Indoor localization of mobile devices

• US 8,588,097 B1
• Filed: 04/20/2011
• Issued: 11/19/2013
• Est. Priority Date: 04/20/2011
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Ogale, Abhijit, Rivlin, Ehud
• Primary Examiner(s): HAN, CLEMENCE S
• Application Number:
  US13/090,785
• Time in Patent Office: 944 Days
• Field of Search: None
• US Class Current: 370/252
• CPC Class Codes: G01S 5/02521 using a radio-map
• CPC Class Codes: H04L 41/12 Discovery or management of network topologies
• CPC Class Codes: H04W 16/20 for indoor coverage or short range network deployment
• CPC Class Codes: H04W 24/10 Scheduling measurement reports ; Arrangements for measurement reports
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/024 Guidance services
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/33 for indoor environments, e.g. buildings
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

Aspects of the present disclosure provide systems and methods for generating models of a wireless network environment in an indoor space which may be used to predict an indoor location. The disclosure relates to collecting wireless network access point identifier information and power level observed at various locations are collected to generate various signal maps. The signal maps may be used to generate models of the indoor space. In one example, a voting model may use a probability distribution of a plurality of signal maps in order to identify a location with a highest probability of overlap with current signals received at a client device. Once a location has been identified, it may be used to assist with any number of navigational functions, such as providing turn by turn directions to another indoor location, for example, a conference room or exit, or simply providing information about the current location.

Claims

1. A method comprising:

receiving a set of maps, each map of the set being associated with a wireless network access point identifier and representative rectangles each representing a signal strength;

scanning for wireless network signals to identify one or more wireless network access point identifiers and associated signal strengths;

selecting, by a processor, one or more representative rectangles of the set of rectangular maps based on the identified one or more wireless network access point identifiers and associated signal strengths;

identifying an intersection rectangle based on the intersection of the one or more identified rectangles; and

identifying a current location of a client device based on the identified intersection rectangle.

2. The method of claim 1, wherein identifying the current location of the client device is based on a previous location of the client device and an isotropic diffusion model.

3. The method of claim 1, wherein identifying the current location of the client device is based on a previous location of the client device and an anisotropic diffusion model.

4. The method of claim 3, further comprising receiving input from a directional device, wherein the anisotropic diffusion model is based on the received input.

5. The method of claim 1, wherein the set of maps correspond to a floor of a building.

6. The method of claim 1, wherein the current location is identified based on a center point of the identified intersection rectangle.

7. The method of claim 1, wherein the current location is identified based on a weighted centroid of the identified intersection rectangle.

8. The method of claim 1, wherein the identified wireless network access point identifiers are MAC addresses.

9. A device for determining a current location, the device comprising:

memory;

a processor to;

collect one or more wireless network access point identifiers and signal strengths for an indoor geographic area;

quantize the collected signal strengths into a plurality of quantized signal strengths for each collected wireless network access point identifier;

generate, for each received wireless network access point identifier, a map including a rectangle representing each of the quantized signal strengths;

store each map in the memory;

scan for wireless network signals to identify one or more wireless network access point identifiers and associated signal strengths;

select one or more representative rectangles of the set of maps based on the identified one or more wireless network access point identifiers and associated signal strengths;

identify an intersection rectangle based on the intersection of the one or more identified rectangles; and

identify a current location of a client device based on the identified intersection rectangle.

10. The device of claim 9, wherein the collected wireless network access point identifiers are MAC addresses.

11. A device for determining a current location of the device, the device comprising:

memory storing a set of maps, each map of the set being associated with a wireless network access point identifier and representative rectangles each representing a signal strength;

a processor to;

scan for wireless network signals to identify one or more wireless network access point identifiers and associated signal strengths;

select one or more representative rectangles of the set of maps based on the identified one or more wireless network access point identifiers and associated signal strengths;

identify an intersection rectangle based on the intersection of the one or more identified rectangles; and

identify a current location of the device based on the identified intersection rectangle.

12. The device of claim 11, wherein the processor identifies the current location based on a previous location of the device and an isotropic diffusion model.

13. The device of claim 11, wherein the processor identifies the current location based on a previous location of the device and an anisotropic diffusion model.

14. The device of claim 13, wherein the processor receives input from a directional device, and the anisotropic diffusion model is based on the received input.

15. The device of claim 11, wherein the set of maps correspond to a floor of a building.

16. The device of claim 11, wherein the processor identifies the current location based on a center point of the identified intersection rectangle.

17. The device of claim 11, wherein the processor identifies the current location based on a weighted centroid of the identified intersection rectangle.

18. The device of claim 11, wherein the wireless network access point identifiers associated with each map are MAC addresses.