Systems and methods are provided for a network node in a wireless communication network for generating an estimated location of a user equipment (UE). The method includes: receiving a request positioning message, determining the estimated location of the UE using a positioning technique, and adjusting the estimated location of the UE based on one or more spatial distribution probabilities associated with one or more locations in a vicinity of the estimated location.

A method for locating an object in a plane that includes a first so-called learning phase for informing a database, and a second so-called location phase, the location phase using a classifier operating using the database comprising information representing the transmission of signals between the object and antennas, for determining a probability of location of the object in zones of the plane, the location phase further using a neural network, for determining, for the zone of the plane for which the location probability is the highest, the position of the object in the form of coordinates in the plane. The invention also relates to a location device implementing the method.

A positioning system for integrating machine learning positioning models and a positioning method for the same are provided. The positioning system includes a DUT (device under test) and a scalable backend subsystem. The DUT obtains current WI-FI® fingerprint data. The scalable backend subsystem communicates with the DUT, and includes a database server, a processing unit, a plurality of machine learning positioning service modules, and a DUT service module. The database server stores a plurality of records of machine learning positioning model data, configuration data and setting data defining a positioning inference path. The DUT service module includes a positioning inference module, the positioning inference module receives the current WI-FI® fingerprint data, and sequentially inputs the current WI-FI® fingerprint data into the machine learning positioning service modules according to the positioning inference path to sequentially obtain and integrate multiple positioning inference results so as to generate positioning results.

Examples herein involve estimating a first position of a mobile device based on first communication signals, assigning a first set of particles to a number of respective first sampling locations within a threshold distance of the first position, adjusting the assignment of the first set of particles to second sampling locations based on movement of the mobile device, and estimating a second position of the mobile device based on the second sampling locations.

Method and system for calibrating a tag location system comprising the steps of placing one or more test radio frequency RF, communication circuits at known locations within a space to be calibrated. Receiving a signal from each one of the test RF communication circuit. Determining a property of each received signal, wherein the determined property is associated with the known location of the test RF communication circuit that generated the signal. Generating a model of the calibrated space from the determined properties of the each received signal and associated known locations, wherein the model provides an output indicating the location of a signal-producing tag based on signals received from the test RF communication circuits and associated known locations.

Inter-alia, a method is disclosed comprising: determining initial positioning information indicative of a respective location of one or more radio nodes; determining distances information indicative of at least one estimated distance between at least two locations of the radio nodes in case the initial positioning information comprises or represents at least two respective locations of the radio nodes; determining cluster information based on the at least one estimated distance; and determining cluster location information indicative of a respective location of one or more clusters. It is further disclosed an according apparatus, computer program and system.

A method for generating radio tomographic images is provided. A plurality of transceivers positioned around a region to be imaged is divided into a plurality of pixels. A control apparatus is configured to cause each of the plurality of transceivers in turn to send a signal to each of the other transceivers. The control apparatus is further configured to determine an attenuation in the received signals, generate weighing, derivative, and attenuation matrices from the signals, group the pixels into a plurality of provinces, select each province in turn and solve for a change in attenuation in each of the pixels while setting the pixels in other provinces to zero, aggregate solutions from each of the provinces into a rough estimate, re-solve each province using the aggregated rough estimate, aggregate the re-solved solutions from each province into a refined estimate, and generate an image from the refined estimate.

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 method is disclosed that includes gathering one or more pieces of crowdsourcing information indicative of one or more positions at which the at least one mobile device is located during the gathering. The method also includes collecting one or more performance indicators indicative of measurable information associated with the gathering of the one or more pieces of crowdsourcing information. The one or more performance indicators enable an evaluation of the gathering of the one or more pieces of crowdsourcing information. The method further includes providing the collected one or more performance indicators. An according apparatus, computer program and system are also disclosed.

A method, apparatus, computer program or system includes obtaining fingerprints including a piece of position information and gathered in a venue; determining a first metric based on the fingerprints indicative of a quality value of the obtained fingerprints that indicates for each piece of position information of the fingerprints whether or not the quality and/or a quantity of the obtained fingerprints with respect to the piece of position information is sufficient; determining a second metric based on the obtained fingerprints indicative of a quality value of an infrastructure of the venue, the fingerprints are gathered from one or more radio nodes of the infrastructure, and the second metric indicates for each of the pieces of position information of each of the respective fingerprints whether or not the quality of the infrastructure is sufficient; determining a third metric indicative of an evaluation of the quality of the infrastructure of the venue based on the second metric; and outputting the first metric, the second metric and/or the third metric.