Abstract:
Implementations are disclosed for obtaining a range state of a device operating in an indoor environment with radio frequency (RF) signal sources. In some implementations, windowed signal measurements obtained from RF signals transmitted by an RF signal source are classified into range classes that are defined by threshold values obtained from a RF signal propagation model. A range class observation is obtained by selecting a range class among a plurality of range classes based on a percentage of a total number of windowed signal measurements that are associated with the range class. The range class observation is provided as input to a state estimator that estimates a range class that accounts for process and/or measurement noise. The output of the state estimator is provided as input to a state machine.
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:
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:
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:
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:
A wireless electronic device may include wireless communications circuitry and processing circuitry. The wireless communications circuitry may receive radio-frequency signals from external communications circuitry in a number of frequency channels of a communications band. The processing circuitry may gather received signal quality data such as receive signal strength indicator (RSSI) values from the radio-frequency signals received in each of the frequency channels. The processing circuitry may accumulate respective probability distributions of gathered RSSI values for each frequency channel and may compare each of the probability distributions to generate RSSI offset values for each frequency channel. The processing circuitry may gather additional RSSI values in one or more frequency channels and may adjust the additional RSSI values based on the associated RSSI offset values. The processing circuitry may use the adjusted RSSI values to determine an accurate location of the wireless electronic 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:
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 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.