IP Library › Granted Patent US 10,383,086
Granted Patent B2
US 10,383,086 · App. 15/777,609 · Granted Aug 13, 2019

Facilitation of indoor localization and fingerprint updates of altered access point signals

Inventors: Shueng Han Gary Chan (Hong Kong, CN); Suining He (Hong Kong, CN)
Assignee: THE HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY
H04W64/006G01S5/0252H04B17/318H04W24/02H04W64/00H04W64/003
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Quick Facts
Patent No.
US 10,383,086
App. No.
15/777,609
Granted
Aug 13, 2019
Kind
B2
Abstract

When access point signals are altered, conventional fingerprint-based indoor localization techniques are note accuracy. Localization with altered access point and fingerprint updating can achieves accurate indoor localization and automatically update a fingerprint database with altered access points. Using subset sampling, the system detect the altered access points, filter them out by a received signal strength vector and find the location of a client. Given the received signal strength vectors received and the estimated location, the system can update a fingerprint database with the signal changes by applying a non-parametric Gaussian process regression method.

Claims (52)

1. A method, comprising:

identifying, by a wireless network device comprising a processor, a first location associated with an access point device of access point devices;

in response to identifying a second location associated with the access point device and different than the first location, determining, by the wireless network device, that the access point device has become an altered access point device;

associating, by the wireless network device, a reference location with the altered access point device;

applying, by the wireless network device, a Gaussian regression analysis to the reference location;

filtering, by the wireless network device, the altered access point device from the access point devices; and

in response to the filtering, updating, by the wireless network device, the reference location associated with the altered access point device.

2. The method of claim 1 , wherein the filtering comprises partitioning a received signal strength into a group of received signal strengths.

3. The method of claim 2 , further comprising:

weighting, by the wireless network device, the group of received signal strengths according to a signal strength similarity of the group of received signal strengths.

4. The method of claim 3 , further comprising:

regressing, by the wireless network device, the group of received signal strengths to reflect an access point environment associated with the access point devices.

5. The method of claim 4 , wherein the regressing comprises applying a Gaussian regression.

6. The method of claim 4 , further comprising:

in response to the regressing, updating, by the wireless network device, a data structure associated with the access point devices.

7. The method of claim 6 , wherein the updating comprises updating based on feedback received from on user input.

8. A system, comprising:

a processor; and

a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising:

identifying a first power associated with an access point device of access point devices;

in response to identifying a second power associated with the access point device and different than the first power, determining that the access point device has become an altered access point device;

estimating a likelihood of an altered access point signal associated with the altered access point device;

grouping received signal strengths associated with the access point devices, resulting in grouped signal strengths;

based on the grouped signal strengths, estimating corresponding locations associated with the access point devices, resulting in estimated corresponding locations;

applying a Gaussian regression analysis to the estimated corresponding locations;

in response to determining a first location of a mobile device, identifying a second location associated with the altered access point device; and

updating a data store with a received signal strength associated with the altered access point device relative to the first location of the mobile device.

9. The system of claim 8 , wherein the identifying the second location is associated with determining a density of the grouped signal strengths.

10. The system of claim 9 , wherein the operations further comprises:

in response to the identifying the second location associated with the altered access point device, identifying the altered access point device.

11. The system of claim 9 , wherein the operations further comprises:

associating the received signal strengths with the estimated corresponding locations.

12. The system of claim 11 , wherein the associating comprises employing a Gaussian process regression.

13. A non-transitory machine-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:

identifying a first power associated with an access point device of access point devices;

in response to identifying a second power associated with the access point device that is different than the first power, determining that the access point device has become an altered access point device;

measuring received signal strengths from the access point devices;

based on the received signal strengths, grouping the access point devices, resulting in grouped access point devices;

in response to the grouping of the access point devices, estimating a location of the altered access point device resulting in an estimated location; and

applying a regression analysis to the estimated location of the altered access point device.

14. The non-transitory machine-readable storage medium of claim 13 , wherein the operations further comprise:

weighting the received signal strengths according to a signal strength similarity of the received signal strengths.

15. The non-transitory machine-readable storage medium of claim 14 , wherein the regression analysis comprises a Gaussian regression analysis.

16. The non-transitory machine-readable storage medium of claim 15 , wherein the operations further comprise:

in response to the applying the regression analysis, verifying the estimated location of the altered access point device.

17. The non-transitory machine-readable storage medium of claim 16 , wherein the verifying comprises comparing the estimated location to a previously stored location associated with the altered access point device.

18. The non-transitory machine-readable storage medium of claim 17 , wherein the operations further comprise:

in response to the comparing the estimated location to the previously stored location associated with the altered access point device, updating a data structure.

19. The non-transitory machine-readable storage medium of claim 13 , wherein the location is a first location, wherein the operations further comprise:

determining a second location associated with a mobile device, and wherein the second location is used to determine the estimated location.

20. The non-transitory machine-readable storage medium of claim 13 , wherein the location is a first location, wherein the operations further comprise:

estimating a second location associated with the grouped access point devices, and wherein the second location is used to determine the estimated location.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 18, 2018
From: CHAN, SHUENG HAN GARY; HE, SUINING
To: THE HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY
Reel/Frame 045849/0136 →
Continuity (2)
Provisional Application 62386137 · Nov 19, 2015
Related Publication 20180332558A1 · Nov 15, 2018