In an aspect, a UE obtains information (e.g., UE-specific information, etc.) associated with a set of triggering criteria for a set of neural network functions, the set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedure, obtains positioning measurement data associated with a location of the UE, and determines a positioning estimate for the UE based at least in part upon the positioning measurement data and at least one neural network function from the set of neural network functions that is triggered by at least one triggering criterion from the set of triggering criteria.

In an aspect, a network component transmits, to a UE, at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE, the at least one neural network function being generated dynamically based on machine-learning associated with one or more historical measurement procedures. The UE may obtain positioning measurement data associated with the UE, and may process the positioning measurement data into a respective set of positioning measurement features based on the at least one neural network function. The UE may report the processed set of positioning measurement features to a network component, such as the BS or LMF.

A system and method determining an error radius reflecting the accuracy of a calculated position of a processing device is provided. A data structure includes an error radius mapped to a scaled geographic area or “tile” comprising an area in which a calculated position may be determined. The data structure may include a plurality of first fields identifying a scaled geographic area based on a global projection reference system, and a plurality of second fields identifying, for each of the first fields, a position error radius associated with a scaled geographic area and level. For any calculation of an inferred position based on beacon observations, a rapid lookup of the corresponding scaled area including the new inferred position in the data structure returns an error radius for the new inferred position.

A tracking device for use when performing astrophotography comprises a guider camera and at least one tilt stage, with the topmost of the tilt stages arranged to support an astrophotography camera and the guider camera. Actuators are coupled to the tilt stages such that the astrophotography and guider cameras can be tilted about three axes. The guider camera and actuators are connected to electronics which include a computer programmed to operate in a calibration mode and a tracking mode. In calibration mode, a calibration procedure determines the effect of each actuator on the positions of at least two objects within the field-of-view (FOV) of the guider camera. In tracking mode, the actuators are operated as needed to maintain the positions of the at least two objects constant within the said FOV.

A system and method for estimating a position of an electronic label in an area are provided. A plurality of devices is distributed over the area. Information is obtained about signal strengths recorded at the respective devices when attempting to receive a wireless signal transmitted by the electronic label. Weights are formed for positions associated with the devices, based on the signal strengths recorded at the corresponding devices. The position of the electronic label is estimated as a weighted average including the positions, wherein the positions are weighted by the weights. In some embodiments, information is instead obtained about signal strengths recorded at the electronic label when attempting to receive wireless signals transmitted by the respective devices. The present method may for example be employed to estimate a position of a product in a retail store.

Methods and apparatus relating to use of actual and/or virtual beacons are described. Virtual beacons are virtual in that an actual beacon need not be transmitted but a rather a virtual beacon transmitter at a desired location maybe considered to transmit virtual beacons. In some embodiments a set of beacon transmitter information for one or more beacons is supplied to devices in a communications system. The beacon transmitter information indicates transmission power and location of actual and virtual beacon transmitters as well as information to be communicated by virtual beacons. Devices with access to beacon information can determine based on the location of a wireless terminal whether the wireless terminal is within coverage area of a virtual beacon and report reception of the virtual beacon to the wireless terminal or a component of the wireless terminal which acts upon receiving an indication of beacon reception.

A server performs: acquiring measurement data of a mobile terminal; extracting fixed AP measurement data of a specific fixed AP; extracting measurement data including a measurement time of which a difference from the measurement time in the fixed AP measurement data is equal to or less than a threshold value, position information of which a difference from a position of the specific fixed AP is equal to or less than a threshold value, and a reception intensity of which a difference from the reception intensity in the fixed AP measurement data is equal to or less than a threshold value as target measurement data and to set the radio access point corresponding to the target measurement data as a target AP; and determining whether the target AP corresponds to a fixed AP on the basis of the measurement data corresponding to the target AP and the fixed AP measurement data.

The present disclosure relates to a method (100) for determining an angle of arrival, AoA, of received radio frequency, RF, measurement signals. The method (100) comprises obtaining (101) measurement data based on the received RF measurement signals from an antenna array, wherein the RF measurement signals are representative of multiple frequency channels. The method (100) further comprises determining (102) power spectra, comprising determining at least one power spectrum for each of the multiple frequency channels by using the measurement data. The method (100) further comprises providing (105) a machine learning algorithm, which is pre-trained to determine an AoA based on power spectra of multiple frequency channels. The method (100) further comprises determining (106) the AoA of the received RF measurement signals by using the machine learning algorithm and the determined power spectra.

This disclosure concerns estimating the location of a transmitter using multiple pairs of locator nodes with known locations and measuring time of arrival of a signal received from a transmitter. A processor of a location estimation node first determines time difference of arrival values from time of arrival values measured by each pair of locator nodes. The processor then determines likelihood information for multiple candidate locations of the transmitter and estimates the location of the transmitter from the likelihood information. A processor further determines an initial time of arrival value for the received signal and channel impulse response for a radio channel between the transmitter and receiver. The processor then determines a time correction from the channel impulse response based on a first peak of the channel impulse response and a leading edge of the first value. The processor finally determines an improved time of arrival value of the received signal.

A method includes obtaining channel information, range information, and angle of arrival (AoA) information based on wireless signals communicated between an electronic device and an external electronic device. The method also includes generating an initial prediction of a presence of the external electronic device relative to a field of view (FoV) of the electronic device based on the channel information and at least one of the range information or the AoA information. The initial prediction includes an indication of whether the external electronic device is within the FoV or outside the FoV of the electronic device. The method further includes performing, using a tracking filter, a smoothing operation on the range information and the AoA information. Additionally, the method includes determining that the external electronic device is within the FoV of the electronic device based on the AoA information, the smoothed AoA information, and the initial prediction.