Abstract: Methods, systems, and computer program product for deduplicating location fingerprint data for a venue are described. A system including a location server, or a mobile device, or both, can deduplicate the location fingerprint data. Deduplicating the location fingerprint data can include identifying correlated signal sources the signals of which are mutually dependent such that measurements of one signal source can be used to predict measurements of another. The system can determine a mutual information entropy value for each pair of signal sources, and identify the correlated signal sources based on high mutual information entropy value.

Abstract: Methods, program products, and systems for using multiple sensors to determine a location fingerprint are described. A sampling device can measure RF signals detected at a train station of a transit system or a route of the transit system. The sampling device, or a location server receiving the measurements, can filter RF signal measurements using one or more readings from sensors coupled to the sampling device and that are different from RF receivers. The readings can be taken concurrently with the RF signal measurements. These readings, designated as motion cues, can include motion sensor readings, barometer readings, or magnetometer readings. Using the motion cues, the sampling device or location server can distinguish different platforms of a station of the transit system and different levels of the station, or filter out RF signal measurements that may have been inaccurate, e.g., as caused by disturbances from a train entering or leaving a station.

Abstract: Methods, program products, and systems for building a location fingerprint database for a transit system are described. The transit system can be a subway system including underground train stations and routes where location determination using GPS signals is difficult or impossible. A sampling device can measure signals, e.g., radio frequency (RF) signals detected at the stations or on the routes. A location server can construct a location fingerprint for each of the stations and the routes. Each location fingerprint can represent expected signal measurements by a user device if the user device is located at the respective station or route. The location server can provide the location fingerprint to a user device for the user device to determine a location of the user device within the station or on the route.

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: Reduced resolution location determination for improved anonymity of a user location is disclosed. In some implementations, a first location of a computing device operating in a geographic area is determined. A population density of the geographic area is estimated. A grid overlay is generated, including a number of cells based on the estimated population density. Using the grid overlay, a second location is generated for the computing device that is less precise than the first location. The less precise second location can be used in a local search or other application to improve the anonymity of the user location.

Abstract: Coarse location estimation for mobile devices is disclosed for detecting mobile device presence at general locations of interest and switching operating modes and services for one or more location context aware applications. In some implementations, sensor data is received from a plurality of data sources at a location. For each data source, a first probability is estimated that the mobile device is at the location based on sensor data from the data sources. A second probability is estimated that the mobile device is not at the location based on sensor data from the data sources. The first and second estimated probabilities are statistically combined to generate a third estimated probability that the mobile device is at the location.

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 general, in one aspect, a method includes receiving, on a mobile device, an indication that an application executing on the mobile device has entered a background state, determining, based on data received from a location system of the mobile device, that the mobile device has remained within a geographic area during a time interval, the geographic area being defined by a radius determined according to an application type of the application, and disabling at least a portion of the location system of the mobile device.

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.

Abstract: Methods, systems, and computer program product for deduplicating location fingerprint data for a venue are described. A system including a location server, or a mobile device, or both, can deduplicate the location fingerprint data. Deduplicating the location fingerprint data can include identifying correlated signal sources the signals of which are mutually dependent such that measurements of one signal source can be used to predict measurements of another. The system can determine a mutual information entropy value for each pair of signal sources, and identify the correlated signal sources based on high mutual information entropy value. The system can adjust weights of the correlated signal sources in location determination.

Abstract: Surveying techniques for generating location fingerprint data are described. A mobile device can survey a venue by measuring, at multiple locations at the venue, signals from one or more signal sources. At each location, the mobile device can take multiple measurements of signals. The mobile device can take each measurement at a distinct orientation. The measurements can be used to determine expected measurements of the signals at the venue. Differences between the multiple measurements of signals can be used to determine a variance of the expected measurements. The expected measurements and variance can be designated as location fingerprint data for the venue. The location fingerprint data can be used by mobile devices for determining a location at the venue.

Abstract: Methods, program products, and systems for reducing a location search space are described. A mobile device, when arriving at a venue, can determine a location of the mobile device using signals from one or more signal sources associated with the venue. The mobile device can use a coarse location estimator to estimate a coarse location of the mobile device at the venue. The mobile device can request, from a server, detailed location data associated with the coarse location. The detailed location data can include location fingerprint data associated with a portion of the venue that includes the coarse location. The mobile device can determine an estimated location that has finer granularity than the coarse location using the location fingerprint data.

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: 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: Methods, systems, and computer program product for generating location fingerprint data for a venue are described. A sampling device surveying a venue can move inside the venue. While the sampling device moves, the sampling device can record environmental readings including, for example, strengths of signals from multiple radio signal sources. The sampling device can take the recording at fixed or various time intervals. Meanwhile, the sampling device can determine, based on a starting point and motion sensor readings, an estimated location of the mobile device for each time the sampling device takes the recordings. The sampling device can display a motion path of the estimated locations and a user interface item for receiving a user input for correcting the estimated locations. The sample device can tag the recorded environmental readings with the corrected locations, and submit the tagged readings to a server for determining a location fingerprint for the venue.

Abstract: Methods, systems, and computer program product for location determination using dual statistical filters are described. A mobile device can determine a location of the mobile device using a particle filter and a Kalman filter. The particle filter can filter candidate locations of the mobile device using measurements of environment variables in the venue. The Kalman filter can filter inputs from a sensor of the mobile device for measuring angular movement of the mobile device. The particle filter and the Kalman filter can be linked by heading of the mobile device. Output of the Kalman filter can be used to determine where to place particles, or candidate locations, in a next iteration of the particle filter. Output from the particle filter can be used to determine a center mode of the Kalman filter and to determine a bias of the sensor for measuring angular movement.

Abstract: Methods, systems, and computer program product for deduplicating location fingerprint data for a venue are described. A system including a location server, or a mobile device, or both, can deduplicate the location fingerprint data. Deduplicating the location fingerprint data can include identifying correlated signal sources the signals of which are mutually dependent such that measurements of one signal source can be used to predict measurements of another. The system can determine a mutual information entropy value for each pair of signal sources, and identify the correlated signal sources based on high mutual information entropy value.

Abstract: A centralized beacon management service includes a beacon database for storing beacon information for different enterprises. The beacon database includes tables that associate beacons deployed by enterprises with beacon information. The beacon information can include information for commissioning the beacons and payload data to be broadcast by the beacon to client devices within communication range of the beacon. Based on proximity sensing data provided by a client device, beacon information is retrieved and sent to the client device over a communication channel (e.g., WiFi, cellular) established with the client 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: Surveying techniques for generating location fingerprint data are described. A mobile device can survey a venue by measuring, at multiple locations at the venue, signals from one or more signal sources. At each location, the mobile device can take multiple measurements of signals. The mobile device can take each measurement at a distinct orientation. The measurements can be used to determine expected measurements of the signals at the venue. Differences between the multiple measurements of signals can be used to determine a variance of the expected measurements. The expected measurements and variance can be designated as location fingerprint data for the venue. The location fingerprint data can be used by mobile devices for determining a location at the venue.