GPS AND WLAN HYBRID POSITION DETERMINATION

• US 20140335887A1
• Filed: 04/15/2014
• Published: 11/13/2014
• Est. Priority Date: 05/09/2013
• Status: Active Grant

• Current Assignee: Marvell Asia Pte Limited (Marvell Technology Group Limited)
• Original Assignee: Marvell World Trade Limited (Marvell Technology Group Limited)
• Inventors: Liu, BoChih, Jia, Zhike, Chen, Jian, Yu, Jing, Zhou, Fagen
• Granted Patent: US 9,642,110 B2
• Time in Patent Office: Days
• US Class Current: 455/456.1
• CPC Class Codes: G01S 19/48 by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
• CPC Class Codes: G01S 5/012 Identifying whether indoors or outdoors
• CPC Class Codes: G01S 5/017 Detecting state or type of motion
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

The present disclosure provides a method for determining a real-time position of a mobile device using a global positioning system (GPS) and wireless local area network (WLAN) hybrid positioning mode. The method includes determining, by a mobile device, the mobile device'"'"'s environment area based on GPS satellite signals; determining an absolute position of the mobile device based on the GPS satellite signals while the mobile device is moving from an outdoor area to an indoor area; estimating a reference position of the mobile device based on the determined absolute position and WLAN access point signals while moving to a shadow area; and while moving to an indoor area, determining an estimated position of the mobile device based on calculated regressive coefficients and the WLAN access point signals, and updating the estimated position of the mobile device based on motion information and indoor floor map information.

Claims

1. A mobile device comprising:

a GPS receiver that receives GPS satellite signals;

a WLAN transceiver that measures WLAN access point signals; and

an environment profile sensor that calculates a Poor-to-Total Satellites Ratio (PTSR) to determine an environment area in which the mobile device is located, the environment profile sensor to;

register an outdoor area if the calculated PTSR is below a lower threshold;

register a shadow area if the calculated PTSR is between the lower threshold and an upper threshold;

register an indoor area if the calculated PTSR is over the upper threshold.

2. The mobile device of claim 1, wherein the PTSR is defined as a ratio of a total number of captured satellites and a number of captured satellites with a carrier-to-noise density below a threshold.

3. The mobile device of claim 1, further comprising a GPS position manager for determining an absolute position of the mobile device based on GPS satellite signals as the mobile device moves from the outdoor area to the indoor area.

4. The mobile device of claim 1, further comprising a WLAN position manager that estimates a reference position of the mobile device based on an absolute position and received WLAN access point signals as the mobile device moves to the shadow area.

5. The mobile device of claim 4, wherein a weight of the absolute position is determined by strengths of received WLAN access point signals.

6. The mobile device of claim 1, further comprising a WLAN position manager including

a coefficient calibration that calculates regressive coefficients of a linear regression formula based on an absolute position and an estimated reference position;

a regressive position estimation unit that determines an estimated position of the mobile device based on the calculated regressive coefficients and received WLAN access point signals; and

a cooperative position estimation that updates the estimated position of the mobile device based on motion information provided by a microelectromechanical systems (MEMS) sensor register and indoor floor map information provided by a map database as the mobile device moves to an indoor area.

7. The mobile device of claim 6, wherein the coefficient calibration unit uses a least squares algorithm to calculate the regressive coefficients and iteratively uses a gradient decent method to optimize the regressive coefficients.

8. The mobile device of claim 6, wherein the regressive position estimation unit uses a linear regression formula to determine the estimated position of the mobile device.

9. The mobile device of claim 6, wherein the cooperative position estimation unit uses a point to link matching method for applying the indoor floor map information to update the estimated position of the mobile device.

10. The mobile device of claim 1, further comprising a MEMS sensor register that receives acceleration magnitude information from an accelerometer and gyroscope information from a gyroscope.

11. The mobile device of claim 10, wherein the acceleration magnitude information is used to detect a mode of the mobile device.

12. The mobile device of claim 10, wherein the gyroscope information is used to detect orientation of movement of the mobile device.

13. The mobile device of claim 1, further comprising a map database for storing indoor floor map data associated with structures of link, node, and region.

14. The mobile device of claim 1, further comprising a database manager that collects position data, each of the position data includes an updated position associated with signal information from a WLAN transceiver or signal information from a wireless radio transceiver.

15. The mobile device of claim 14, wherein the wireless radio transceiver comprises one of a cellular transceiver, Bluetooth transceiver, or Zigbee transceiver.

16. The mobile device of claim 1, further comprising a position database for storing one or more position data for use in determining a position.

17. The mobile device of claim 1, further comprising a network manager that establishes a network connection for transmitting position data to a positioning server or for receiving map data from a map server.

18. A method comprising:

calculating, by an environment profile sensor, a Poor-to-Total Satellites Ratio (PTSR) based on GPS satellite signals received by a mobile device;

determining that the mobile device is in an outdoor area if the calculated PTSR is below a lower threshold;

determining that the mobile device is in a shadow area if the calculated PTSR is between the lower threshold and an upper threshold; and

determining that the mobile device is in an indoor area if the calculated PTSR is above the upper threshold.

19. The method of claim 18, wherein the PTSR is defined as a ratio of a total number of captured satellites and a number of captured satellites with a carrier-to-noise density below a threshold.

20. A method comprising

determining, by a GPS position manager executed by one or more processors, an absolute position of a mobile device based on GPS satellite signals received by the mobile device as the mobile device moves from an outdoor area to an indoor area.

21. The method of claim 20, further comprising estimating a reference position of the mobile device based on the determined absolute position and WLAN access point signals received by the mobile device as the mobile device moves to a shadow area.

22. The method of claim 21, wherein a weight of the absolute position is determined based on strengths of received WLAN access point signals.

23. The method of claim 20, further comprising

calculating regressive coefficients of a linear regression formula based on the determined absolute position and an estimated reference position;

determining an estimated position of the mobile device based on the calculated regressive coefficients and WLAN access point signals received by the mobile device; and

updating the estimated position of the mobile device based on motion information provided by a MEMS sensor register and indoor floor map information provided by a map database as the mobile device moves to an indoor area.

24. The method of claim 23, wherein the regressive coefficients are calculated using a least squares algorithm and are iteratively optimized using a gradient decent method.

25. The method of claim 23, wherein the estimated position of the mobile device is determined using a linear regression formula.

26. The method of claim 23, wherein the estimated position of the mobile device is updated using a point to link matching method for applying the map data.

27. The method of claim 20, further comprising receiving acceleration magnitude from an accelerometer and gyroscope information from a gyroscope.

28. The method of claim 27, wherein the acceleration magnitude is used to detect of a mode of the mobile device.

29. The method of claim 27, wherein the gyroscope information is used to detect of orientation of movement of the mobile device.

30. The method of claim 20, further comprising storing indoor floor map data, the indoor floor map data being associated with structures of link, node, and region.

31. The method of claim 20, further comprising collecting position data, the position data includes an updated position associated with signal information from a WLAN transceiver or signal information from a wireless radio transceiver.

32. The method of claim 31, wherein the wireless radio transceiver comprises one of a cellular transceiver, a Bluetooth transceiver, or a Zigbee transceiver.

33. The method of claim 20, further comprising storing position data for use in determining a position of the mobile device.

34. The method of claim 20, further comprising establishing a network connection for transmitting position data to a positioning server, or for receiving map data from a map server.

35. A method comprising

determining a position of a mobile device by accessing a position database that is hosted locally by or remotely from the mobile device as the mobile device initiates location-based tasks while within an indoor area.