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.

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.

Offloading of location computation from a location server to an access point through the use of projections on base phase vectors may be provided. First, an Access Point (AP) may receive a set of two or more base phase vectors from a location server. Next, the AP may measure a measured phase vector for a first signal from a user device. Then, the AP can determine projection values based on a comparison of the measured phase vector to each base phase vector. From these comparisons, the AP can determine a subset of base phase vectors with the highest projection values. The AP can then send the projection values and the subset of base phase vectors to the location server, wherein the location server determines the device location from these projection values and subset of base phase vectors.

An imaging system includes: a transmitter that is attached to an object and wirelessly transmits a positioning signal; a positioning device that determines the position of the object based on the positioning signal; an imaging device that images the object in a variable direction and at a variable magnification; and a control device. The control device calculates a direction and a distance from the imaging device to the object based on the position of the object, and sets a first direction and a first magnification on the imaging device based on the calculated direction and distance. Then, the control device sets a second direction and a second magnification on the imaging device based on imaging data generated by imaging the object, the imaging data being generated by the imaging device on which the first direction and the first magnification are set.

In one embodiment, a device obtains a machine learning model indicative of how to focus on particular location information from a plurality of radio frequency (RF) elements to provide an accurate location estimate of a wireless client based at least in part on angle-of-arrival information of the wireless client. When the device then obtains location information regarding the wireless client from the plurality of RF elements, it may apply the machine learning model to the location information regarding the wireless client to focus on particular location information of the location information from the plurality of RF elements. The device may then estimate a physical location of the wireless client based on focusing on the particular location information during a locationing computation.

An inventory transport monitoring system for gathering usage information in a retail store includes a network of sensors and data collection hubs located within the retail store. A plurality of inventory stocking carts are outfitted with nodes for tracking the cart location within the store. The tracking node may track location data and patterns and uses a unique ID that broadcasts monitored data to understand when and where the node has been moved. The usage information is transmitted to the hub for collection and analyzing. The system can use the usage information collected from the individual stores to measure and compare successful stores to failing stores.

Offloading of location computation from a location server to an access point through the use of projections on base phase vectors may be provided. First, an Access Point (AP) may receive a set of two or more base phase vectors from a location server. Next, the AP may measure a measured phase vector for a first signal from a user device. Then, the AP can determine projection values based on a comparison of the measured phase vector to each base phase vector. From these comparisons, the AP can determine a subset of base phase vectors with the highest projection values. The AP can then send the projection values and the subset of base phase vectors to the location server, wherein the location server determines the device location from these projection values and subset of base phase vectors.

Systems, methods, and apparatus receive a signal from a first wireless device through a first antenna, of a plurality of antennas, the signal including a first segment and a second segment. Responsive to detecting a change in the signal from the first segment to the second segment, embodiments traverse the plurality of antennas to receive the second segment through each of the plurality of antennas. Embodiments determine a plurality of phase samples, each associated with the second segment received through one of the plurality of antennas. Embodiment then use the plurality of phase samples to calculate direction data associated with the first wireless device.

The disclosure provides examples of systems and methods for determining locations of a number of radio frequency-enabled devices such as mobile devices and radio frequency-equipped beacons/luminaires within an indoor location. The radio frequency-enabled devices may be part of an indoor positioning system and/or content delivery system. The examples describe obtaining an angle of arrival (AoA) of the signals received by the respective radio frequency-enabled devices. The AOA data is used to identify the relative positions of the radio frequency-enabled devices as the mobile device moves about the indoor location. Upon comparing AOA measurements of the collected data related to a map of the location, the system may generate a data structure that may be presented graphically as a map of positions of the devices at the location. The described examples may enable a rapid commissioning process with respect to the radio frequency-enabled devices in a network.

The disclosure provides examples of systems and methods for determining locations of a number of radio frequency-enabled devices such as mobile devices and radio frequency-equipped beacons/luminaires within an indoor location. The radio frequency-enabled devices may be part of an indoor positioning system and/or content delivery system. The examples describe obtaining an angle of arrival (AoA) of the signals received by the respective radio frequency-enabled devices. The AOA data is used to identify the relative positions of the radio frequency-enabled devices as the mobile device moves about the indoor location. Upon comparing AOA measurements of the collected data related to a map of the location, the system may generate a data structure that may be presented graphically as a map of positions of the devices at the location. The described examples may enable a rapid commissioning process with respect to the radio frequency-enabled devices in a network.