Abstract: Techniques for estimating the current state (e.g., position, velocity) of a mobile device based on motion context and multiple input observation types are disclosed. In some implementations, an Extended Kalman Filter (EKF) formulation is used to combine multiple input observations received from a variety of sources (e.g., WiFi, cell, GPS) to compute a minimum error state estimate. In some implementations, the EKF is updated using position estimates from an active cell and/or a candidate active cell during a cell-hopping event.

Abstract: Among other things, we describe a method that includes receiving, on a mobile device, an indication that an application executing on the mobile device has entered a background state, receiving, from the application, a value indicating a condition for providing location data to the application, disabling a resource associated with the application, while the resource associated with the application is disabled, storing location data received from a location system of the mobile device, and when the condition indicated by the value is met, enabling the resource associated with the application, and providing the stored location data to the application.

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: Information such as altitude or speed limits for a specific geographic region can be utilized to improve position and velocity estimation for a mobile device using inequality constraints. The inequality constraints can be used as pseudo-measurements when needed to improve position and velocity estimation.

Abstract: Methods, program products, and systems of location estimation using multiple wireless access gateways are disclosed. In general, in one aspect, a mobile device can scan and detect multiple wireless access gateways. The mobile device can determine an initial estimate of distance between the mobile device and each wireless access gateway. The mobile device can receive, from a server, location data of the detected wireless access gateways. The location data can include an estimated location of each wireless access gateway, an uncertainty of the estimated location, and a reach of each wireless access gateway. The mobile device can assign a weight to each estimated location using the uncertainty, the reach, and the initial estimate. The mobile device can estimate the location of the mobile device using the weighted locations.

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, program products, and systems of location estimation using multiple wireless access gateways are disclosed. In general, in one aspect, a mobile device can scan and detect multiple wireless access gateways. The mobile device can determine an initial estimate of distance between the mobile device and each wireless access gateway. The mobile device can receive, from a server, location data of the detected wireless access gateways. The location data can include an estimated location of each wireless access gateway, an uncertainty of the estimated location, and a reach of each wireless access gateway. The mobile device can assign a weight to each estimated location using the uncertainty, the reach, and the initial estimate. The mobile device can estimate the location of the mobile device using the weighted locations.

Abstract: In some implementations, a location of a mobile device can be determined by calculating an average of the locations of wireless signal transmitters that have transmitted signals received by the mobile device. In some implementations, locations are weighted with coefficients and the average is a weighted average. In some implementations, the locations of the wireless signal transmitters are determined based on identification information encoded in the wireless signals received by the mobile device. The identification information can include an identifier for a wireless signal transmitter. The identification information can include characteristics of the received wireless signal that can be used to identify wireless signal transmitters. In some implementations, identification information from one signal can be combined with identification information from another signal to determine a location of a wireless transmitter.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a motion state of a mobile device. Accelerometer data is received from accelerometer sensors onboard the mobile device, wherein the accelerometer data represents acceleration of the mobile device in three-dimensional space. An accelerometer signal vector representing at least a force due to gravity on the mobile device is determined. Two-dimensional accelerometer data orthogonal to the accelerometer signal vector is calculated. A motion state of the mobile device is determined based on the two-dimensional accelerometer data.

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 apparatus, including computer programs encoded on a computer storage medium, for determining a presumed activity associated with a mobile device. A plurality of sensor values detected by one or more sensors onboard the mobile device is received over a period of time. A plurality of derived values is calculated from the plurality of sensor values. The derived values are selectively combined to generate one or more abstract values. A presumed activity is identified from a plurality of possible activities based on a level of similarity between the one or more abstract values and expected values of each of the plurality of possible activities that correspond to the one or more abstract values.

Abstract: Techniques for estimating the current state (e.g., position, velocity) of a mobile device based on motion context and multiple input observation types are disclosed. In some implementations, an Extended Kalman Filter (EKF) formulation is used to combine multiple input observations received from a variety of sources (e.g., WiFi, cell, GPS) to compute a minimum error state estimate. In some implementations, the EKF is updated using position estimates from an active cell and/or a candidate active cell during a cell-hopping event.

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 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. The server can calculate an effective location 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 location of the wireless access gateway. The server can send the effective location of the wireless access gateway to other mobile devices for estimating locations of the other mobile devices.