Polaris Wireless, Inc.

Ntt Docomo, Inc.

Virginia Tech Intellectual Properties Inc

Virginia Polytechnic Institute and State University

Scot Douglas Gordon

Jerome Arthur Blaha, Jr

Jian Zhu

Jeffrey Noel Wu

Tarun Kumar Bhattacharya

David Stevenson Spain, Jr

Jonathan Shiao-En Lu

Martin J Feuerstein

Robert Lewis Martin

Yasuhiro Oda

Jiyun Shen

Hassan Mohamed El-Sallabi

R Michael Buehrer

Sayed Reza Monir Vaghefi

Mahesh B Patel

Dmitry Kaplan

Bhavin Pankaj Shah

Raul Vera

Jonathan Shiao-en Lu

Ankit Bansal

Hassan Mohamed Ei-Sallabi

Anjui Bahuguna

Anjul Bahuguna

A method for providing an enhanced estimate of reference barometric pressure. The method uses multiple, dissimilar pressure references, such as in airport weather stations, personal weather stations, and wireless terminals such as smartphones, in order to provide the enhanced estimate of reference barometric pressure. The method generates an enhanced estimate of reference barometric pressure based on a first estimate of reference barometric pressure from a first pressure reference network made up of airport weather stations, for example. The method also uses a second estimate of reference barometric pressure from a second pressure reference network made of up personal weather stations, for example, and a third estimate of reference barometric pressure, also from the second network. The first, second, and third estimates of reference barometric pressure are combined such that both measurement accuracy and timeliness are improved in the resulting, enhanced estimate.

Estimation of reference barometric pressure by using dissimilar pressure references

A method for estimating the pressure measurement bias of a barometric sensor in a wireless terminal. A location engine using the method generates an enhanced estimate of the measurement bias. The location engine generates the enhanced estimate based in part on Global Navigation Satellite System (GNSS)-based estimates of the elevation of the wireless terminal, which the terminal generates as it concurrently makes barometric pressure measurements. Each GNSS-based estimate of elevation is often generated from noisy measurements and has an associated uncertainty. The location engine accounts for the uncertainty in the GNSS estimates of elevation by applying an optimal estimation technique, such as Kalman filtering, on the biased pressure measurements and the GNSS-based estimates. Once the location engine generates the enhanced estimate of measurement bias, it can adjust subsequent measurements of barometric pressure made by the wireless terminal and generate improved estimates of elevation of the wireless terminal.

Estimation of barometric pressure measurement bias based on global navigation satellite system signals

A method for performing contact tracing. An analysis system performing the method receives geo-temporal data comprising location data points for various wireless terminals, including the wireless terminals being used by people diagnosed as having a specified disease and the wireless terminals of others, one of whom having possibly infected those diagnosed with the disease. Based on filtering the geo-temporal data, the analysis system generates relatively-condensed mobility profiles that are representative of each person's locations and movements, and analyzes the mobility profiles. Through careful selections of various parameters based on the disease that is being analyzed, the mobility profiles are used instead of the relatively large amounts of geo-temporal data, to represent users of wireless terminals and to determine their interactions in regard to disease transmission.

Contact tracing involving an index case, based on comparing geo-temporal patterns that include mobility profiles

A method for performing contact tracing. An analysis system performing the method receives geo-temporal data comprising location data points for various wireless terminals, including the wireless terminal being used by a person diagnosed as having a specified disease and the wireless terminals of people who possibly have come in contact with the infected person. Based on filtering the geo-temporal data, the analysis system generates relatively-condensed mobility profiles that are representative of each person's locations and movements, and analyzes the mobility profiles. Through careful selections of various parameters based on the disease that is being analyzed, the mobility profiles are used instead of the relatively large amounts of geo-temporal data, to represent users of wireless terminals and to determine their interactions in regard to disease transmission.

Contact tracing based on comparing geo-temporal patterns of wireless terminals, including mobility profiles

A method for estimating the pressure measurement bias of a barometric sensor in a wireless terminal. A location engine using the method generates an enhanced estimate of the measurement bias. The location engine generates the enhanced estimate based in part on relatively coarse estimates of the elevation of the wireless terminal. Each coarse estimate of elevation is often generated from noisy measurements, such as measurements of signals transmitted by Global Positioning System (GPS) satellites, and has an associated uncertainty. The location engine accounts for the uncertainty in these estimates of elevation by applying an optimal estimation technique, such as Kalman filtering, and by compensating for environment-related effects. Compensating Includes filtering across a plurality of lateral locations and imposing a lower bound of bias uncertainty at the lateral locations. Once the location engine generates the enhanced estimate of measurement bias, it can generate improved estimates of elevation of the wireless terminal.

Estimation of barometric pressure measurement bias by compensating for environment-related effects

A method for estimating the pressure measurement bias of a barometric sensor in a wireless terminal. A location engine using the method generates an enhanced estimate of the measurement bias. The location engine generates the enhanced estimate based in part on relatively coarse estimates of the elevation of the wireless terminal. The coarse estimates are used to generate instantaneous estimates of measurement bias and bias uncertainty. As needed, the location engine adjusts the instantaneous estimate of bias uncertainty, in order to reflect an instantaneous estimate of measurement bias that is recognized as being in error. The adjustment is based on what is expected as a probable measurement bias value for the particular wireless terminal. Once the location engine generates the enhanced estimate of measurement bias, it can generate improved estimates of elevation of the wireless terminal.

Estimation of barometric pressure measurement bias with adjustment based on a value expected for a wireless terminal

A location engine that estimates the barometric pressure measurement bias of a wireless terminal, resulting in an improved estimate of elevation of the wireless terminal. The location engine generates an estimate of measurement bias by comparing the barometric pressure measured by the wireless terminal while at that elevation and the barometric pressure that corresponds to an estimated elevation of the wireless terminal when it made the pressure measurement (i.e., the expected pressure). The estimated elevation is based on an inferred above-ground height and the local terrain elevation, and the expected pressure is based on the measurement of barometric pressure at the pressure reference and the estimated elevation. The location engine infers the height based on various techniques disclosed herein. The location engine can use the measurement bias to adjust subsequent pressure measurements reported by the wireless terminal, in order to generate an improved estimate of elevation of the wireless terminal.

Calibration of measurement bias of a barometric sensor in a wireless terminal

A technique for developing a stack-effect compensation model that is representative of a structure, such as a particular building, and of generating an estimate of the elevation of a wireless terminal inside the structure by using the model. The technique includes estimating information related to one or more neutral pressure planes within the structure, at which the difference between the indoor barometric pressure and the outdoor barometric pressure is essentially zero. The technique leverages pressure and temperature measurements that are provided by one or more wireless terminals that are regularly present in the building. The stack-effect compensation model includes a parameter that represents, for each of a plurality of uncompensated elevations, the height of the particular uncompensated elevation above a neutral pressure plane. The location engine uses this height-information parameter from the compensation model, in order to develop the estimate of the elevation of the wireless terminal.

Estimation of a stack-effect compensation model based on measurements provided by a wireless terminal

A technique for the estimation of the location of a wireless terminal. The disclosed technique considers boundaries that are represented in a geographic information system (GIS) database in combination with a first, unenhanced estimate of location, in order to generate a second, enhanced estimate of location. To do so, the technique generates a point cloud based on the first estimate of location. At least some of the data points in the point cloud are then removed, depending on their positions in relation to certain boundaries stored in the GIS database, such as the exterior walls of buildings and other structures. By considering these boundaries, the disclosed technique can increase the probability that the estimated location is within an area that is occupiable by a person, thereby providing a more reasonable result. The technique generates the second, enhanced estimate based on the distribution of the remaining data points.

Method of considering the positions of data points in relation to boundaries represented in a geographic information system database, in estimating location

A location engine that estimates the barometric pressure measurement bias of a pressure station reference, such as an airport pressure station, resulting in an improved estimate of the elevation of a wireless terminal. The location engine generates the estimate of bias of barometric pressure by comparing i) the outdoor barometric pressure measured by the airport pressure station at its unknown height above mean sea level (MSL) and ii) the expected outdoor barometric pressure derived from the pressure measurements from an already-calibrated pressure station, while accounting for the known height of the already-calibrated pressure station. The expected outdoor measurements correspond to a derived height above MSL of the airport pressure station. Subsequently, the location engine generates an estimate of the elevation of the wireless terminal by accounting for the estimate of measurement bias of the airport pressure station.

Estimating elevation of a wireless terminal, based on the measurement bias of a pressure reference that is in a second region, based on the measurement bias of a pressure reference that is in a first region

Method of considering boundaries represented in a geographic information system database, in estimating location

A method for estimating the location of a wireless terminal is disclosed that is based on using: the known location of one or more devices, empirical data that indicates the relative position of the wireless terminal to the devices, and empirical data that indicates the relative position of the wireless terminal (which is at an unknown location) relative to other wireless terminals that are also at unknown locations.

Estimating the location of a wireless terminal based on cooperative measurements

An improved technique for the estimation of the location of a wireless terminal. A disclosed location engine is capable of utilizing a service set identifier (SSID) or a media access control (MAC) address, or both, received from a reporting wireless terminal, in order to improve the estimate of the wireless terminal's location. The location engine generates a first hypothesis for the location of the wireless terminal based on evidence of the location of the wireless terminal, such signal strength measurements, wherein the evidence is independent of the SSID and MAC addresses. The location engine also generates a second hypothesis for the location based on the SSID or a third hypothesis for the location based on the MAC address, or both. An estimate of location of the wireless terminal is based on a combination of the first, second, and third hypotheses. The estimate can then be used in a location-based application.

Growing community of inventors

Redmond, WA, United States of America

Scot Douglas Gordon