A computer implemented method in a communications network for determining location information about an actual location of a drone comprises obtaining (302) a reported location of the drone at a first time point and obtaining (304) a measurement of radio conditions between the drone and a node in the telecommunications network, at the first time point. The method then comprises predicting (306) radio conditions at one or more locations related to the reported location of the drone, and determining (308) the location information about the actual location of the drone based on the measured radio conditions and the predicted radio conditions.

An indoor geolocation system for determining a location in three-dimensional space includes a plurality of base stations and a mobile device movable about an indoor environment in three dimensions. The mobile device detects electromagnetic signals in the indoor environment emitted by devices other than the base stations, and generates a signal profile based on the signals. The mobile device transmits the signal profile to one or more of the base stations, which forward the signal profile to a remote server. The system determines a location of the in three-dimensional space of the mobile device by comparing the signal profile to data regarding signal profiles at a plurality of locations in the indoor environment. The data regarding signal profiles in the indoor environment may have been captured by a detection device other than the mobile device at a time prior to the detection of the electromagnetic signals by the mobile device.

Disclosed are systems, methods, and non-transitory media for performing passive radio frequency (RF) location detection operations. In some aspects, RF data, such as RF signals including channel state information (CSI), can be received from a wireless device. The RF data can be provided to a self-supervised machine-learning architecture that is configured to perform three-dimensional (3D) object location estimation.

Aspects of the subject disclosure may include, for example, utilizing a Radio Frequency (RF) fingerprint model for a demarcated area associated with a plurality of anchors. A machine learning process is applied to an RF training set that includes RF characteristics for a plurality of messages and locations determined for one or more mobile devices within the demarcated area. The plurality of messages are wirelessly transmitted as part of radio measurement locating processes to determine the mobile device locations. A first location for the first mobile device can be determined based on the RF fingerprint model according to particular RF characteristics of first messages being received by or received from the first mobile device. Other embodiments are disclosed.

Time and effort to generate an electric field map to realize high-accuracy positioning is reduced. In an electric field map generation device (10) that generates an electric field map in which RSSI obtained when radio waves transmitted from a beacon (60) are observed at each of a plurality of points in the same space is associated with each of coordinates at the plurality of points, a radio wave strength acquisition unit (12) acquires RSSI observed at each of at least three observation points different in distance from the beacon (60), a space propagation characteristics estimation unit (14) estimates space propagation characteristics of the radio waves transmitted from the beacon (60) in the same space using the acquired RSSI at the observation points, a radio wave strength estimation unit (16) estimates RSSI at estimation points on the basis of the estimated space propagation characteristics and distances between the estimation points different from the observation points and the beacon (60), and a generation unit (18) generates the electric field map (26) using the RSSI at the observation points and the estimation points.

Various embodiments relate to the generation and/or use of a positioning map comprising instances of neighbor-cell information that each comprising, are associated with, and/or indexed by a respective globally unique identifier. In an example embodiment, a processor determines a locally unique identifier and an estimated location of an observed neighbor cell. The processor defines a globally unique identifier for the neighbor cell comprising the locally unique identifier and an indication of the estimated location of the neighbor cell. The processor generates a positioning map to include an instance of neighbor-cell information corresponding to the neighbor cell. The instance of neighbor-cell information comprises, is associated with, and/or is indexed by the defined globally unique identifier. The instance of neighbor-cell information may be accessed via an observed locally unique identifier when determining a position estimate for a computing entity that observed a neighbor cell corresponding to the observed locally unique identifier.

A method, computer readable storage device and an apparatus for locating a mobile endpoint device in an indoor environment are disclosed. For example, the method generates a location map having a predicted signal strength for each respective location on the location map, receives a signal strength associated with the mobile endpoint device within the indoor environment, compares the signal strength to the location map having the predicted signal strength for each respective location on the location map and locates the mobile endpoint device as being at a particular location within the indoor environment.

A system for determining a position of a user in a building includes a control device, a plurality of stationary radio signal transmission devices, a radio signal receiving device, and a signal processing device. The signal processing device determines primary channel impulse responses based on the radio signals received by the receiving device. The signal processing device also determines a secondary channel impulse response based on a secondary radio signal received by the receiving device from a mobile device of a user. The channel impulse responses are evaluated to determine degrees of similarity that indicate how similar a first primary channel impulse response and the secondary channel impulse response are to one another. For each degree of similarity, a distance of the mobile device from the transmission device corresponding to the degree of similarity is determined. A position of the mobile device is determined based on the distances.

A system for synthesizing signal of user equipment and a method thereof are provided. The system includes a physical channel modeler and a physical channel training module. The physical channel modeler receives geo information of a field under test of and a sparse real physical field channel feature to build a physical channel model. The physical channel modeler estimates a plurality of predefined positions of the geo information to obtain a plurality of simulated physical field channel features corresponding to the predefined positions. The physical channel training module receives and performs training on the geo information, the sparse real physical field channel feature and the simulated physical field channel features by using an AI algorithm to perform an inference of a fully real physical field channel feature.

A method, a system, and a computer program product for localization of wireless devices. At least one of mapping and wireless data describing a physical environment is received. The physical environment includes one or more wireless access points. Using the received mapping and/or wireless data, a location of each of the wireless access points is determined. Using the determined location of the wireless access points, one or more wireless devices positioned in the physical environment are located.