Abstract: A method of generating a user?s location using a mobile device. The method comprises, determining a signal snapshot on the mobile device, the signal snapshot describing characteristics of unregulated radio frequency (RF) transmissions detectable by the mobile device. Generating the user?s location on the mobile device using the signal snapshot and at least one additional input from the mobile device. The generating and determining are iteratively repeated. The unregulated RF transmission can comprise WiFi signals.

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: Methods, systems, and computer program products for correcting in-venue location estimation using structural information are described. A mobile device can use wireless location technologies and dead reckoning to determine an estimated location of the mobile device in a venue. The mobile device can compare the estimated location with a map of the venue. Upon determining that the estimated location conflicts with a structural constraint, the mobile device can adjust the location estimation using the structural information. Adjusting the location estimation can include adjusting a statistical filter that provides estimation of the location and changing a heading of the mobile device used in the dead reckoning.

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, 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: 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 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: 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: 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: 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: 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 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, 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 location transition determination are described. A mobile device can use location fingerprint data and sensor readings to determine a transition of the mobile device into or out of a portion of a venue by using particle filters. When the mobile device determines that the mobile device is located at a first portion of the venue, e.g., on a given floor, the mobile device can introduce candidate locations, or particles, on a second portion of the venue and candidate locations outside of the venue. If estimated locations at the first portion of the venue do not converge, the mobile device can increase weight of the candidate locations that are outside of the first portion of the venue to detect possible transition to the second portion of the venue or to outside of the venue.

Abstract: Survey data for an environment is used to predict the accuracy of a position estimate in the environment and whether or not more survey data may improve that accuracy. In some implementations, a user performs a site survey of an environment by observing the strengths of radio frequency signals at various survey points in the environment. An expected positioning accuracy of the surveyed environment can be determined using the new survey data collected and optionally historical survey data for the environment. The user can be informed about the usefulness of collecting additional survey data and/or the expected positioning accuracy in the environment.

Abstract: A method of generating a user?s location using a mobile device. The method comprises, determining a signal snapshot on the mobile device, the signal snapshot describing characteristics of unregulated radio frequency (RF) transmissions detectable by the mobile device. Generating the user?s location on the mobile device using the signal snapshot and at least one additional input from the mobile device. The generating and determining are iteratively repeated. The unregulated RF transmission can comprise WiFi signals.