the mobile device. The location server can the provide location fingerprint data associated with the tile and neighboring tiles to the mobile device. The mobile can use the location fingerprint data to determine a more detailed location.
Abstract:
Methods, program products, and systems for using a location fingerprint database to determine a location of a mobile device are described. A mobile device can use location fingerprint data and readings of a sensor to obtain a location observation. The mobile device can use the location observation in a particle filter for determining a location of the mobile device at a venue. Using state of movement of the mobile device and a map of the venue, the mobile device can determine one or more candidate locations of the device. The mobile device can then update the candidate locations using a next observation, and determine a probability density function based on the candidate locations. The mobile device can then present to a user a most probable location as a current location of the device in the venue.
Abstract:
Methods, program products, and systems of location estimation using a probability density function are disclosed. In general, in one aspect, a server can estimate an effective altitude of a wireless access gateway using harvested data. The server can harvest location data from multiple mobile devices. The harvested data can include a location of each mobile device and an identifier of a wireless access gateway that is located within a communication range of the mobile device. The server can calculate an effective altitude of the wireless access gateway using a probability density function of the harvested data. The probability density function can be a sufficient statistic of the received set of location coordinates for calculating an effective altitude of the wireless access gateway. The server can send the effective altitude of the wireless access gateway to other mobile devices for estimating altitudes of the other mobile devices.