To estimate a positional relationship between devices having transmitted and received signals with higher accuracy.

A control device comprising:

a control unit that performs control for estimating a positional relationship between a communication device having four or more antennas and another communication device on the basis of signals transmitted and received between the communication device and the other communication device, wherein

the control unit applies a weight parameter based on a reliability parameter that is an index indicating a degree of whether or not a signal is appropriate as a processing target for estimating a positional relationship between the communication device and the other communication device calculated on the basis of a signal received from the other communication device by the communication device to a phase difference between adjacent antennas of the four or more antennas of the communication device, and performs control for estimating the positional relationship.

In an aspect, an initiator transmits a positioning reference signal. The initiator receives a plurality of responder positioning reference signals sent from individual responders of a plurality of responders. The initiator transmits a measurement message comprising a first set of measurements that are determined based on the positioning reference signal and the plurality of responder positioning reference signals. The initiator receives responder measurement messages sent from the individual responders of the plurality of responders. The initiator receives updated map information from an anchor and updates pre-existing map information based on the updated map information.

A ranging-type positioning system and a ranging-type positioning method based on crowdsourced calibration are provided. In a crowdsourcing stage, pedestrian dead reckoning (PDR) is performed based on readings of inertial measurement units on a mobile device, a particle filter (PF) is executed to reconstruct a path of the mobile device with map information of the target field, and FTM data records are collected. Then, a ranging model based on a neural network can be used to calibrate and inversely infer approximate locations of unknown base stations. The optimized ranging model can estimate estimated distances and standard deviations based on the FTM data records obtained in the crowdsourcing stage. In a positioning stage, a position of a to-be-positioned mobile device can be positioned by having the ranging model operated in cooperation with the PDR and the PF.

Ultra Wide Band (UWB) based Real Time Location Systems (RTLS) that are being used for location tracking suffer from due to environment specific errors that are introduced due to factors such as difference in reflection and propagation. The disclosure herein generally relates to object localization, and, more particularly, to a method and system of error modelling for object localization using Ultra Wide Band (UWB) sensors. The error modelling allows the system to correct a determined location of an object being tracked, to determine a corrected location.

A hybrid method of estimating position of a mobile device which utilizes both received signal power and timing measurements. Received signal power of signals received by the mobile device from a plurality of cells are measured and corresponding received signal power measurements are stored. The method further includes measuring, at the mobile device, times of arrival of signals received from the plurality of cells. A plurality of time difference of arrival (TDOA) measurements are determined from the times of arrival. A power-time hybrid Gaussian maximum likelihood estimator and positioning assistance data for the plurality of cells are used to generate a maximum likelihood estimate of the position of the mobile device by evaluating a joint conditional probability of the received signal power measurements and the plurality of TDOA measurements. Gaussian random variables may be used to represent the received signal power measurements and the TDOA measurements.

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.

Disclosed are a cooperative positioning method and apparatus, a device, and a non-transitory computer-readable storage medium. The method may include: determining an initial positioning estimated value of each of a plurality of objects to be measured by a simulated annealing algorithm and a first preset positioning algorithm; screening at least two distance measurement values based on a preset error threshold to obtain a target distance measurement value, where the at least two distance measurement values are measurement values obtained by measuring a distance between each object to be measured and each of a plurality of target base stations for at least two times; and determining a position of each object to be measured according to a multi-target-source Taylor series algorithm, each target distance measurement value and each initial positioning estimated value.

A method in a measuring station is described. The method includes determining a plurality of Time of Flights (TOFs) corresponding to plurality of beacons and determining an overall circular error probability ellipse (CEP) based at least in part upon a plurality of times of departure and a corresponding plurality of measuring station positions for each TOF. The method further includes determining at least one individual CEP of a plurality of individual CEPs if at least one of a predetermined time has elapsed and the measuring station has travelled a predetermined distance and determining a merged CEP, where the merged CEP includes the plurality of individual CEPs. Further, the merged CEP is determined to be a better CEP if the merged CEP is more consistent with the plurality of individual CEPs than with the overall CEP. The better CEP is usable to determine a location of a wireless device.

A vessel analysis device capable of appropriately determining a suspicious vessel is provided. The vessel analysis device (1) includes a pattern generation unit (2), an estimation unit (4), and a determination unit (6). The pattern generation unit (2) generates an intended track pattern representing a track of an intended vessel that is a vessel to be analyzed, from position information on the intended vessel, the position information changing as time proceeds. The estimation unit (4) estimates the navigation state of the intended vessel using the generated track pattern. The determination unit (6) determines whether an intended navigation state that is a navigation state indicated in vessel information originated by the intended vessel is falsified or not by comparing the estimated navigation state with the intended navigation state.

The detection and identification of unmanned aerial vehicles (UAVs) from a radio wave measurement result based on artificial intelligence (AI) are provided. A method of operating an apparatus to detect unmanned aerial vehicles (UAVs) includes generating a spectrogram, determining a first region to find a direction of the UAVs in the spectrogram, determining a direction of a first UAV of the UAVs based on signal values in the first region, determining a second region to identify a type of the first UAV in the spectrogram, and identifying the type of the first UAV based on signal values in the second region.