An example method of determining a position of a mobile device includes, for each of a plurality of anchor points, receiving respective relative position information between the respective anchor point and the mobile device such that the received relative position information for the plurality of anchor points forms a set of position information, each relative position information including at least one of: a direction between the respective anchor point and the mobile device, a distance between the respective anchor point and the mobile device, or a direction and a distance between the respective anchor point and the mobile device. The method includes determining an estimated position of the mobile device based on the set of position information; defining a set of loci, each locus of the set of loci being based on one or more of the relative position information from the set of position information; determining, for each locus of the set of loci, a distance value between the estimated position of the mobile device and the respective locus; determining, for each locus of the set of loci, a weight factor as a function of the determined distance values; and determining a refined position of the mobile device based on the set of position information and the determined weight factors. A positioning engine for determining a position of a mobile device is disclosed as well.

Certain aspects of the present disclosure provide techniques for domain adaptation. An input tensor comprising channel state information (CSI) for a wireless signal is determined, where each channel in the input tensor corresponds to a respective degree of freedom (DoF) in the wireless signal. A domain-adapted tensor is generated by processing the input tensor using a domain-adaptation network comprising, for each respective DoF in the wireless signal, a respective convolution path. The domain-adapted tensor is provided to a neural network trained for position estimation.

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 vehicle system may include a plurality of wireless transmitters carried by a vehicle and configured to transmit wireless signals, at least one indicator carried by the vehicle, and a portable device moveable relative to the vehicle and configured to receive the wireless signals from the plurality of wireless transmitters. The system may further include a controller carried by the vehicle and configured to wirelessly communicate with the portable device, and determine a predicted zone the portable device is located in adjacent to the vehicle based upon the received wireless signals, with the predicted zone being one of a plurality of different zones adjacent the vehicle having respective vehicle functions associated therewith. The controller may be further configured to cause the at least one indicator to provide an indication that the portable device has entered the predicted zone, and enable the respective vehicle function associated with the predicted zone.

First information is obtained from a sensing device at a first time. The first information corresponds to a radio signal received at the device from a candidate location. The device is at a first location at the first time. Second information is obtained from the device at a second time. The second information corresponds to a radio signal received at the device from the candidate location. The device is at a second location at the second time. A system determines that a pattern is in each of the first and second information and determines relationships between the candidate location and the device at each first and second location. The system obtains inverses of the relationships and determines estimates of the received radio signals based on the information and inverses. The system measures or estimates energy emitted from the candidate location based on the estimates.

A method of operating a vehicle includes navigating the vehicle along a path, collecting a set of navigation parameters of the vehicle from at least one of a sensor, a global positioning system, or an inertial reference system, determining a set of statistical uncertainties related to at least some navigation parameters in the set of navigation parameters, and associating a set of statistical weights to the at least some navigation parameters.

In one embodiment, a method includes receiving, from a client system associated with a user of an online social network, data indicating that the user is located at a first geographic location at a first time; accessing a first embedding representing a first place-entity corresponding to the first geographic location; accessing multiple second embeddings representing multiple respective second place-entities each corresponding to a second geographic location; calculating, a similarity metric between the embedding representing the first place-entity and each of the embeddings representing the second place-entities; ranking each of the second place-entities based on their calculated similarity metrics; and sending, to the client system, information associated with one or more second geographic locations corresponding to one or more second place-entities having a ranking greater than a threshold ranking.

A method (100) for creating a model for positioning, the method (100) comprising: receiving (S150) a training dataset (1) comprising samples (10) of an estimation labeled type, each sample (10) of the estimation labeled type being associated with a measurement position, each sample (10) of the estimation labeled type comprising sensor data (12), measured at the measurement position associated with the sample (10), the sensor data (12) being characteristic of the measurement position associated with the sample (10); an estimated measurement position (14), being an estimate of the measurement position associated with the sample (10); an estimated accuracy (16), being an estimate of an accuracy of the estimated measurement position (14); training (S160) a machine learning model (20) to convert sensor data (12) to a position, wherein the training is based on the samples (10) of the estimation labeled type in the training dataset (1), whereby the model for positioning is created.

A communication apparatus mounted on a vehicle includes: a camera that captures a still image used for generating a map; a positioning circuit that positions a captured position of the still image; a control circuit that associates position information indicating the captured position with image data of the still image; and a communication circuit that establishes a radio communication with a roadside unit and transmits the image data by radio to the roadside unit, in which the control circuit rearranges an transmission order of the image data to be transmitted by radio to the roadside unit, based on the position information.

A system and method for determining location information of a portable device relative to an object is provided. In one embodiment, aspects of the system to determine location with respect to the portable device may be distributed among more than one device in the system.