Disclosed is an approach for improving positioning quality via a feedback loop between a radio-based positioning system and a client device, such as a tracking system. In particular, the tracking system or other client device may request and then receive a position estimate from the positioning system. The tracking system could then make a determination that the position estimate is incorrect, such as by determining that it is an outlier relative to a location trace, for instance. Responsive to this determination, the tracking system may transmit, to the position system, an indication of radio node(s) associated with the incorrect position estimate. Based on this indication, the positioning system could then exclude one or more of those radio node(s) from a radio map, thereby improving quality of the radio map and in turn quality of future position estimates, among other advantages.

It is disclosed to transmit query information that comprises information on at least one property of a device (101), to obtain response information that comprises information indicating that the device has been determined, based at least on the information on the at least one property of the device comprised in the transmitted query information, to be one of suited to provide fingerprint information and unsuited to provide fingerprint information, wherein fingerprint information comprises information on at least one radio node and/or on at least one cell observed at at least one position of the device (102). It is further disclosed to obtain the query information, to determine if the device is one of suited to provide fingerprint information and unsuited to provide fingerprint information, and to transmit the response information.

A method for geolocating a terminal of a wireless communication system, based on a learning method making it possible to estimate the geographical position of a terminal using both a radio signature corresponding to a set of values representative of the quality of radio links existing between the terminal located at the sought position and a plurality of base stations of the wireless communication system, as well as a reference data set associating radio signatures with known geographical positions. To limit the complexity of the learning algorithm and to make it resistant to topology changes of the access network, each radio signature contains a selection of N values among the set of measured values, as well as the geographical positions of the corresponding base stations.

A map data correcting method for correcting map data used in a vehicle using a controller includes executing a first correction process of uniformly offsetting the map data as a whole to reduce a first error that is a general position error of the map data and executing a second correction process of reducing a second error that is a position error still remaining in the map data even after uniformly offsetting the map data as a whole.

Embodiments described herein provide for an electronic device comprising a set of classifiers that can determine whether the electronic device is likely on an airplane. Upon a determination that the electronic device is likely on an airplane, one or more wireless radios on the electronic device (e.g., an ultra-wideband ranging radio, a cellular radio, etc.) may be disabled or a prompt can be displayed to enable a user to place the device into airplane mode.

A method of determining locations of access points in a venue, one or more electronic devices configured to receive radio signals from the access points, the method comprises: generating and storing, at a server in communication with the one or more electronic devices, a database comprising information received from the one or more electronic devices, the information comprising: values associated with a radio signal characteristic of the radio signals at locations of RF scan capture by the one or more electronic devices and access point identifiers representing access points corresponding to the values, the locations of RF scan capture being unknown at a time of RF scan capture; assigning correlation values to quantify similarity between the values for pairs of the access point identifiers; mapping the correlation values to correlation ranges based on known locations of at least two of the access points; determining the locations of the access points based on the correlation ranges and the known locations.

Inter-alia, a method is disclosed comprising: determining at least one fingerprint information indicative of at least one identifier of a cellular and/or a non-cellular radio network, wherein based on the identifier at least one cell of the cellular radio network and/or at least one node of the non-cellular radio network is identifiable; sending the at least one fingerprint information, wherein the at least one fingerprint information comprises or is accompanied by a first set of tuning parameters in case such a set of tuning parameters is stored in a memory; receiving a second set of tuning parameters in response to the sending of the at least one fingerprint information, wherein the first and/or the second set of tuning parameters respectively comprise a correction value for compensating a bias for the at least one fingerprint information; and storing the received second set of tuning parameters, wherein the first set of tuning parameters is updated with the second set of tuning parameters enabling to iteratively converge the set of tuning parameters to the actual bias. It is further disclosed an according apparatus, computer program and system.

A robot and a method for localizing a robot are disclosed. The method for localizing a robot may include acquiring communication environment information including identifiers of access points and received signal strengths from the access points, generating an environmental profile for a current position of the robot based on the acquired communication environment information, comparing the generated environmental profile with a plurality of learning profiles associated with a plurality of regions, respectively, determining a learning profile corresponding to the environmental profile, based on the comparison, and determining a region associated with the determined learning profile as a current position of the robot. In a 5G environment connected for the Internet of Things, embodiments of the present disclosure may be implemented by executing an artificial intelligence algorithm and/or machine learning algorithm.

Provided is a computer-implemented method including acquiring a first pieces of observation data that include a position of a mobile object and a received signal strength of a wireless signal observed by the mobile object; and correcting each position of the mobile object included in each piece of observation data of the first pieces of observation data using one position of the mobile object at a time before the received signal strength included in the piece of observation data is observed.

A method includes obtaining or causing obtaining a reference position of the at least one apparatus based on at least one reference tag located at a predefined position of a map represented by map data of at least part of a structure. The method also includes obtaining or causing obtaining identification information of at least one first wireless access point based on a radio signal of the at least one first wireless access point. The method further includes associating or causing associating a first position determined based on the reference position with at least one of: a position estimate determined based on the identification information of the at least one first wireless access point and a radiomap represented by radiomap data of at least part of a structure; of the identification information of the at least one first wireless access point. A corresponding apparatus is also disclosed.