An electronic device may include wireless circuitry that detects the location of external objects. A signal generator may concurrently transmit different radio-frequency ranging signals over two or more transmit antennas. The ranging signals may include waveforms with time-varying frequencies, where each waveform includes frequencies that are non-overlapping with the frequencies of each of the other waveforms at any given time. Antennas may receive reflected versions of the ranging signals and a processor may process the reflected versions of the ranging signals to identify the location of the external objects. This may prevent interference between the ranging signals and may significantly reduce the latency of location detection relative to examples where the ranging signals are transmitted by different transmit antennas in series.

A base station device includes: a beamforming setting information obtaining unit obtaining a plurality of beamforming setting information; a positioning reference signal generation unit generating a plurality of positioning reference signals using each of the plurality of beamforming setting information; a schedule information obtaining unit obtaining schedule information indicating a transmission schedule of the plurality of the positioning reference signals; and a transmission unit transmitting each of the plurality of positioning reference signals based on the schedule information.

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.

A computing device obtains an instance of radio observation data and an associated circumstance parameter. The instance of radio observation data indicates an observed signal strength a radio node observed by a mobile device. The circumstance parameter indicates a characteristic of motion of the mobile device and/or a characteristic of the environment about the mobile device when the instance of radio observation data was captured. The computing device determines a context associated with the instance of radio observation data based at least in part on the circumstance parameter; determining a context-specific signal strength correction based at least in part on the context; generates a corrected signal strength of the radio node based on the observed signal strength and the signal strength correction; and generates and/or updates a radio map based at least in part on the corrected signal strength of the at least one radio node.

Systems and methods for locating tagged objects in remote regions are presented herein, in one embodiment, a method of locating tagged objects in remote regions includes creating a signal strength probability density map by. The method also includes transmitting first packets of data from at least one first tag to a plurality of stations and determining, by a plurality of stations, received signal strength indicator (RSSI) for received first packets of data. The method also includes transmitting, by the plurality of stations, the RSSI to an uplink node; and transmitting, by the uplink node, the RSSI to a database. The method further includes determining, by the database, the signal strength probability density map representative of probabilistic locations of the at least one first tag; and transmitting second packets of data from a second tag to the plurality of stations. Based on the signal strength probability density map and the second packets of data from the second tag, a location of the second tag is determined.

A method is provided that includes obtaining one or more pieces of crowd-sourced information. A respective crowd-sourced information is at least indicative of a location at which the respective crowd-sourced information was gathered. The method determines a set of tiles at least partially based on the one or more geographical areas. For at least one tile, the method obtains one or more pieces of fingerprint information comprised by the one or more pieces of crowd-sourced information that were gathered within the respective area of the respective tile and determines an area type and/or context information indicative of a type of venue located within the respective tile and/or context the respective area of the respective tile is used for. The area type and/or context information is determined at least partially based on the one or more pieces of fingerprint information. A corresponding apparatus and computer program product are also provided.

A method includes obtaining or holding available first radio map information representing a first radio map for a first environment. The method also includes determining, at least partially based on said first radio map information, second radio map information representing a second radio map for a second environment. The second radio map contains or represents a respective radio coverage model for each radio device of a group of radio devices. A portion of the second environment at least partially covers the first environment. A density of radio coverage models contained in or represented by said second radio map in the portion of said second environment and at least partially covering the first environment is higher than a density of radio coverage models contained in or represented by the second radio map in a remaining portion of the second environment. A corresponding apparatus and computer program product are also provided.

An indoor positioning method based on image visual features. A Wi-Fi signal strength value of a Wi-Fi tag closest to a current location of a mobile device is matched with a signal strength list in a map database to obtain a first location of a first Wi-Fi tag with the greatest matching degree. A SURF descriptor of an image of the Wi-Fi tag closest to the current location of the mobile device is matched with SURF descriptors recorded in the signal strength list in the map database to discover an image of a Wi-Fi tag with the greatest matching degree, thereby obtaining a second location of a second Wi-Fi tag corresponding to the image of the Wi-Fi tag with the greatest matching degree. A three location of a three Wi-Fi tag is obtained according to a homography matrix corresponding to the image of the Wi-Fi tag with the largest matching degree and an empirical value of a positioning error. Positioning information of the mobile device is obtained according to the first location, the second location and the third location.

A mobile device is configured for determining a position of the mobile device within buildings, the mobile device including: one or more motion sensors; one or more proximity sensors; a relative feature spot map establishing module; wherein the relative feature spot map establishing module is configured for transmitting the one or more relative feature spot maps to an absolute coordinates determining module configured for determining absolute coordinates of the mobile device; wherein the absolute coordinates determining module is configured for determining the absolute coordinates of the position of the mobile device by determining to which absolute feature spot map of the absolute feature spot maps the one or more relative feature spot map correspond.

A method may include obtaining connection-related data associated with user equipment (UE) devices operating in a network, wherein the UE devices include unmanned aerial vehicles (UAVs) and devices other than UAVs. The method may include storing the connection-related data; identifying, in the stored connection-related data, data associated with known UAVs and filtering the stored connection-related data based on a distance associated with a known UAV. The method may further include training a machine learning classifier using the filtered data and executing the machine learning classifier to identity UAVs operating in the network.