Location determination using RF fingerprinting

• US 6,393,294 B1
• Filed: 03/22/2000
• Issued: 05/21/2002
• Est. Priority Date: 09/22/1998
• Status: Expired due to Term

• Current Assignee: Polaris Wireless Incorporated
• Original Assignee: Polaris Wireless Incorporated
• Inventors: Rao, Padmanabha R., Siccardo, Paolo, Dressler, Robert M., Spain, David S., Perez-Breva, Luis, Chong, Chee-Yee
• Primary Examiner(s): BANKS HAROLD, MARSHA DENISE
• Application Number:
  US09/532,418
• Time in Patent Office: 790 Days
• Field of Search: 455/456, 455/454, 455/440, 455/186.1, 455/FOR.100, 342/457
• US Class Current: 455/456.5
• CPC Class Codes: G01S 5/0236 Assistance data, e.g. base station almanac
• CPC Class Codes: G01S 5/02526 using non-dedicated equipment, e.g. user equipment or crowd-sourcing
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

A method for determining the location of a mobile unit (MU) in a wireless communication system and presenting it to a remote party. The location of a remote MU is determined by comparing a snapshot of a predefined portion of the radio-frequency (RF) spectrum taken by the MU to a reference database containing multiple snapshots taken at various locations. The result of the comparison is used to determine if the MU is at a specific location. The comparison may be made in the MU, or at some other location situated remotely from the MU. In the latter case, sufficient information regarding the captured fingerprint is transmitted from the MU to the remote location. The database may be pre-compiled or generated on-line.

Claims

1. A method for locating the position of a mobile unit in a wireless communication network, the method comprising:

providing a fingerprint database having a multiplicity of RF fingerprints, each fingerprint in the fingerprint database having an associated unique location;

generating a location probability distribution by assigning respective probabilities to locations associated with fingerprints in the fingerprint database;

capturing an RF fingerprint in the mobile unit;

updating the location probability distribution using the fingerprint, so captured, and the fingerprint database by generating an additional probability distribution based on the fingerprint database and the fingerprint, so captured, and combining the additional probability distribution with the location probability distribution to generate an updated location probability distribution; and

using the updated location probability distribution to generate an estimate of the position of the mobile unit.

2. The method of claim 1 wherein the estimate of the location of the mobile unit includes an expected value derived from the location probability distribution.

3. The method of claim 1 wherein the estimate of the location of the mobile unit includes a most probable value derived from the location probability distribution.

4. The method of claim 1 wherein generating the additional location probability distribution comprises comparing the fingerprint, so captured, to fingerprints in the fingerprint database.

5. The method of claim 1 wherein the estimate of the location of the mobile unit is generated after updating the location probability distribution.

6. The method of claim 1 wherein the location probability distribution is generated from an initial state in which each location is assigned the same probability.

7. The method of claim 1, and further comprising generating a Markov model that provides, for at least one location, a transition probability that the mobile unit at that location will transition to another location within a given time interval.

8. The method of claim 7, and further comprising using the Markov model to modify the location probability distribution prior to updating the location probability distribution using the fingerprint, so captured, and the fingerprint database.

9. The method of claim 1, and further comprising:

capturing an additional fingerprint of the RF spectrum in the mobile unit; and

using the additional fingerprint, so captured, and the fingerprint database to update the updated location probability distribution and so generate a further updated location probability distribution.

10. The method of claim 1 wherein the fingerprint database is generated from field measurements at predetermined locations and from interpolation of fingerprints at locations in between the predetermined locations.

11. The method of claim 1 wherein the fingerprint database is generated by calculations with RF prediction models.

12. The method of claim 1 wherein the fingerprint database is generated by calculations with RF prediction models and calibrated using field measurements.

13. The method of claim 1 wherein:

the fingerprint database is stored in an Other Party that is linked to the mobile unit via the wireless communication network; and

the fingerprint, so captured, is transmitted over the wireless network to the Other Party; and

the Other Party generates the location probability distribution, updates the probability distribution, and generates the estimate of the position of the mobile unit.

14. The method of claim 1 wherein the mobile unit generates the estimate of the position of the mobile unit.

15. A method for locating the position of a mobile unit in a wireless communication network, the method comprising:

providing a fingerprint database having a multiplicity of RF fingerprints, each fingerprint in the fingerprint database having an associated unique location;

capturing a measured RF fingerprint from the mobile unit;

generating a location probability distribution by assigning respective probabilities to locations associated with fingerprints in the fingerprint database by comparing the measured RF fingerprint to each of the multiplicity of RE fingerprints; and

using the location probability distribution to generate an estimate of the position of the mobile unit.

16. The method of claim 15 wherein the estimate of the location of the mobile unit includes an expected value derived from the location probability distribution.

17. The method of claim 15 wherein the estimate of the location of the mobile unit includes a most probable value derived from the location probability distribution.

18. The method of claim 15 wherein the location probability distribution is generated from an initial state in which each location is assigned the same probability.

19. The method of claim 15, and further comprising generating a Markov model that provides, for each location in the fingerprint database, transition probabilities that the mobile unit at that location will transition to other locations in the fingerprint database within a given time interval.

20. The method of claim 19, and further comprising using the Markov model to modify the location probability distribution prior to updating the location probability distribution using the fingerprint, so captured, and the fingerprint database.

21. The method of claim 15, and further comprising:

capturing an additional fingerprint of the RF spectrum in the mobile unit and

using the additional fingerprint, so captured, and the fingerprint database to update the updated location probability distribution and so generate a further updated location probability distribution.

22. A method for locating the position of a mobile unit in a wireless communication network, the method comprising:

providing a fingerprint database having a multiplicity of RF fingerprints, each fingerprint in the fingerprint database having an associated unique location;

generating a location probability distribution by assigning respective probabilities to locations associated with fingerprints in the fingerprint database;

capturing a series of fingerprints in the mobile unit at different times;

for each successively captured fingerprint, recursively updating the location probability distribution, each updating using a previous version of the location probability distribution, the fingerprint database, and the successively captured fingerprint; and

using the updated location probability distribution to generate an estimate of the position of the mobile unit.

23. The method of claim 22, and further comprising providing a Markov model that provides, for at least one location, a transition probability that the mobile unit at that location will transition to another location within a time interval between successive captures.

24. The method of claim 23, and further comprising using the Markov model to modify the location probability distribution prior to updating the location probability distribution using the fingerprint so captured, and the fingerprint database.

25. The method of claim 22 wherein the fingerprint database is generated from field measurements at predetermined locations and from interpolation of fingerprints at locations in between the predetermined locations.

26. The method of claim 22 wherein the fingerprint database is generated by calculations with RF prediction models.

27. The method of claim 22 wherein the fingerprint database is generated by calculations with RF prediction models and calibrated using field measurements.

28. A method for tracking the position of a mobile unit in a wireless communication network, the method comprising:

providing a fingerprint database having a multiplicity of RF fingerprints, each fingerprint in the predicted fingerprint database having an associated unique location;

capturing a series of measured RF fingerprints from the mobile unit at different times;

starting from an initial state in which each location is assigned the same probability;

for each successively captured RF fingerprint, recursively updating the location probability distribution, each updating using the previous version of the location probability distribution and a location probability distribution calculated from the captured fingerprint; and

using the series of location probability distributions so calculated to generate an updated series of estimates of the position of the mobile unit.

29. The method of claim 28, and further comprising generating a Markov model that provides, for each location in the fingerprint database, transition probabilities that the mobile unit at that location will transition to other locations in the fingerprint database within a given time interval.

30. The method of claim 29, and further comprising using the Markov model to modify the location probability distribution prior to updating the location probability distribution using the fingerprint, so captured, and the predicted fingerprint database.

31. The method of claim 29, and further comprising:

using the Markov model to predict the location probability distribution to some future time where a measured fingerprint may not be available; and

using the predicted location probability distribution to generate an estimate of the position of the mobile unit at that future time.

32. A method for locating the position of a mobile unit in a wireless communication network, the method comprising:

providing a fingerprint database having a multiplicity of RF fingerprints, each fingerprint in the fingerprint database having an associated unique location;

providing a Markov model that provides, for at least one location, transition probabilities that the mobile unit at that location will transition to other locations within a time interval between successive fingerprint captures;

generating a location probability distribution by assigning respective probabilities to locations associated with fingerprints in the fingerprint database;

capturing a series of fingerprints in the mobile unit at different times;

for each newly captured fingerprint, recursively updating the location probability distribution, each updating being performed by;

subjecting the previous version of the location probability distribution to the Markov model to provide a predicted probability distribution reflecting the transition probabilities in the Markov model,

comparing the newly captured fingerprint with the fingerprint database to generate an additional probability distribution based on the fingerprint database and the newly captured fingerprint, and

combining the additional probability distribution with the predicted probability distribution to provide the newly updated location probability distribution; and

using an updated version of the location probability distribution to generate an estimate of the position of the mobile unit.

33. The method of claim 32, and further comprising using the Markov model to modify the location probability distribution prior to updating the location probability distribution using the fingerprint, so captured, and the predicted fingerprint database.

34. The method of claim 32, and further comprising:

using the Markov model to predict the location probability distribution to some future time where a measured fingerprint may not be available; and

using the predicted location probability distribution to generate an estimate of the position of the mobile unit at that future time.

35. The method of claim 32 wherein the fingerprint database is generated from field measurements at predetermined locations and from interpolation of fingerprints at locations in between the predetermined locations.

36. The method of claim 32 wherein the fingerprint database is generated by calculations with RF prediction models.