CORRECTING DEVICE ERROR RADIUS ESTIMATES IN POSITIONING SYSTEMS
First Claim
1. A system for adjusting computed device error radiuses associated with inferred device positions, said system comprising:
- a memory area storing a plurality of positioned observations each including at least a set of beacons observed by one of a plurality of computing devices, the plurality of positioned observations including training positioned observations and test positioned observations, the memory area further storing a plurality of beacon positions inferred from the training positioned observations and storing a plurality of minimum beacon radiuses achieving a pre-defined confidence level, the plurality of minimum beacon radiuses being estimated by comparing the inferred beacon positions with the test positioned observations; and
a processor programmed to;
receive data representing a set of beacons from a mobile computing device, said beacons being observed by the mobile computing device;
obtain, from the plurality of inferred beacon positions stored in the memory area, a beacon position for each of the beacons in the set of beacons in the received data;
for each of the obtained, inferred beacon positions, obtain a minimum beacon radius from the plurality of minimum beacon radiuses stored in the memory area to achieve the pre-defined confidence level;
apply a Kalman filter to one or more of the beacons observed by the mobile computing device using the inferred beacon positions and the obtained minimum beacon radiuses for said one or more of the beacons, wherein applying the Kalman filter infers a device position for the mobile computing device and computes a device error radius for the inferred device position; and
adjust the computed device error radius as a function of a quantity of said one or more of the beacons input to the Kalman filter to achieve the pre-defined confidence level.
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Accused Products
Abstract
Embodiments adjust device error radiuses associated with inferred device positions produced by positioning systems. Inferred beacon positions and associated beacon radiuses are accessed for beacons in a beacon fingerprint from an observing computing device. The beacon radiuses are associated with a pre-defined confidence level (e.g., an in-circle percentage). A Kalman filter is applied to at least one of the beacons using the inferred beacon positions and the beacon radiuses associated therewith to infer a device position for the computing device and to compute a device error radius for the inferred device position. The computed device error radius is adjusted as a function of the quantity of beacons input to the Kalman filter to achieve the pre-defined confidence level.
19 Citations
20 Claims
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1. A system for adjusting computed device error radiuses associated with inferred device positions, said system comprising:
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a memory area storing a plurality of positioned observations each including at least a set of beacons observed by one of a plurality of computing devices, the plurality of positioned observations including training positioned observations and test positioned observations, the memory area further storing a plurality of beacon positions inferred from the training positioned observations and storing a plurality of minimum beacon radiuses achieving a pre-defined confidence level, the plurality of minimum beacon radiuses being estimated by comparing the inferred beacon positions with the test positioned observations; and a processor programmed to; receive data representing a set of beacons from a mobile computing device, said beacons being observed by the mobile computing device; obtain, from the plurality of inferred beacon positions stored in the memory area, a beacon position for each of the beacons in the set of beacons in the received data; for each of the obtained, inferred beacon positions, obtain a minimum beacon radius from the plurality of minimum beacon radiuses stored in the memory area to achieve the pre-defined confidence level; apply a Kalman filter to one or more of the beacons observed by the mobile computing device using the inferred beacon positions and the obtained minimum beacon radiuses for said one or more of the beacons, wherein applying the Kalman filter infers a device position for the mobile computing device and computes a device error radius for the inferred device position; and adjust the computed device error radius as a function of a quantity of said one or more of the beacons input to the Kalman filter to achieve the pre-defined confidence level. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method comprising:
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receiving data representing a set of beacons from a computing device, said beacons being observed by the computing device; accessing beacon reference data to access an inferred beacon position for each of the beacons in the set of beacons and to access a minimum beacon radius for each of the inferred beacon positions to achieve a pre-defined confidence level; applying a Kalman filter to one or more of the beacons using the inferred beacon positions and the minimum beacon radiuses for said one or more of the beacons, wherein applying the Kalman filter infers a device position for the computing device and computes a device error radius for the inferred device position; and adjusting the computed device error radius as a function of a quantity of said one or more of the beacons input to the Kalman filter to achieve the pre-defined confidence level. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16)
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17. One or more computer storage media embodying computer-executable components, said components comprising:
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an interface component that when executed causes at least one processor to receive data representing a set of beacons from a computing device, said beacons being observed by the computing device; a beacon store component that when executed causes at least one processor to access beacon reference data to infer a beacon position for each of the beacons in the set of beacons; a beacon radius component that when executed causes at least one processor to determine a minimum beacon radius for each of the inferred beacon positions to achieve a pre-defined confidence level; a refiner component that when executed causes at least one processor to apply a Kalman filter to one or more of the beacons using the inferred beacon positions and the determined minimum beacon radiuses for said one or more of the beacons, wherein applying the Kalman filter infers a device position for the computing device and computes a device error radius for the inferred device position; and a coefficient component that when executed causes at least one processor to adjust the computed device error radius as a function of a quantity of said one or more of the beacons input to the Kalman filter to achieve the pre-defined confidence level, wherein the interface component further executes to provide the inferred device position and the adjusted, computed device error radius to the computing device. - View Dependent Claims (18, 19, 20)
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Specification