A network device receives a plurality of data samples. Each data sample comprises measurements and a vertical component of a location. The vertical component is provided as an altitude and/or a floor name for a building floor at which the respective device was located when the measurements were obtained. The network device identifies a first set and/or a second set of data samples of the plurality of data samples. Each data sample in the first set comprises the floor name. Each data sample in the second set comprises both the altitude and the floor name. Based on the first and/or second set of data samples, the network device determines a discrete vertical axis for the horizontal reference position with levels of the discrete vertical axis labeled by corresponding floor names of the building containing the horizontal reference position and/or a floor name-altitude relation for the horizontal reference position.

A positioning system has an initiator device configured for emitting a high-speed wireless signal, at least one reference device configured for receiving the high-speed wireless signal and emitting a low-speed wireless signal after receiving the high-speed wireless signal, at least one target device each having one or more components for receiving the low-speed wireless signals, and at least one engine configured for determining the position of each of the at-least-one target device by calculating the distance between the target device and each of the at-least-one reference device based on at least the times-of-arrival of the low-speed wireless signals, each time-of-arrival being the time that the corresponding low-speed wireless signal being received by the target device, and determining the position of the target device based on the calculated distances.

In one embodiment, a method includes receiving from multiple magnetic navigation devices magnetic measurements of one or more geographical regions collected over a period of time; performing a similarity correlation analysis on the magnetic measurements; generating one or more geomagnetic map patches corresponding to the one or more geographical regions based on the similarity correlation analysis; transmitting the one or more geomagnetic map patches to a main mapping server for updating geomagnetic map data; receiving updated geomagnetic map data from the main mapping server; and transmitting the updated geomagnetic map data to one or more of the magnetic navigation devices for navigation and localization.

When deploying or upgrading an enhanced cellular communication system, a site survey may be performed by configuring cellular devices in a given area to report various operational metrics. The devices may also be configured to report environmental data that can be used to determine whether the devices are indoors or outdoors, which may be useful when interpreting the operational metrics. The environmental signatures may include an audio signature, which may comprise an audio impulse response produced by an acoustic echo canceller (AEC) of the device. The environmental signatures may further include a light signature, which may be based on frequency components of ambient light. The environmental signatures may further include an audio signature, which may be based on characteristics of radio signals received by the device. Machine learning techniques may be used, with the environmental signatures as features, to predict whether a given device is indoors or outdoors.

The data collecting method includes: collecting first and second sensor data respectively through a first and a second sensors while a data collecting apparatus moves within a target area, and tagging a first and a second timestamp values respectively to the first and the second sensor data; generating map data of the target area and location data at a point of time corresponding to the first timestamp value, based on the first sensor data; generating map information of the target area based on the map data, and generating moving path information on the map based on the location data; and estimating a sensing location at a point of time corresponding to the second timestamp value based on the moving path information, and tagging the sensing location to the second sensor data.

An example method of determining geolocations of objects based on information retrieved from heterogeneous data sources comprises: receiving, from a first data source associated with an object by an ontology-defined relationship, a first dataset including a first data item specifying a first time identifier and a first geolocation associated with the object; receiving, from a second data source associated with an object by an ontology-defined relationship, a second dataset including a second data item specifying a second time identifier and a second geolocation associated with the object; and determining, by applying a rule set associated with the ontology to the first dataset and the second dataset, a geolocation of the object and a corresponding time identifier.

A system for generating and managing maps of a plurality of real-world environments, the system comprising a map generation unit operable to generate a map of each of the plurality of real-world environments, a wireless signal identification unit operable to identify one or more wireless signals at one or more locations in each of the plurality of real-world environments, and to associate the identified signal information with a map corresponding to the same real-world environment, and a compiling unit operable to compile the respective generated maps into a composite map, wherein the respective generated maps are arranged in dependence upon the wireless signals identified in each of the corresponding real-world environments.

Provided is a process including: acquiring signal strength information that includes a plurality of signal strength measurements detected by a mobile computing device and that are each associated with a respective wireless signal measurement time; generating a wireless signal-based localization by localizing the mobile computing device based on the plurality of signal strength measurements and known locations of beacon devices; acquiring a plurality of mobility sensor measurements that are each associated with a respective mobility sensor measurement time; generating a mobility-based localization by localizing the mobile computing device based on the plurality of mobility sensor measurements; fusing the wireless signal-based localization and the mobility-based localization based on correspondence between the respective wireless signal measurement time for the signal strength measurements and the respective mobility sensor measurement time for the mobility sensor measurement to generate a fused mobile computing device localization.

A robot and a method for localizing a robot are disclosed. The method for localizing a robot may include acquiring communication environment information including identifiers of access points and received signal strengths from the access points, generating an environmental profile for a current position of the robot based on the acquired communication environment information, comparing the generated environmental profile with a plurality of learning profiles associated with a plurality of regions, respectively, determining a learning profile corresponding to the environmental profile, based on the comparison, and determining a region associated with the determined learning profile as a current position of the robot. In a 5G environment connected for the Internet of Things, embodiments of the present disclosure may be implemented by executing an artificial intelligence algorithm and/or machine learning algorithm.

A location engine uses the empirical measurements made by a scouting wireless terminal (i) to discover the existence of a reference radio within a geographic region; (ii) to generate an estimate of the location of the newly-discovered reference radio, and (iii) to generate an estimate of the transmission power of the downlink control channel radio signal transmitted by the newly-discovered reference radio. The location engine then uses: (i) the estimate of the location of the newly-discovered reference radio, and (ii) the estimate of the transmission power of the downlink control channel radio signal transmitted by the newly-discovered reference radio, and (iii) measurements, made by a user wireless terminal, of the power of each of the downlink control channel radio signals transmitted by each of the reference radios to generate an estimate of the location of the user wireless terminal.