Abstract:
Various techniques are provided that may be implemented at one or more of a plurality of co-located mobile devices. For example, a first mobile device may identify a plurality of location determination tasks, transmit a request indicative of a subset of the plurality of location determination tasks to be performed by a second mobile device, and receive a response to the request.
Abstract:
Techniques are provided which may be implemented using various methods and/or apparatuses to determine time difference of arrival of signals from two base stations as received at a mobile device, to use the time difference of arrival to determine differential forward link calibration for at least two base stations, and also to determine location using the differential forward link calibration for at least two base stations, determined using the time difference of arrival of signals from at least two base stations as received by a mobile device.
Abstract:
Techniques provided herein are directed toward resolving a direction of travel of a mobile device based on MEMS sensor data by identifying a type of motion the mobile device is subject to and offsetting vertical acceleration data with horizontal acceleration data by a predetermined time offset based on the identified type of motion. The offset vertical and horizontal acceleration data can then be used to resolve, with an increased accuracy, a direction of travel of the mobile device.
Abstract:
Disclosed is a method and apparatus for correcting clocks for a plurality of local transmitters. In one embodiment, the functions implemented include: receiving base station time from a plurality of local transmitters; assigning each local transmitter of the plurality of local transmitters to at least one subset of a plurality of subsets, wherein each subset corresponds to a single base station, and wherein each local transmitter in a particular subset receives time from a base station corresponding to the subset; selecting a single subset of local transmitters as a reference subset of local transmitters, the reference subset corresponding to a reference base station, each non-reference subset corresponding to a non-reference base station; determining a time difference between the reference base station and each of the non-reference base stations based on time received from one or more local transmitters that belong in more than one of the subsets; and transmitting the time difference to respective non-reference subsets of local transmitters.
Abstract:
Techniques are provided which may be implemented using various methods and/or apparatuses to determine time difference of arrival of signals from two base stations as received at a mobile device, to use the time difference of arrival to determine differential forward link calibration for at least two base stations, and also to determine location using the differential forward link calibration for at least two base stations, determined using the time difference of arrival of signals from at least two base stations as received by a mobile device.
Abstract:
Various techniques are provided that may be implemented at one or more of a plurality of co-located mobile devices. For example, a first mobile device may identify a plurality of location determination tasks, transmit a request indicative of a subset of the plurality of location determination tasks to be performed by a second mobile device, and receive a response to the request.
Abstract:
Techniques provided herein are directed toward enabling on-device learning to create user-specific movement models that can be used for dead reckoning. Because these moving models are user-specific, they can be later used to identify user-specific motions in a manner that provides for a dead reckoning location estimation. In some embodiments, these models can be focused on pedestrian movement, based on the repetitive motion that occurs when a user takes a stride (walking, jogging, running, etc.) or other repetitive motion (swimming, riding a horse, etc.).
Abstract:
Techniques provided herein are directed toward enabling on-device learning to create user-specific movement models that can be used for dead reckoning. Because these moving models are user-specific, they can be later used to identify user-specific motions in a manner that provides for a dead reckoning location estimation. In some embodiments, these models can be focused on pedestrian movement, based on the repetitive motion that occurs when a user takes a stride (walking, jogging, running, etc.) or other repetitive motion (swimming, riding a horse, etc.).
Abstract:
A method of performing functions by proxy for a set of associated proximate devices is disclosed. In some embodiments, the method may comprise associating a set of user equipments (UEs), wherein upon determination that a first UE in the set is unavailable for performing a requested function, at least one alternate second UE in the associated set of UEs is selected, wherein the at least one second UE is proximate to the first UE and the at least one second UE is available for performing the requested function. The performance of the requested function on the at least one second UE is initiated.
Abstract:
Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate or support one or more operations or techniques for hybrid RTT and TOA positioning, such as for use in or with a mobile communication device, for example.