Location determination with geographic and bias tuning

• US 7,986,953 B2
• Filed: 06/01/2009
• Issued: 07/26/2011
• Est. Priority Date: 06/01/2009
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Rice, Christopher R., Sharpe, Timothy, Seshadri, Aravind Krishnamachari
• Primary Examiner(s): Patel; Ajit
• Application Number:
  US12/475,987
• Publication Number:
  US 20100304755A1
• Time in Patent Office: 785 Days
• Field of Search: None
• US Class Current: 455/456.1
• CPC Class Codes: G01S 5/0268 by deriving positions from different combinations of signals or of estimated positions in a single positioning system
• CPC Class Codes: G01S 5/14 Determining absolute distances from a plurality of spaced points of known location

Abstract

A method for determining location of an observer device is disclosed. The method includes receiving basestation distance data for the observer device and applying a location algorithm to the basestation distance data to determine a computed location of the observer device. The method further includes determining whether the basestation distance data correlates to any of a plurality of observer device conditions. Upon determining the basestation data correlates with one of the observer device conditions, a location bias associated with that observer device condition is employed to correct the computed location of the observer device and produce a corrected location.

Claims

1. A method for determining location of an observer device, comprising:

receiving basestation distance data for the observer device, the basestation distance data including identification of one or more basestations and information indicating a distance between the observer device and each of the one or more basestations;

applying a location algorithm to the basestation distance data to calculate a computed location of the observer device;

determining whether the basestation distance data correlates to any of a plurality of observer device conditions; and

if the basestation distance data correlates with one of the plurality of observer device conditions, correcting the computed location with a location bias that is associated with said one of the plurality of observer device conditions, in order to provide a corrected location of the observer device.

2. The method of claim 1, where the location bias is a vector.

3. The method of claim 1, where the location bias depends at least in part upon spatial layer data.

4. The method of claim 3, where the spatial layer data includes weather data which is contemporaneous with the basestation distance data.

5. The method of claim 3, where the spatial layer data includes map data specifying locations of streets and buildings in a vicinity of the computed location of the observer device.

6. The method of claim 5, where the location bias is generated such that the corrected location of the observer device is co-located with a building or a street included in the map data.

7. The method of claim 3, where the spatial layer data includes map data specifying topography in a vicinity of the computed location of the observer device.

8. The method of claim 1, further comprising:

establishing a plurality of geographic regions; and

selecting, for each of the plurality of geographic regions, an optimal location algorithm from a plurality of location algorithms.

9. The method of claim 8, further comprising determining whether the basestation distance data correlates to any of the plurality of geographic regions, and where applying the location algorithm to the basestation distance data includes applying the optimal location algorithm for one of the plurality of geographic regions if it is determined that the basestation distance data correlates to said one of the plurality of geographic regions.

10. The method of claim 8, where selecting an optimal location algorithm for each of the plurality of geographic regions is based on comparing relative performance, for geographic region, of the plurality of location algorithms.

11. The method of claim 10, where comparing relative performance includes comparing relative computation time of the plurality of location algorithms.

12. The method of claim 10, where comparing relative performance includes comparing relative precision of the plurality of location algorithms.

13. A method of determining location of an observer device, comprising:

establishing a plurality of geographic regions;

comparing, for each of the plurality of geographic regions, relative performance of a plurality of location algorithms in order to determine an optimal location algorithm for each of the plurality of geographic regions;

receiving basestation distance data for the observer device;

determining whether the basestation distance data correlates to any of the plurality of geographic regions; and

if the basestation distance data correlates to one of the plurality of geographic regions, applying the optimal location algorithm for that one of the plurality of geographic regions to the basestation distance data to determine a computed location for the observer device.

14. The method of claim 13, where comparing relative performance includes comparing relative precision of the plurality of location algorithms.

15. The method of claim 13, where comparing relative performance includes comparing relative computation time of the plurality of location algorithms.

16. The method of claim 13, where comparing relative performance of the plurality of location algorithms is performed repeatedly over time to dynamically update the optimal location algorithm that is associated with each of the plurality of geographic regions.

17. A computing system for determining location of an observer device, comprising:

a logic subsystem; and

a data-holding subsystem including instructions executable by the logic subsystem to;

process basestation distance data received for the observer device to determine whether the basestation distance data correlates to any of a plurality of geographic regions;

select an optimal location algorithm associated with one of the plurality of geographic regions if it is determined that the basestation distance data correlates to said one of the plurality of geographic regions;

apply the optimal location algorithm to the basestation distance data to obtain a computed location of the observer device;

determine whether the basestation distance data correlates to any of a plurality of observer device conditions;

select a location bias associated with one of the plurality of observer device conditions if it is determined that the basestation distance data correlates to said one of the plurality of observer device conditions; and

correct the computed location of the observer device with the location bias to obtain a corrected location of the observer device.

18. The computing system of claim 17, where the location bias is a vector generated based on spatial layer data.

19. The computing system of claim 17, where the instructions are further executable to assemble and repeatedly update, over time, a store of observer device conditions and associated location biases that are usable to adjust output of location algorithms applied to basestation distance data.

20. The computing system of claim 17, where the instructions are further executable to repeatedly update, over time, a store identifying geographic regions and associated optimal location algorithms that are usable to calculate locations of observer devices in the geographic regions.