Techniques are described herein for developing a fingerprint map that may be used for 3D indoor localization. In one example, a network server may leverage a building footprint from an open source database with signal measurements taken by probing user devices from signal sources such as access point (AP) devices. The network server may use the signal measurements to remotely calculate corresponding 3D positions of the AP devices in a particular building. Further, the network server may use the building footprint and the calculated 3D positions of the AP devices as references for developing the fingerprint map for 3D indoor localization.

A method for geolocating a signal-transmitting device, the geolocation method including: supplying first data of reception by a plurality of first reception stations of a first radio signal transmitted at a first frequency, calculating a first geographic position of the signal-transmitting device, detecting that the geographic position of the signal-transmitting device is included in a predefined second geographic zone, transmitting a signal instructing transmission of a second radio signal, supplying second data of reception of the second radio signal by a plurality of second reception stations according to a second frequency, and calculating a second geographic position of the signal-transmitting device.

Determining a device's location in a space in real time is computing intensive. To offload some of the workload in conducting this hyperlocation, the access points in the network conduct some of process in determining the location of a device. The cloud determines a restricted AoA search area based on previous client locations. After this determination, a three-dimensional (3D) AoA search is conducted by each AP in the restricted area (restricted by a range of azimuth directions) for a device. Finally, each AP reports a location(s) for the device, which comprises weights for selected angular sectors. The cloud can then construct a probability heat map for location computation from the weights provided from each AP for the device.

Systems and methods for indoor tracking via Wi-Fi fingerprinting and electromagnetic fingerprinting are provided and can include a gateway receiver device measuring a RSSI value of a signal transmitted by a Wi-Fi transmitter device, the gateway receiver device measuring an EMF value of an interference in an electromagnetic field created by the gateway receiver device that is caused by the Wi-Fi transmitter device, the gateway receiver device determining whether the RSSI value matches any of a plurality of Wi-Fi fingerprints associated with a monitored region and whether the EMF value matches any of a plurality of electromagnetic fingerprints associated with the monitored region, and responsive thereto, the gateway receiver device identifying that a location of the Wi-Fi transmitter device is within the monitored region.

Methods, apparatus and systems for wirelessly monitoring a micro motion of an object are described. In one example, a described system comprises: a transmitter configured for transmitting a first wireless signal through a wireless multipath channel of a venue; a receiver configured for receiving a second wireless signal through the wireless multipath channel; and a processor. The second wireless signal differs from the first wireless signal due to the wireless multipath channel and a motion of an object in the venue. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each channel information (CI) of the TSCI comprises N1 CI components; selecting N2 CI components from the N1 CI components based on a first analysis of the N1 CI components; computing a micro-motion (MM) statistics based on the N2 selected CI components and the first analysis; monitoring the motion of the object based on a second analysis of the MM statistics; performing a task based on the first analysis and the second analysis; and generating a response based on the task, the first analysis and the second analysis.

A data processing method includes obtaining positioning data collected during positioning, obtaining a fingerprint set from a location database based on the positioning data, where the fingerprint set includes at least one fingerprint, and each fingerprint in the fingerprint set includes at least one positioning media access control (MAC) address, determining, for each fingerprint in the fingerprint set, a matching degree between the fingerprint and the positioning data based on a second signal strength and a first signal strength of each positioning MAC address in the fingerprint, and determining that an access point (AP) included in the positioning data moves when each of matching degrees of all fingerprints in the fingerprint set is less than a first threshold.

An access control system is described for controlling access to a wireless communication system. The access control system employs a beam-steering based spatial location system in order to spatially locate potential users by means of locating their user equipment and can allow or prevent that user equipment from associating with one or more wireless access points forming a part or the whole of the wireless communication system. The beam-steering based spatial location system is also described and comprises at least one of a transmitter and a receiver together with an antenna array wherein the antenna array is capable of varying the pointing angle of at least two antenna lobes independently without the need to move either the antenna array or its constituent parts physically and wherein the at least two antenna lobes are arranged such that they may partially intersect at one or more pointing angles under electronic control. The antenna array may, for example, comprise at least a first sub-array and at least a second sub-array wherein the second sub-array is oriented substantially orthogonally to the first sub-array.

The pointing angle of an antenna lobe or null or other characteristic feature of the antenna radiation pattern of a first sub-array and the pointing angle of an antenna lobe or null or other characteristic feature of the antenna radiation pattern of at least a second sub-array may be independently controllable in order to allow each to separately locate a signal which falls within their respective steering ranges. A point within the area of intersection of the two or more beams, when they are arranged to point at a signal to be located, may be reported to a spatial processing module or any other system, as a spatial location of the signal source or its transmitting antenna.

Systems and methods for determining a location of one or more user equipment (UE) in a wireless system can comprise receiving reference signals via a location management unit having two or more co-located channels, wherein the two or more co-located channels are tightly synchronized with each other and utilizing the received reference signals to calculate a location of at least one UE among the one or more UE. Embodiments include multichannel synchronization with a standard deviation of less than or equal 10 ns. Embodiments can include two LMUs, with each LMU having internal synchronization, or one LMU with tightly synchronized signals.

An electronic device may comprise a communication interface and a processor configured to control the communication interface to receive cellular data from at least one cellular base station, to predict a position of the electronic device based on the received cellular data, to receive information about at least one wireless LAN base station, and to determine the position of the electronic device based information about the at least one wireless LAN base station and the predicted position.

A mobile device that is able to locate itself in a topological map using a combination of visual and radio information is provided. The mobile device captures an image of its environment, detects receivable radio transmitters and builds an observation vector that defines visual words and radio words that are present at the current location of the mobile device. It then compares this observation vector to reference vectors corresponding to reference location and identifies, based on correlations between visual words and radio words, the location of the mobile device among reference locations.