TRACKING LOCATION OF MOBILE DEVICE IN A WIRELESS NETWORK

• US 20140045518A1
• Filed: 01/11/2012
• Published: 02/13/2014
• Est. Priority Date: 11/19/2010
• Status: Active Grant

• Current Assignee: Commonwealth Scientific & Industrial Research Organisation (Commonwealth Of Australia As Represented By Department Of Industry, Innovation, Climate Change, Science, Research And Tertiary Education)
• Original Assignee: Commonwealth Scientific & Industrial Research Organisation (Commonwealth Of Australia As Represented By Department Of Industry, Innovation, Climate Change, Science, Research And Tertiary Education)
• Inventors: Sathyan, Thuraiappah, Hedley, Mark
• Granted Patent: US 9,197,996 B2
• Time in Patent Office: Days
• US Class Current: 455/456.1
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds

Abstract

A method and system for dynamically tracking the location of mobile nodes (104, 106, 108, 110_n) in a wireless network (102) is disclosed. The method comprises: for each mobile node, dynamically measuring the range between the mobile node and at least one neighbouring node (step 202); and executing a Bayesian tracking algorithm for each mobile node (step 204). The algorithm has the measured range as an input, exchanges data with tracking algorithms for neighbouring mobile nodes, and utilises a statistical model of error in measured range and a statistical model of node motion to dynamically determine location.

Claims

1. A method for dynamically tracking the location of mobile nodes in a wireless network comprising:

for each said mobile node, dynamically measuring the range between said mobile node and at least one neighbouring node; and

executing a Bayesian tracking algorithm for each mobile node, said algorithm having said measured range as an input, exchanging data with tracking algorithms for neighbouring mobile nodes, and utilising a statistical model of error in measured range and a statistical model of node motion to dynamically determine location.

2. The method of claim 1, wherein said tracking algorithm determines location using a current data set and temporal tracking.

4. The method of claim 2, wherein said data includes node location distribution information and said tracking algorithm uses a weighted least squares calculation to determine location.

5. The method of claim 4, wherein said tracking algorithm is implemented as a Kalman filter or interacting multiple models.

7. The method of claim 5, wherein said model of node motion is nearly constant velocity.

8. The method of claim 1, wherein said tracking algorithm determines location directly using range measurements to neighbouring nodes and said range error model.

10. The method of claim 8, wherein said data includes location information and an uncertainty measure, and said tracking algorithm uses the range error model modified by neighbour node location variance to determine location.

11. The method of claim 10, wherein said tracking algorithm is implemented as a particle filter or as interacting multiple model particle filters.

13. The method of claim 11, wherein said model of node motion is nearly constant velocity.

14. The method of claim 1, wherein said statistical model of error in measured range is a likelihood function with constant positive value beyond the region of support, a plurality of motion models are used, and said nodes transmit data to an access point, and said tracking algorithm for at least some of said mobile nodes is executed via said access point.

17. The method of claim 1, wherein said neighbouring nodes each transceive data from each other, from which range is measured and location is determined, and wherein each said tracking algorithm is implemented on a respective said node.

3. (canceled)

6. (canceled)

9. (canceled)

12. (canceled)

15-16. -16. (canceled)

18. A wireless tracking system comprising:

a plurality of mobile wireless devices, each said mobile device including a transmission circuit for the sending data as radio signals, and a range estimation module for dynamically measuring the range between said device and at least one neighbouring device; and

a plurality of anchor wireless devices, each said anchor device being at a known location and including a range estimation circuit for dynamically measuring the range between said anchor device and at least one neighbouring device; and

a Bayesian tracking filter for each said mobile device for receiving said measured range as an input and exchanging data with tracking algorithms for neighbouring mobile nodes, said tracking filter utilising a statistical model of error in measured range and a statistical model of node motion to dynamically determine location.

19. The system of claim 18, wherein each mobile wireless device includes computational circuitry to implement the respective tracking filter, and said transmitter circuit additionally operates as a transceiver for exchanging data with neighbouring mobile devices.

20. The system of claim 18, wherein each mobile wireless device transmits range measurements to another computational device that executes the tracking filter for said mobile node and wherein each mobile wireless device transmits range measurements to one of a plurality of access points, and wherein said tracking filters are accessed via said access points.

22. The system of claim 19, wherein each said tracking filter determines location using a current data set and temporal tracking.

24. The system of claim 22, wherein said data includes location distribution information and said tracking filters use a weighted least squares calculation to determine location.

25. The system of claim 24, wherein said tracking filters are implemented as a Kalman filter or as interacting multiple models.

27. The system of claim 25, wherein said model of node motion is nearly constant velocity.

28. The system of claim 19, wherein said tracking filters directly determine location using range measurements to neighbouring modes.

29. The system of claim 28, wherein said data includes location information and said tracking filters use the range error model directly or modified by neighbour node location variance to determine location.

31. The system of claim 29, wherein said tracking filters are implemented as particle filters or as interacting multiple model particle filters.

33. The system of claim 31, wherein said model of node motion is nearly constant velocity, wherein said model of error in measured range is a likelihood function with constant positive value beyond the region of support, and wherein a plurality of motion models are used in each tracking filter.

21. (canceled)

23. (canceled)

26. (canceled)

30. (canceled)

32. (canceled)

34-35. -35. (canceled)

36. A mobile wireless device comprising:

a transceiver circuit for sending and receiving data to neighbouring said mobile devices;

a range estimation module for dynamically measuring the range between said device and at least one neighbouring device; and

a Bayesian tracking filter that receives said measured range as an input and exchanges data with tracking algorithms for neighbouring mobile nodes, and utilises a statistical model of error in measured range and a statistical model of node motion to dynamically determine location.

37. The device of claim 36, wherein each said tracking filter determines location using a current data set and temporal tracking.

39. The device of claim 37, wherein said data includes location distribution information and said tracking filters use a weighted least squares calculation to determine location.

40. The device of claim 39, wherein said tracking filters are implemented as a Kalman filter or as interacting multiple models.

42. The device of claim 40, wherein said model of node motion is nearly constant velocity.

43. The device of claim 36, wherein said tracking filters directly determine location using range measurements to neighbouring modes.

44. The device of claim 43, wherein said data includes location information and said tracking filters use the range error model directly or modified by neighbour node location variance to determine location.

46. The device of claim 44, wherein said tracking filters are implemented as particle filters or interacting multiple model particle filters.

48. The device of claim 46, wherein said model of node motion is nearly constant velocity, wherein said model of error in measured range is a likelihood function with constant positive value beyond the region of support, and wherein at least two motion models are used in each tracking filter.

38. (canceled)

41. (canceled)

45. (canceled)

47. (canceled)

49-50. -50. (canceled)

51. A mobile wireless device comprising:

a range estimation module for dynamically measuring the range between said device and said neighbouring devices; and

a transmitter circuit for sending measured ranges to separate device having a tracking filter for said mobile device.