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:
Various arrangements for determining a location of a base station without timing synchronization are presented. A mobile device may determine that it is moving faster than a threshold velocity. The mobile device may capture a first unsynchronized time of arrival (TOA) measurement and determine an associated first location, wherein the first unsynchronized TOA measurement is based on a first unsynchronized timing measurement of a first received reference signal. The mobile device may capture a second unsynchronized TOA measurement and determine an associated second location, wherein the second unsynchronized TOA measurement is based on a second unsynchronized timing measurement of a second received reference signal. Based on the mobile device moving faster than the threshold velocity, the first location, the second location, the first unsynchronized TOA measurement, and the second unsynchronized TOA measurement may be used for determining the location of the base station.
Abstract:
Methods, apparatuses, and/or articles of manufacture are disclosed, which may be employed in a mobile device communicating with a transponder via a near field communications channel. In one example, round trip time of a message may be computed to estimate processing latency contributed by processes occurring within the mobile device and/or the transponder.
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:
Various arrangements for determining a location of a base station without timing synchronization are presented. A mobile device may determine that it is moving faster than a threshold velocity. The mobile device may capture a first unsynchronized time of arrival (TOA) measurement and determine an associated first location, wherein the first unsynchronized TOA measurement is based on a first unsynchronized timing measurement of a first received reference signal. The mobile device may capture a second unsynchronized TOA measurement and determine an associated second location, wherein the second unsynchronized TOA measurement is based on a second unsynchronized timing measurement of a second received reference signal. Based on the mobile device moving faster than the threshold velocity, the first location, the second location, the first unsynchronized TOA measurement, and the second unsynchronized TOA measurement may be used for determining the location of the base station.
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.).