Presented herein are techniques for assigning Ultra-Wideband (UWB) anchors for client ranging. A control device can monitor UWB ranging between a mobile device and a primary anchor. In response to determining that a signal strength between the mobile device and the primary anchor is below a threshold, the control device can identify anchors for which the mobile device has had a signal strength above the threshold during a period of time, and select one of the anchors as a new primary anchor for the mobile device. For example, the control device can select the new primary anchor based on a relative collision tolerance mapping for the new primary anchor and at least one other anchor within a UWB range of the new primary anchor. The control device can send a command causing UWB ranging to be performed between the mobile device and the new primary anchor.

Systems and methods including mobile devices determining their locations using location determination units, such as GPS receivers. A computing device generates a statistical measurement of location pattern changes in relation to a predetermined region for a predetermined period of time. The measurement is generated from a difference between the average response of exposed mobile devices and the average response of non-exposed mobile devices, where each response of a mobile device is computed by: determining a sequence of time instances of the mobile devices being at locations within the region, generating a time series of visitation measurements from the time sequence, applying at a time instance an anti-symmetric weight function having an exponential distribution to the time series to generate the response corresponding to the time instance, and summing the responses corresponding to time instances falling within the predetermined period of time as the response of the mobile device.

UE positioning is added by use of a reconfigurable reflecting surface (e.g., IRS). The IRS is configured to adjust elements of the surface. The configuration may include signal switching on or off, signal phase, group delay, or signal amplitude. Positioning reference signal transmissions are performed that have line of sight to the UE and that reflect off the IRS. The UE takes measurements for the transmissions and can determine measurement(s) of angle of arrival or time of arrival or reference signal received power, and/or determine a channel estimation. Multiple methods are proposed to provide UE positioning.

Provided are a location prediction method and apparatus, a node and a storage medium. The method includes: a server obtaining a measurement report message sent by a base station, where the measurement report message includes historical location measurement information reported by at least one UE and/or historical location measurement information of the at least one UE measured by the base station; and the server determining prediction location information of the at least one UE at a first time point or in a first time period according to the measurement report message, and sending notification information to the base station according to the prediction location information. By introducing measurement information in multiple dimensions, the accuracy of predicting the location of UE can be effectively improved.

A method for passively locating a non-movable transmitter on the ground implemented by a group of at least one receiving station, each of the receiving stations comprising a detector of radars and a time reference, the set of time references being mutually synchronized, the transmitter transmitting a set of periodic pulses, wherein a first estimation of the position of the transmitter is carried out by the Bancroft scheme on the basis of the mean arrival times of the pulses transmitted by the transmitter at the level of each station of the group of at least one receiving station, the result obtained being used thereafter as point for initializing a maximum likelihood scheme so as to converge toward the position of the transmitter.

The present invention relates to a robust coverage method for relay nodes in a double-layer structure wireless sensor network. The present invention is a local search based relay node 2-coverage deployment algorithm which, by means of reducing the global deployment problem to a local deployment problem, achieves optimal deployment whilst ensuring robustness. The method specifically comprises two steps: first 1-coverage and second 1-coverage, wherein the first 1-coverage comprises the three steps of construction of relay node candidate deployment locations, grouping of sensor nodes and local deployment of relay nodes, wherein the sensor nodes are grouped by means of a novel grouping method, and the complexity of the algorithm is reduced whilst ensuring optimal deployment. The second 1-coverage adjusts a threshold, selects from every group the sensor nodes covered by just one relay node, and uses a 1-coverage method to re-implement 1-coverage of the sensor nodes, thereby ensuring robustness, reducing the number of relay nodes deployed, and shortening the problem-saving time.

An illustrative UE locator system determines a time measurement indicative of a signal travel time between a UE device and an access point device. The signal travel time corresponds to an apparent distance, presuming a line-of-sight (LoS) signal travel path, between a location of the UE device and a location of the access point device. The UE locator system also accesses, from a bias suppression datastore, bias suppression data configured for use in suppressing an influence of a bias between the apparent distance and a true distance between the location of the UE device and the location of the access point device. The bias is associated with a non-line-of-sight (NLoS) signal travel path between the UE device and the access point device. Based on the time measurement and the bias suppression data, the UE locator system estimates the location of the UE device. Corresponding methods and systems are also disclosed.

Systems and methods are provided for determining location area of mobile user devices. In a mobile device, a first area probability as a likelihood that the mobile device is located within a first area and a second area probability indicative of the mobile device being located within a second area may be determined, with the first area probability determined based on a first probability and a second probability, and the second area probability determined based on a third probability and a fourth probability. The first probability, second probability, third probability, and fourth probability are determined based on assessing strength received in the respective area from a first base station or a second base station. A determination of whether the mobile device is located in the first area or the second area is made based on the first area probability and the second area probability.

Disclosed are apparatus and methods for providing outputs; e.g., location estimates, based on measurement bins (MBs) determined by a computing device. An MB can be associated with a wireless signal emitter (WSE), and can include a mean signal strength value (SSV) and a standard deviation of SSVs for each WSE associated with the MB. The computing device can designate a WSE. The computing device can determine a collection of the MBs associated with the designated WSE. The computing device can train a mean Gaussian process for the designated WSE based on the mean SSV and the standard deviation of SSVs of the collection of MBs. The mean Gaussian process can be associated with a covariance matrix having a diagonal entry based on a standard deviation of SSVs of an MB in the collection of MBs. The computing device can provide an estimated location based on the trained mean Gaussian process.

Techniques for predictive user assistance are described. A mobile device can learn movement patterns of the mobile device. The mobile device can construct a state model that is an abstraction of locations where the mobile device stayed for sufficient amount of time. The state model can include states representing the locations, and transitions representing movement of the mobile device between the locations. The mobile device can use the state model, a current location of the mobile device, and a current time to determine a predicted future location of the mobile device at a given future time. Based on the predicted location and the given future time, the mobile device can predict what assistance a user of the mobile device may request. The mobile device can then provide the assistance to the user before the given future time.