Abstract: Embodiments described herein provide for a system, a non-transitory machine-readable medium, and methods to provide location services. In an embodiment, a method provides receiving at least one indication that an electronic device is in transit to at least one defined location, establishing a first fence boundary for the at least one defined location, receiving an indication that the electronic device has crossed the first fence boundary and is in transit to a defined location, and establishing a second fence for the defined location, where the second fence provides a finer granularity fence for the defined location than the first fence.

Abstract: In an example method, a computer system receives a query from a mobile device, including an indication of a location of the mobile device, and an environmental measurement obtained by the mobile device at the location. A set of candidate points of interest in geographical proximity to the location is determined. For each of one or more candidate points of interest of the set, a location fingerprint of the candidate point of interest and contextual data regarding the candidate point of interest are obtained. A similarity between the environmental measurement and each location fingerprint is determined. A particular candidate point of interest is selected from among the set based on the similarity, and based on an assessment of the contextual data. A label of the selected point of interest is associated with the location and transmitted to the mobile device.

Abstract: 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.

Abstract: In an embodiment, a method includes receiving, by a processor of a mobile device, raw pedometer data from a pedometer included in or coupled to the mobile device. The processor receives raw GNSS data from a GNSS receiver included in or coupled to the mobile device. A first filter uses the raw pedometer data to estimate a step frequency of a user wearing or holding the mobile device. The processor determines a pace change from the estimated step frequency. Responsive to the pace change, the processor adapts a bandwidth of a second filter and estimates, by the second filter, an estimated pace or speed of the user.

Abstract: Among other things, we describe a method that includes, on an electronic device, determining that a current quality metric of signals received by a location system of the electronic device does not meet a threshold quality metric, and based on the determination, selecting a recommendation for changing a position of the device in a manner that would alter the current quality metric. This aspect can also include corresponding systems, apparatus, and computer program products stored on a storage device.

Abstract: In an example method, a computer system receives a query from a mobile device, including an indication of a location of the mobile device, and an environmental measurement obtained by the mobile device at the location. A set of candidate points of interest in geographical proximity to the location is determined. For each of one or more candidate points of interest of the set, a location fingerprint of the candidate point of interest and contextual data regarding the candidate point of interest are obtained. A similarity between the environmental measurement and each location fingerprint is determined. A particular candidate point of interest is selected from among the set based on the similarity, and based on an assessment of the contextual data. A label of the selected point of interest is associated with the location and transmitted to the mobile device.

Abstract: In an example method, a computer system receives a query from a mobile device, including an indication of a location of the mobile device, and an environmental measurement obtained by the mobile device at the location. A set of candidate points of interest in geographical proximity to the location is determined. For each of one or more candidate points of interest of the set, a location fingerprint of the candidate point of interest and contextual data regarding the candidate point of interest are obtained. A similarity between the environmental measurement and each location fingerprint is determined. A particular candidate point of interest is selected from among the set based on the similarity, and based on an assessment of the contextual data. A label of the selected point of interest is associated with the location and transmitted to the mobile device.

Abstract: Among other things, we describe a method that includes, on an electronic device, determining that a current quality metric of signals received by a location system of the electronic device does not meet a threshold quality metric, and based on the determination, selecting a recommendation for changing a position of the device in a manner that would alter the current quality metric. This aspect can also include corresponding systems, apparatus, and computer program products stored on a storage device.

Abstract: Systems, methods and non-transitory, computer-readable storage mediums are disclosed for a multimode GNSS odometer. In some implementations, a method comprises: obtaining, by a device, position and velocity data from a sensor embedded in, or coupled to the device; statistically combining, by the device, the position and velocity data to generate a first delta distance travelled by the device over a period of time, the statistical combining including weighting a contribution of an integrated speed to the first delta distance, the integrated speed derived from the obtained velocity data and period of time; filtering, by the device, the first delta distance to generate a filtered first delta distance; and generating, by the device, a first estimate of total distance travelled by the device based on the filtered first delta distance.

Abstract: Systems, methods, devices and computer-readable mediums are disclosed for parking event detection and location estimation. In some implementations, a method comprises: determining, by a processor of a mobile device, a first activity state indicative of a possible parking event; obtaining, by the processor, a speed of the mobile device from a global navigation satellite system (GNSS) of the mobile device; obtaining, by the processor, pedometer data from a digital pedometer of the mobile device; determining, by the processor, a second activity state indicative of a possible parking event based at least in part on the GNSS speed and pedometer data; and responsive to the second activity state, estimating, by the processor, a location of the vehicle.

Abstract: Techniques of range free proximity determination are described. A mobile device can determine an entry into or exit from a proximity fence upon determining that the mobile device is sufficiently close to a signal source. The proximity fence can be a virtual fence defined by the signal source and associated with a service. The mobile device can detect signals from multiple signal sources. The mobile device can determine that, among the signal sources, one or more signal sources are located closest to the mobile device based on a ranking of the signal sources using signal strength. The mobile device can determine a probability indicating a confident level of the ranking. The mobile device can determine that the mobile device entered or exited a proximity fence associated with a highest ranked signal source satisfying a confidence threshold.

Abstract: Techniques of range free proximity determination are described. A mobile device can determine an entry into or exit from a proximity fence upon determining that the mobile device is sufficiently close to a signal source. The proximity fence can be a virtual fence defined by the signal source and associated with a service. The mobile device can detect signals from multiple signal sources. The mobile device can determine that, among the signal sources, one or more signal sources are located closest to the mobile device based on a ranking of the signal sources using signal strength. The mobile device can determine a probability indicating a confident level of the ranking. The mobile device can determine that the mobile device entered or exited a proximity fence associated with a highest ranked signal source satisfying a confidence threshold.

Abstract: In an example method, a computer system receives a query from a mobile device, including an indication of a location of the mobile device, and an environmental measurement obtained by the mobile device at the location. A set of candidate points of interest in geographical proximity to the location is determined. For each of one or more candidate points of interest of the set, a location fingerprint of the candidate point of interest and contextual data regarding the candidate point of interest are obtained. A similarity between the environmental measurement and each location fingerprint is determined. A particular candidate point of interest is selected from among the set based on the similarity, and based on an assessment of the contextual data. A label of the selected point of interest is associated with the location and transmitted to the mobile device.

Abstract: Systems, methods, devices and computer-readable mediums are disclosed for parking event detection and location estimation. In some implementations, a method comprises: determining, by a processor of a mobile device, a first activity state indicative of a possible parking event; obtaining, by the processor, a speed of the mobile device from a global navigation satellite system (GNSS) of the mobile device; obtaining, by the processor, pedometer data from a digital pedometer of the mobile device; determining, by the processor, a second activity state indicative of a possible parking event based at least in part on the GNSS speed and pedometer data; and responsive to the second activity state, estimating, by the processor, a location of the vehicle.

Abstract: In some implementations, a beaconing protocol can be used to broadcast beacon information to mobile devices. The beaconing protocol can be used by a Bluetooth Low Energy (LE) (e.g., Bluetooth 4.0) beacon to transmit a package of information that identifies the beacon and indicates the calibrated transmission power (e.g., measured power) of the beacon. The package of information can be configured by beacon providers and/or beacon installers at the locations where the beacons are installed. When a mobile device receives the beacon package, the mobile device can use the beacon identification information and/or the measured power of the beacon to determine a location (e.g., precise location, geofence location) of the mobile device. In some implementations, the mobile device can transmit the beacon information to a server and the server can determine the location of the mobile device and send location and/or content information back to the mobile device.

Abstract: A proximity fence can be a location-agnostic fence defined by signal sources having no geographic location information. The proximity fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. A signal source can be a radio frequency (RF) transmitter broadcasting a beacon signal. The beacon signal can include a payload that includes an identifier indicating a category to which the signal source belongs, and one or more labels indicating one or more subcategories to which the signal source belongs. The proximity fence defined by the group of signal sources can trigger different functions of application programs associated with the proximity fence on a mobile device, when the mobile device moves within the proximity fence and enters and exits different parts of the proximity fence corresponding to the different subcategories.

Abstract: A proximity fence can be a location-agnostic fence defined by signal sources having no geographic location information. The proximity fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. A signal source can be a radio frequency (RF) transmitter broadcasting a beacon signal. The beacon signal can include a payload that includes an identifier indicating a category to which the signal source belongs, and one or more labels indicating one or more subcategories to which the signal source belongs. The proximity fence defined by the group of signal sources can trigger different functions of application programs associated with the proximity fence on a mobile device, when the mobile device moves within the proximity fence and enters and exits different parts of the proximity fence corresponding to the different subcategories.

Abstract: Among other things, we describe a method that includes, on an electronic device, receiving, from a first location system of the electronic device, first data indicative of a first location of the device at a first time, comparing the first data to second data indicative of a second location of the device at a second time, the second data having been received from a second location system of the electronic device, and based on the comparison, determining whether the first data meets a threshold of location data integrity.

Abstract: Systems, methods and non-transitory, computer-readable storage mediums are disclosed for a multimode GNSS odometer. In some implementations, a method comprises: obtaining, by a device, position and velocity data from a sensor embedded in, or coupled to the device; statistically combining, by the device, the position and velocity data to generate a first delta distance travelled by the device over a period of time, the statistical combining including weighting a contribution of an integrated speed to the first delta distance, the integrated speed derived from the obtained velocity data and period of time; filtering, by the device, the first delta distance to generate a filtered first delta distance; and generating, by the device, a first estimate of total distance travelled by the device based on the filtered first delta distance.

Abstract: Systems, methods and non-transitory, computer-readable mediums are disclosed for pedestrian pace estimation with pace change detection. In some implementations, a method comprises: receiving, by a processor of a mobile device, raw pedometer data from a pedometer included in or coupled to the mobile device; receiving, by the processor, raw GNSS data from a GNSS receiver included in or coupled to the mobile device; estimating, using a first filter implemented on the processor, a step frequency of a user wearing or holding the mobile device, the estimating including using the raw pedometer data; determining, by the processor, a pace change, the pace change determined from the estimated step frequency; responsive to the pace change, adapting by the processor, a bandwidth of a second filter implemented by the processor; and estimating, by the second filter, an estimated pace or speed of the user.