Abstract: Object movement detection may be provided. Channel State Information (CSI) data may be obtained. The CSI data may be associated with a plurality of links between a plurality of Access Points (APs) that provide coverage to an area. Next, a plurality of features may be extracted from the CSI data. The plurality of features may then be converted to a plurality of scores. Then motion in sub-areas of the area may be classified based on a comparison of the plurality of scores to a threshold for the area. The threshold may be determined based upon data collected when no motion occurred in the area.

Abstract: Correcting for antennae spatial distortions in Radio Frequency (RF) localization may be provided. A plurality of actual locations associated with a plurality of Access Point (APs) may be received. Then a plurality of signal strengths associated with the plurality of APs may be received. Based on the plurality of signal strengths, a model may be created that models a plurality of inference errors respectively corresponding to the plurality of APs between a plurality of inferred locations respectively corresponding to the plurality of APs and the plurality of actual locations. The model may then be used in determining a location of a device.

Abstract: Systems and methods provide for improving the accuracy of a location system. The location system can capture partial phase vector data from one or more access points (APs). The location system can capture associated data associated with the partial phase vector data across multiple dimensions, such as identity data of the APs and client devices generating the partial phase vector data and frequency band data, location data, a time and date, and other data associated with the partial phase vector data. The location system can determine correlation data across the multiple dimensions using the first partial phase vector data and the associated data. The location system can a cause of the partial phase vector data based on the correlation data. The location system can perform one or more remediation actions based on the cause of the partial phase vector data.

Abstract: An access point includes first and second radios that operate in accordance with first and second wireless protocols, respectively. The access point performs a method including receiving a first protocol packet of the first wireless protocol and a second protocol packet of the second wireless protocol transmitted by a wireless device over a communication channel, and measuring a first received power of the first protocol packet and a second received power of the second protocol packet at the access point. The method further includes determining whether reception times of the first protocol packet and the second protocol packet are within a coherence time for the communication channel and, when the reception times are within the coherence time, combining the first received power and the second received power into a multiprotocol received power. The method also includes determining a location of the wireless device based on the multiprotocol received power.

Abstract: In one embodiment, a service obtains spatial information regarding a physical area. The service estimates locations of a device within the physical area over time, based on wireless signals sent by the device. The service generates a set of images based on the spatial information regarding the physical area and on the estimated locations of the device within the physical area over time. The service updates an estimated location of the device by inputting the generated set of images to a machine learning model trained to minimize a location estimation error.

Abstract: In one embodiment, a service obtains spatial information regarding a physical area. The service estimates locations of a device within the physical area over time, based on wireless signals sent by the device. The service generates a set of images based on the spatial information regarding the physical area and on the estimated locations of the device within the physical area over time. The service updates an estimated location of the device by inputting the generated set of images to a machine learning model trained to minimize a location estimation error.

Abstract: A plurality of access points located within a building structure may provide various location-based services, such as navigation, to mobile devices within the building structure. To more accurately determine the location of the plurality of access points, a floorplan of the interior of the building structure may be obtained. A plurality of images of the interior of the building structure is then obtained. At least one of the images includes an image of at least one of the plurality of access points. A plurality of reference points may also be located within the building structure and obtained. Based on the plurality of images, reference points, and the floorplan, a composite image of the interior of the structure is generated. Based on the composite image, the location of each of the plurality of access points is determined.

Abstract: An enterprise system configures access point devices at an enterprise location to communicate with a location determination system. The location determination system receives wireless signal attributes of user computing devices broadcasting Wi-Fi signal data at the enterprise location from one or more access point devices. For a particular time window, the location determination system determines aggregated features of received wireless signal data across all access point devices, and classifies each of the user computing devices as moving or stationary by applying the wireless signal data to a model. For each of the user computing devices determined to be moving, the location determination system calculates a respective position of the user computing device based on the wireless signal data. For each of the user computing devices determined to be stationary, the location determination system does not calculate a respective position of the respective user computing device.

Abstract: Systems and methods provide for improving the accuracy of a location system. The location system can capture partial phase vector data from one or more access points (APs). The location system can capture associated data associated with the partial phase vector data across multiple dimensions, such as identity data of the APs and client devices generating the partial phase vector data and frequency band data, location data, a time and date, and other data associated with the partial phase vector data. The location system can determine correlation data across the multiple dimensions using the first partial phase vector data and the associated data. The location system can a cause of the partial phase vector data based on the correlation data. The location system can perform one or more remediation actions based on the cause of the partial phase vector data.

Abstract: A plurality of access points located within a building structure may provide various location-based services, such as navigation, to mobile devices within the building structure. To more accurately determine the location of the plurality of access points, a floorplan of the interior of the building structure may be obtained. A plurality of images of the interior of the building structure is then obtained. At least one of the images includes an image of at least one of the plurality of access points. A plurality of reference points may also be located within the building structure and obtained. Based on the plurality of images, reference points, and the floorplan, a composite image of the interior of the structure is generated. Based on the composite image, the location of each of the plurality of access points is determined.

Abstract: An enterprise system configures access point devices at an enterprise location to communicate with a location determination system. The location determination system receives wireless signal attributes of user computing devices broadcasting Wi-Fi signal data at the enterprise location from one or more access point devices. For a particular time window, the location determination system determines aggregated features of received wireless signal data across all access point devices, and classifies each of the user computing devices as moving or stationary by applying the wireless signal data to a model. For each of the user computing devices determined to be moving, the location determination system calculates a respective position of the user computing device based on the wireless signal data. For each of the user computing devices determined to be stationary, the location determination system does not calculate a respective position of the respective user computing device.

Abstract: It may be determined that each of a first plurality of devices was stationary at a location based on received first location information. Next, a first estimated ground truth may be determined for each of the first plurality of devices at the location based on the first location information corresponding to each of the respective first plurality of devices at the location. A first plurality of location estimation errors may then be determined for each of the first plurality of devices at the location respectively based on the first location information corresponding to each of the respective first plurality of devices at the location and the first estimated ground truth for each of the respective first plurality of devices at the location. Next, each of the determined first plurality of location estimation errors for each of the first plurality of devices at the location may be aggregated to provide a first location accuracy measurement for the location.

Abstract: An enterprise system configures access point devices at an enterprise location to communicate with a location determination system. The location determination system receives wireless signal attributes of user computing devices broadcasting Wi-Fi signal data at the enterprise location from one or more access point devices. For a particular time window, the location determination system determines aggregated features of received wireless signal data across all access point devices, and classifies each of the user computing devices as moving or stationary by applying the wireless signal data to a model. For each of the user computing devices determined to be moving, the location determination system calculates a respective position of the user computing device based on the wireless signal data. For each of the user computing devices determined to be stationary, the location determination system does not calculate a respective position of the respective user computing device.

Abstract: A method including obtaining location estimates of a wireless mobile device inside a structure based on wireless signals transmitted by the wireless mobile device and received at a plurality of wireless access points, wherein the inside of the structure is represented by a plurality of path segments; modeling a transition from a first estimated location of the wireless mobile device to a second estimated location of the wireless mobile device by minimizing a ratio of an angle to a length between the first estimated location and the second estimated location; selecting, based on the modeling a particular path segment of the plurality of path segments; and aligning the second estimated location of the mobile device to the particular path segment. An apparatus and a computer-readable storage media implementing the method are also disclosed.

Abstract: A method including obtaining location estimates of a wireless mobile device inside a structure based on wireless signals transmitted by the wireless mobile device and received at a plurality of wireless access points, wherein the inside of the structure is represented by a plurality of path segments; modeling a transition from a first estimated location of the wireless mobile device to a second estimated location of the wireless mobile device by minimizing a ratio of an angle to a length between the first estimated location and the second estimated location; selecting, based on the modeling a particular path segment of the plurality of path segments; and aligning the second estimated location of the mobile device to the particular path segment. An apparatus and a computer-readable storage media implementing the method are also disclosed.

Abstract: An efficient process is provided that exploits features in historical and current client location data to forecast client counts of different zones up to several hours ahead. These features may be obtained from correlations of client counts of multiple zones in a recent and long period of time. These features may also be combined using techniques that choose the best performing method for a particular dataset and a particular lookahead time. This process provides better forecast/prediction on the zone-based client count data, and is very useful in customer analytics which can now show the future predicted value. This can help the analytics customers to plan their operations based on the location analytics.