A method for locating a wireless tag includes transmitting a first set of signals by the wireless tag in a first time period, receiving the first set of signals by a base station of at least one base station during the first time period, generating a first set of distance values according to the first set of signals received by the base station, generating a first subset of distance values by removing at least a maximum distance value and a minimum distance value from the first set of distance values, obtaining a first distance between the wireless tag and the base station by averaging the first subset of distance values, and identifying a first location of the wireless tag according to at least one first distance.

Methods, systems, and devices are described herein. One method can include determining a cross validation model using a user input, estimating a movement of a device from a first location to a second location using the cross validation model and the user input, and determining the second location of the device using the estimated movement of the device.

A method for determining user activity range includes: determining a moving state of a user; determining, when the moving state of the user is a first state, geographical location information of the user corresponding to the first state; determining, when the moving state of the user is a second state, a geographical location information set of the user corresponding to the second state; and collecting, in a first predetermined time period, geographical location information of the user corresponding to at least one first state, and a geographical location information set of the user corresponding to at least one second state, and combining the geographical location information of the user determined in the first state in the first predetermined time period with the geographical location information set of the user determined in the second state in the first predetermined time period, so as to determine a user activity range.

An apparatus and method for predicting mobility based on relative mobile characteristics are disclosed herein. The method of predicting mobility based on relative mobile characteristics includes detecting, by each of a plurality of sensor nodes of a sensor network including the plurality of sensor nodes, a change in relative location with respect to an observed node; recording, by the each of the plurality of sensor nodes, information about the detected change in relative location; generating, by the each of the plurality of sensor nodes, a mathematical model for a possibility of approach of the observed node with respect to the each of the plurality of sensor nodes using the recorded relative location change information; and predicting probabilistically, by the each of the plurality of sensor nodes, whether the observed node will approach the each of the plurality of sensor nodes using the mathematical model.

A method for locating a wireless tag includes transmitting a first set of signals by the wireless tag in a first time period, receiving the first set of signals by a base station of at least one base station during the first time period, generating a first set of distance values according to the first set of signals received by the base station, generating a first subset of distance values by removing at least a maximum distance value and a minimum distance value from the first set of distance values, obtaining a first distance between the wireless tag and the base station by averaging the first subset of distance values, and identifying a first location of the wireless tag according to at least one first distance.

System and methods are disclosed to use information available on the state of mobile devices in a heuristics-based approach to improve motion state detection. In one or more embodiments, information on the WiFi connectivity of mobile devices may be used to improve the detection of the in-transit state. The WiFi connectivity information may be used with sensor signal such as accelerometer signals in a motion classifier to reduce the false positives of the in-transit state. In one or more embodiments, information that a mobile device is connected to a WiFi access point (AP) may be used as heuristics to reduce the probability of falsely classifying the mobile device in the in-transit state when mobile device is actually in the hand of a relatively stationary user. Information on the battery charging state or the wireless connectivity of the mobile devices may also be used to improve the detection of in-transit state.

A method includes obtaining signal information based on wireless signals communicated between an electronic device and a target device. The method also includes obtaining motion information based on movement of the electronic device. The method further includes identifying first location information based on the signal information, the first location information indicating whether the target device is within a field of view (FoV) of the electronic device. Additionally, the method includes identifying second location information based on the motion information and the signal information, the second location information indicating whether the target device is within the FoV of the electronic device. The method also includes determining that the target device is within the FoV or outside the FoV of the electronic device based on at least one of the first location information or the second location information.

The embodiments relate to an apparatus and a method, where the apparatus includes circuitry configured for listening and receiving a positioning measurement request from a location server at a first point of time; circuitry configured for determining whether a set of positioning metrics need to be measured, wherein the determining is based on an output from a machine learning classifier to which channel metrics between two points of time is given as input; whether the output indicates a new measurement, circuitry configured for performing the measurement of positioning metrics from all transmission points; or if not, circuitry configured for determining which subset of transmission points needs to be measured and circuitry configured for performing the measurement of such subset of transmission points; and circuitry configured for reporting the measured positioning metrics to the location server.

A positioning system includes a plurality of beacon modules and a communication device. Further, the communication device includes a calculation unit calculating action state data that are used for determining an action state of a user who carries the communication device; a searching unit selectively searching for one of the beacon modules in accordance with the action state of the user, the action state being determined based on the action state data calculated by the calculation unit, and a derivation unit deriving positional information of the user based on a response signal transmitted from the one of the beacon modules having been searched for by the searching unit.

A method and apparatus are provided for obtaining and using multiple timing advance measurements for positioning purposes. A first timing advance measurement is obtained. The first timing advance measurement is indicative of a measurement based on one or more signals of a first serving cell. A second timing advance measurement is also obtained. The second timing advance measurement is indicative of a measurement based on one or more signals of a second serving cell. A location estimate is determined based at least partially on the first timing advance measurement and the second timing advance measurement.