Self monitoring GPS on mobile device

• US RE46,237 E1
• Filed: 09/19/2013
• Issued: 12/13/2016
• Est. Priority Date: 09/02/2004
• Status: Expired

• Current Assignee: Robert Anderson Malaney
• Original Assignee: Nariste Networks Pty Ltd
• Inventors: Malaney, Robert Anderson
• Primary Examiner(s): Deb, Anjan K
• Application Number:
  US14/031,992
• Time in Patent Office: 1,181 Days
• Field of Search: 342/357.15, 342/357.28, 342/357.29, 342/357.42, 342/357.74, 455/574, 701/408, 701/468, 701/518, 701/455, 701/469, 701/485, 701/490, 701/524
• US Class Current: 1/1
• CPC Class Codes: G01S 19/34 Power consumption
• CPC Class Codes: G01S 19/42 Determining position

Abstract

A method, a mobile device arid a computer program product for acquiring GPS on a mobile device possessing GPS capability are disclosed. The method comprises the step of setting a current value of the period of the power-up phase of the GPS dependent upon adaptive predictions of when the GPS should be powered on to meet specifications on positioning accuracy and GPS acquisition time.

Claims

1. A method of acquiring GPS position on a mobile wireless communication device for a wireless communications network, said mobile wireless communication device possessing wireless communications capability for said wireless communications network and an embedded GPS Global Positioning System (GPS) module for GPS capability, said method comprising:

setting a current value of a period of a power-up phase of said GPSadaptively predicting when said GPS module should be powered on to meet specifications on at least one of positioning accuracy and GPS acquisition time, said adaptively predicting when said GPS module should be powered on being dependent on a current radio position and error of said mobile wireless communication device in the wireless network, said position error being determined from the wireless communications network and adaptive predictions of when said GPS should be powered on to meet specifications on positioning accuracy and GPS acquisition time, said wireless communications network being different from said GPS.

2. The method according to claim 1, wherein the current radio position and error are is determined using Cramer-Rao Bounds.

3. The method according to claim 1, wherein the period of the power-up phase is set while retaining the accuracy of a position fix and acquisition times of the position fix within acceptable statistical bounds.

4. The method according to claim 1, wherein said mobile device determines dynamically an optimal period of the power-up phase.

5. The method according to claim 1, wherein said current value of said period of the power-up phase is set using adaptive-filter and machine-learning techniques.

6. The method according to claim 5, wherein said adaptive-filter and machine learning techniques are implemented using said mobile device.

7. The method according to claim 1 further comprising:

if at least one of said position error and acquisition times of GPS fail to meet acceptable bounds, reducing said value of said period of the power-up phase; and

if at least one of said position error and acquisition times of GPS meet said acceptable bounds, increasing said value of said period of the power-up phase.

8. The method according to claim 1, wherein said mobile device comprises a power limited device.

9. The method according to claim 1, wherein said embedded GPS module comprises an embedded GPS device.

10. The method according to claim 1, further comprising the step of making adaptive predictions using a neural network.

11. The method according to claim 1, wherein said setting step comprises:

predicting a next position at a next scheduled power-up time using a number of last positions reported of said mobile device in the wireless network determined from the wireless network;

comparing said predicted position at said next power-up time with an actual position of said mobile device in the wireless network determined from the wireless network and determining said position error; and

storing current values of said period of the power-up phase, acquisition time, said position error, and position in a storage unit of said mobile device.

37. The method according to claim 1, further comprising setting a current value of a period of the power-up phase of said GPS dependent upon adaptive predictions of when said GPS should be powered on to meet specifications on positioning accuracy or GPS acquisition time.

12. A mobile wireless communication device for a wireless communications network, said mobile wireless communication device possessing wireless communications capability for said wireless communications network, said mobile wireless communication device comprising:

a processor;

a memory coupled to said processor;

an embedded GPS Global Positioning System (GPS) module coupled to said processor providing a GPS capability for said mobile wireless communication device; and

means for setting a current value of a period of a power-up phase of said GPS adaptively predicting when said GPS module should be powered on to meet specifications on at least one of positioning accuracy and GPS acquisition time, said adaptively predicting when said GPS module should be powered on being dependent on the current radio a position and error of said mobile wireless communication device in the wireless network, said position error being determined from the wireless communications network and adaptive predictions of when said GPS should be powered on to meet specifications on positioning accuracy and GPS acquisition time, said wireless communications network being different from said GPS.

13. The mobile device according to claim 12, wherein the current radio position and error are is determined using Cramer-Rao Bounds.

14. The mobile device according to claim 12, wherein the period of the power-up phase is set while retaining the accuracy of a position fix and acquisition times of the position fix within acceptable statistical bounds.

15. The mobile device according to claim 12, wherein said mobile device determines dynamically an optimal period of the power-up phase.

16. The mobile device according to claim 12, wherein said current value of said period of the power-up phase is set using adaptive-filter and machine-learning techniques.

17. The mobile device according to claim 16, wherein said adaptive-filter and machine-learning techniques are implemented using said mobile device.

18. The mobile device according to claim 12, further comprising:

means for, if at least one of position error and acquisition times of GPS fail to meet acceptable bounds, reducing said value of said period of the power-up phase; and

means for, if at least one of said position error and acquisition times of GPS meet said acceptable bounds, increasing said value of said period of the power-up phase.

19. The mobile device according to claim 12, wherein said mobile device comprises a power limited device.

20. The mobile device according to claim 19, further comprising a battery for powering said mobile device.

21. The mobile device according to claim 12, further comprising a neural network module for making adaptive predictions.

22. The mobile device according to claim 12, wherein said setting means comprises:

means for predicting a next position at a next scheduled power-up time using a number of last positions reported of said mobile device in the wireless network determined from the wireless network;

means for comparing said predicted position at said next power-up time with an actual position of said mobile device in the wireless network determined from the wireless network and determining said position error; and

means for storing current values of a period of the power-up phase, acquisition time, said position error, and position in a storage unit of said mobile device.

38. The mobile device according to claim 12, further comprising means for setting a current value of the period of the power-up phase of said GPS dependent upon adaptive predictions of when said GPS should be powered on to meet specifications on positioning accuracy or GPS acquisition time.

23. A computer program product for a mobile wireless communication device for a wireless communication network, said mobile wireless communication device possessing wireless communications capability for said wireless communications network and an embedded GPS Global Positioning System (GPS) module for GPS capability, said computer program product having a computer readable medium storing a computer program for acquiring GPS position on said mobile wireless communication device, comprising:

computer program code means for interfacing with the GPS capability of said mobile wireless communication device; and

computer program code means for setting a current value of the period of the power-up phase of said GPS adaptively predicting when said GPS module should be powered on to meet specifications on at least one of positioning accuracy and GPS acquisition time said adaptively predicting when said GPS module should be powered on being dependent on a current radio position and error of said mobile wireless communication device in the wireless network, said position error being determined from the wireless communications network and adaptive predictions of when said GPS should be powered on to meet specifications on positioning accuracy and GPS acquisition time, said wireless communications network being different from said GPS.

24. The computer program product according to claim 23, wherein the current radio position and error are is determined using Cramer-Rao Bounds.

25. The computer program product according to claim 23, wherein the period of the power-up phase is set while retaining the accuracy of a position fix and acquisition times of the position fix within acceptable statistical bounds.

26. The computer program product according to claim 23, wherein said computer program product determines dynamically an optimal period of the power-up phase.

27. The computer program product according to claim 23, wherein said current value of said period of the power-up phase is set using adaptive-filter and machine-learning techniques.

28. The computer program product according to claim 27, wherein said adaptive-filter and machine-learning techniques are implemented using said mobile device.

29. The computer program product according to claim 23, further comprising:

computer program code means for, if at least one of said position error and acquisition times of GPS fail to meet acceptable bounds, reducing said value of said period of the power-up phase; and

computer program code means for, if at least one of said position error and acquisition times of GPS meet said acceptable bounds, increasing said value of said period of the power-up phase.

30. The computer program product according to claim 23, wherein said mobile device comprises a power limited device.

31. The computer program product according to claim 30, wherein said mobile device comprises a battery for powering said mobile device.

32. The computer program product according to claim 23, further comprising computer program code means for providing a neural network module to make adaptive predictions.

33. The computer program product according to claim 23, wherein said computer program code means for setting comprises:

computer program code means for predicting at a next position a next scheduled power-up time using a number of last positions reported of said mobile device in the wireless network determined from the wireless network;

computer program code means for comparing said predicted position at said next power-up time with an actual position of said mobile device in the wireless network determined from the wireless network and determining said position error; and

computer program code means for storing current values of a period of the power-up phase, acquisition time, said position error, and position in a storage unit of said mobile device.

39. The computer program product according to claim 23, further comprising computer program code means for setting a current value of the period of the power-up phase of said GPS dependent upon adaptive predictions of when said GPS should be powered on to meet specifications on positioning accuracy or GPS acquisition time.

34. A method of acquiring GPS position on a mobile device for a wireless network, said mobile device possessing wireless communications capability for said wireless network and GPS having a Global Positioning System (GPS) module possessing GPS capability, said method comprising the steps of:

usingdetermining a position error of said mobile device in the wireless network, the position error being determined from the wireless network, said wireless communications network being different from said GPS;

ifupon being determined that the position error is too large, adjusting a current value of a period of a power-up phase of said GPS module downward;

ifupon being determined that the position error is too small, adjusting the current value of said period of the power-up phase of said GPS module upward;

estimating using a neural network a new value of said period of the power-up phase of said GPS module dependent upon said position error; and

setting a the new value of said period of the power-up phase of said GPS to a weighted value dependent upon an acceptable error bound.

36. The method according to claim 34, wherein the position error is the Cramer-Rao Bound on the radio position of the mobile device.

35. A method of acquiring GPS position on a mobile device for a wireless network, said mobile device possessing wireless communications capability for said wireless network and GPS having a Global Positioning System (GPS) module processing GPS capability, said method comprising the steps of:

usingdetermining an acquisition time at a last position fix of the mobile device in the wireless network, the last position fix being determined from the wireless network, said wireless communications network being different from said GPS;

ifupon being determined that the acquisition time is too large, adjusting a current value of a period of a power-up phase of said GPS module downward;

ifupon being determined that the acquisition time is too small, adjusting the current value of said period of the power-up phase of said GPS module upward;

estimating using a neural network a new value of said period of the power-up phase of said GPS module dependent upon said acquisition time; and

setting a the new value of said period of the power-up phase of said GPS module to a weighted value dependent upon an acceptable bound.