Systems and methods for indoor location services
Many indoor location services do not require precise knowledge of the position of a mobile device, but simply whether a mobile device is within a particular zone of an indoor area. Existing signals in an indoor space may provide enough information to distinguish between different zones of an indoor area, allowing a server to provide indoor location services without requiring the installation of new signal sources in the indoor area.
1. An indoor location identification system, comprising:
a communications processor configured to:
receive a measurement from a mobile device in an indoor area, and
receive a signal identifying a type of the mobile device;
a location database for storing information associating, base on the identified type, each zone of a plurality of zones with a respective corresponding measurement range of a plurality of measurement ranges, wherein the plurality of zones is associated with the indoor area; and
a location processor in communication with the communications processor and the location database, wherein the location processor is configured to:
identify the indoor area,
determine that the measurement falls within an identified measurement range of the respective corresponding measurement ranges, and
identify a zone in which the mobile device is located based on:
the measurement received from the mobile device,
the information associating the zone with the corresponding measurement range, wherein the zone is one of the plurality of zones, and
determine that the zone is associated with the identified measurement range.
2. The system of claim 1 , wherein:
the communication processor is further configured to receive a plurality of test measurement packages, wherein a test measurement package identifies a zone of the plurality of zones and a measurement; and
the location processor is further configured to modify the information stored in the location database based on the plurality of measurement packages.
3. The system of claim 2 , wherein a test measurement package is a measurement associated with a zone-specific code.
4. The system of claim 2 , wherein the information associating each zone of the plurality of zones with the corresponding measurement range of the plurality of measurement ranges is a neural network.
5. The system of claim 2 , wherein the information associating each zone of the plurality of zones with the corresponding measurement range of the plurality of measurement ranges is a Bayesian classifier.
6. The system of claim 1 , wherein the communication processor is further configured to provide content to the mobile device based on the zone in which the mobile device is located.
7. The system of claim 1 , wherein the location processor is further configured to identify the indoor area based on a Global Positioning System (GPS) measurement.
8. The system of claim 1 , wherein the location processor is further configured to identify the indoor area based on a cellular tower signal.
9. The system of claim 1 , wherein the location processor is further configured to identify the indoor area based on an application run by the mobile device.
10. The system of claim 1 , wherein the communication processor is further configured to transmit a location request to the mobile device, and wherein the location processor is further configured to identify the indoor area based on a response to the location request.
11. The system of claim 1 , wherein the measurement includes a Global Positioning System (GPS) measurement.
12. The system of claim 1 , wherein the measurement includes a BLUETOOTH signal strength.
13. The system of claim 1 , wherein the measurement includes a WI-FI signal strength.
14. The system of claim 1 , wherein the measurement includes a cellular tower signal strength.
15. The system of claim 1 , wherein the measurement includes a local magnetic field strength.
16. The system of claim 1 , wherein the measurement includes a local magnetic field direction.
17. The system of claim 1 , wherein the measurement includes an ambient light intensity.
18. The system of claim 1 , wherein the measurement includes an intensity of sound of a predetermined frequency.
19. The system of claim 1 , wherein the measurement includes a time.
20. The system of claim 1 , wherein the measurement includes at least two of a Global Positioning System (GPS) measurement, a BLUETOOTH signal strength, a WI-FI signal strength, a cellular tower signal strength, a local magnetic field strength, a local magnetic field direction, an ambient light intensity, an intensity of sound of a predetermined frequency, and a time.
21. A computer-implemented method for identifying an indoor location of a mobile device in an indoor area, comprising:
receiving, at a communications processor and from the mobile device in the indoor area, a measurement;
receiving a signal identifying a type of the mobile device;
storing, in a location database, information associating, based on the identified type, each zone of a plurality of zones with a corresponding measurement range of a plurality of measurement ranges, wherein the plurality of zones is associated with the indoor area;
identifying, by a location processor in communication with the communications processor and the location database, the indoor area;
determining that the measurement falls within an identified measurement range of the respective corresponding measurement ranges; and
identifying a zone in which the mobile device is located based on:
the measurement,
the information associating the zone with the corresponding measurement range, wherein the zone is one of the plurality of zones, and
determining that the zone is associated with the identified measurement range.
22. The method of claim 21 , further comprising:
receiving a plurality of test measurement packages, wherein a test measurement package identifies a zone of the plurality of zones and a measurement; and
modifying a model associating each zone of the plurality of zones with a corresponding measurement range,
wherein each corresponding measurement range is based on the plurality of test measurement packages, and
wherein the zone in which the mobile device is located is identified based on the model.
23. The method of claim 22 , wherein a test measurement package is a measurement associated with a zone-specific code.
24. The method of claim 22 , wherein the model is a neural network.
25. The method of claim 22 , wherein the model is a Bayesian classifier.
26. The method of claim 21 , further comprising providing content to the mobile device based on the zone in which the mobile device is located.
27. The method of claim 21 , wherein the indoor area is identified based on a Global Positioning System (GPS) measurement.
28. The method of claim 21 , wherein the indoor area is identified based on a cellular tower signal.
29. The method of claim 21 , wherein the indoor area is identified based on an application run by the mobile device.
30. The method of claim 21 , further comprising transmitting a location request to the mobile device, wherein the indoor area is identified based on a response to the location request.
31. The method of claim 21 , wherein the measurement includes a Global Positioning System (GPS) measurement.
32. The method of claim 21 , wherein the measurement includes a BLUETOOTH signal strength.
33. The method of claim 21 , wherein the measurement includes a WI-FI signal strength.
34. The method of claim 21 , wherein the measurement includes a cellular telephone signal strength.
35. The method of claim 21 , wherein the measurement includes a local magnetic field strength.
36. The method of claim 21 , wherein the measurement includes a local magnetic field direction.
37. The method of claim 21 , wherein the measurement includes an ambient light intensity.
38. The method of claim 21 , wherein the measurement includes an intensity of sound of a predetermined frequency.
39. The method of claim 21 , wherein the measurement includes a time.
40. The method of claim 21 , wherein the measurement includes at least two of a Global Positioning System (GPS) measurement, a BLUETOOTH signal strength, a WI-FI signal strength, a cellular tower signal strength, a local magnetic field strength, a local magnetic field direction, an ambient light intensity, an intensity of sound of a predetermined frequency, and a time.