Disclosed herein are an apparatus and method for precise positioning based on deep learning. The method performed by the apparatus includes setting a collection location and a collection environment, collecting wireless signal data based on the collection location and the collection environment, generating a magnitude map image for training from the wireless signal data, and generating a positioning DB model by learning the image characteristics of the magnitude map image for training through deep-learning-based training.

Wireless communication between two electronic devices may be used to determine a distance between the two devices, even in the presence of an otherwise-disruptive attacker. A wireless receiver system of one device may receive a true wireless ranging signal from a first transmitting device and a false wireless ranging signal from an attacker. The wireless receiver system may correlate the wireless signals with a known preamble sequence and perform channel estimation using the result, obtaining a channel impulse response for the wireless signals. The wireless receiver system may filter the channel impulse response for the plurality of wireless signals by removing at least part of the channel impulse response due to the false wireless ranging signal while not removing at least part of the channel impulse response due to the true wireless ranging signal. The receiver system may perform a wireless ranging operation using the filtered channel impulse response.

The present invention can improve the precision of location estimation of a signal transmitting source. This location estimating apparatus is provided with: a data acquisition unit for acquiring measurement values of sensors which measure radio waves of a signal transmitting source and location information on the sensors; a class classification unit for class-classifying the acquired measurement values by using relative locations and the dissimilarity of the measurement values of each set of the sensors; and a location estimation unit for estimating the location of the signal transmitting source on the basis of the classified measurement values and the location information of the sensors.

Examples of systems and methods described herein may be used to track a tagged object through a scene. Techniques are described herein to calculate a position of the tag using wireless communication with multiple anchor devices. In some examples, the anchor devices may be self-localizing, e.g., they may dynamically determine their position and relationship to one another. In some examples, position of a tag may be calculated by calculating multiple candidate positions using different localization techniques—such as geometric localization techniques and/or optimization-based techniques. An error may also be identified associated with each candidate position. A final position may be determined for the tag based on the errors associated with the candidate positions (e.g., the candidate position with the smallest error may be utilized as the position, e.g., the determined position, of the tag).

Methods and systems related to neural networks or other function approximators operate for training a neural network is provided, or another function approximator, for inferring a predetermined time of arrival of a predetermined transmitted signal on the basis of channel-impulse-responses, CIRs, of transmitted signals between a mobile antenna and a fixed antenna, the method having: obtaining a channel impulse response condition characteristic, CIRCC, descriptive of channel impulse responses of transmitted signals associated with mobile antenna positions within a reach of the fixed antenna; generating, by simulation, a training set of simulated CIRs which are associated with different times of arrival in one or more simulated scenes, and which fit to the CIRCC; training the neural network, or other function approximator, using the simulated CIRs and the different associated times of arrivals to obtain a parametrization of the neural network, or other function approximator, associated with the CIRCC.

An area determination system includes a decision unit, a calculation unit, and a determination unit. The decision unit decides, based on a strength of a radio signal transmitted from a transmitter and received by a receiver, a location of the transmitter. The calculation unit calculates a presence determination value. The presence determination value is based on a number of times that the location of the transmitter is determined to be in a presence determination region during a presence determination time period. The presence determination region corresponds to a target area. The determination unit, when a presence condition is satisfied, determine that the transmitter is in the target area. The presence condition is that a state where the presence determination value is greater than or equal to a presence threshold continues for a presence determination time.

Techniques are provide for neural network based positioning of a mobile device. An example method for determining a line of sight delay, an angle of arrival, or an angle of departure value, according to the disclosure includes receiving reference signal information, determining a channel frequency response or a channel impulse response based on the reference signal information, processing the channel frequency response or the channel impulse response with a neural network, and determining the line of sight delay, the angle of arrival, or the angle of departure value based on an output of the neural network.

Methods, apparatus and systems for correlation-based wireless monitoring are described. For example, a described method comprises: detecting and monitoring motion of a first object in a first sensing task based on a first motion information (MI) computed based on a first time series of channel information (TSCI) associated with a first device pair; detecting and monitoring motion of a second object in a second sensing task based on a second MI computed based on a second TSCI associated with a second device pair; computing a correlation score based at least partially on: the first TSCI, the second TSCI, the first MI and the second MI; detecting the first object and the second object as a same object when the correlation score is greater than a first threshold; and detecting the first object and the second object as two different objects when the correlation score is less than a second threshold.

A method for detecting a change in location of at least one wheel of a motor vehicle, the vehicle having at least one central processing unit, one wheel unit which includes an electronic assembly of sensors and which is mounted on the wheel, and one bidirectional communications assembly. The method notably includes a first comparison step, during which a first evaluation pattern is compared with a first reference pattern to determine whether the location of the wheel has changed, the patterns being representative of the effective location of the wheel unit in the motor vehicle.

Methods and apparatus for predicting wireless measurements based on virtual access points is described. In some embodiments, a location of a user equipment (UE) may be obtained, and given the location of the UE, an output may be generated using a machine learning model, the output including one or more predicted wireless measurements. The output may be indicative of a wireless channel in a multipath environment. In some variants, the machine learning model may have been trained by obtaining a training dataset comprising multipath components data and ground truth locations of a wireless device, and performing an optimization with respect to the multipath components data and the ground truth locations. In some implementations, a training output comprising a predicted multipath component may be produced during the training, and the optimization may include an iterative minimization of an error between at least the predicted multipath component and a labeled multipath component.