Method and apparatus for enhanced pedestrian navigation based on WLAN and MEMS sensors

• US 10,190,881 B2
• Filed: 01/08/2016
• Issued: 01/29/2019
• Est. Priority Date: 01/08/2015
• Status: Active Grant

• Current Assignee: Profound Positioning, Inc.
• Original Assignee: Profound Positioning, Inc.
• Inventors: Yuan, Zhuang, El-Sheimy, Naser
• Primary Examiner(s): Cass, Jean Paul
• Application Number:
  US14/991,854
• Publication Number:
  US 20170227363A1
• Time in Patent Office: 1,117 Days
• Field of Search: None
• CPC Class Codes: G01C 21/165 combined with non-inertial navigation instruments
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G01C 22/006 Pedometers
• CPC Class Codes: H04B 17/318 Received signal strength
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information
• CPC Class Codes: H04W 84/12 WLAN [Wireless Local Area Networks]

Abstract

Methods, systems and apparatuses for enhanced Microelectromechanical (MEMS)-based navigation in a mobile device are disclosed. In an embodiment, a method includes receiving navigation data from one or more navigation sensors on board the mobile device. The method may also include calculating, using a processing device, position, velocity, and attitude (PVA) values in response to the navigation data using an Inertial Navigation System (INS) mechanization. Additionally, the method may include calculating, using the processing device, Pedestrian Dead Reckoning (PDR) values in response to the navigation data. Also, the method may include determining, using the processing device, one or more navigation values in response to a combination of the PVA values calculated by the INS mechanization and the PDR values.

Claims

1. A method for enhanced Microelectromechanical (MEMS)-based navigation in a mobile device, comprising:

receiving navigation data from one or more MEMS-based navigation sensors on board the mobile device;

calculating, using a processing device coupled to the one or more MEMS-based navigation sensors, position, velocity, and attitude (PVA) values in response to the navigation data using an Inertial Navigation System (INS) mechanization;

calculating, using the processing device, Pedestrian Dead Reckoning (PDR) values in response to the navigation data received from the one or more MEMS-based navigation sensors;

calculating ranges between one or more Wireless Local Area Network (WLAN) access points and the mobile device or a WLAN position solution for the mobile device; and

determining, using the processing device, one or more navigation values in response to a combination of the PVA values calculated by the INS mechanization and the PDR values, in combination with at least one of the WLAN range or WLAN position solution;

wherein the one or more navigation values calculated in response to the combination of PVA values, PDR values and the WLAN range or the WLAN position solution are more accurate than a navigation value calculated in response to any one of the PVA values, PDR values, WLAN range or WLAN position solution alone.

2. The method of claim 1, wherein the method further comprises detecting whether the mobile device is moving or static and using motion constraints on the solution if the mobile is moving.

3. The method of claim 2, further comprising calculating the navigation data using a Zero velocity updates (ZUPT) algorithm and a Zero Angular Rate Updates (ZARU) algorithm if the mobile device is static.

4. The method of claim 1, further comprising:

calculating a set of WLAN positioning solutions and estimated accuracies by trilateration;

selecting a solution from the set of solutions in response to the estimated accuracies; and

estimating a position and attitude of the mobile device by a loosely-coupled integration of the navigation values and the selected WLAN positioning solution.

5. The method of claim 1, further comprising:

calculating the WLAN positioning solution according to a fingerprinting process; and

estimating the position and attitude of the mobile device by the loosely-coupled integration of the navigation values and the WLAN positioning solution.

6. The method of claim 1, further comprising:

determining MEMS sensors based ranges between the mobile device and a plurality of WLAN access points;

determining WLAN based ranges between the mobile device and WLAN access points according to an Received Signal Strength (RSS) bias value that is automatically estimated in an Extended Kalman Filter (EKF); and

estimating the position of the mobile device by tightly-coupled integration of the MEMS sensors based ranges and WLAN based ranges.

7. A system for enhanced Microelectromechanical (MEMS)-based navigation in a mobile device, comprising:

one or more MEMS-based navigation sensors on board a mobile device configured to generate navigation data;

a wireless local area network (WLAN) receiver configured to receive one or more WLAN signals; and

a processing device coupled to the one or more MEMS-based navigation sensors and the WLAN receiver, the processing device configured to;

calculate position, velocity, and attitude (PVA) values in response to the navigation data using an Inertial Navigation System (INS) mechanization;

calculate Pedestrian Dead Reckoning (PDR) values in response to the navigation data generated by the one or more MEMS-based navigation sensors;

calculate ranges between one or more WLAN access points and the mobile device or a WLAN position solution for the mobile device; and

determine one or more navigation values in response to a combination of the PVA values calculated by the INS mechanization and the PDR values in combination with at least one of the WLAN range or WLAN position solution;

wherein the one or more navigation values calculated in response to the combination of PVA values, PDR values and either the WLAN range or the WLAN position solution are more accurate than a navigation value calculated in response to any one of the PVA values, PDR values, WLAN range or WLAN position solution alone.

8. The system of claim 7, wherein the processing device is further configured to detect whether the mobile device is moving or static and using motion constraints on the solution if the mobile is moving.

9. The system of claim 8, wherein the processing device is further configured to calculate the navigation data using a Zero velocity updates (ZUPT) algorithm and a Zero Angular Rate Updates (ZARU) algorithm if the mobile device is static.

10. The system of claim 7, wherein the processing device is further configured to:

calculate a set of WLAN positioning solutions and estimated accuracies by trilateration;

select a solution from the set of solutions in response to the estimated accuracies; and

estimate a position and attitude of the mobile device by a loosely-coupled integration of the navigation values and the selected WLAN positioning solution.

11. The system of claim 7, wherein the processing device is further configured to:

calculate the WLAN positioning solution according to a fingerprinting process; and

estimate the position and attitude of the mobile device by the loosely-coupled integration of the navigation values and the WLAN positioning solution.

12. The system of claim 7, wherein the processing device is further configured to:

determine MEMS sensors based ranges between the mobile device and a plurality of WLAN access points;

determine WLAN based ranges between the mobile device and WLAN access points according to an RSS bias value that is automatically estimated in an Extended Kalman Filter (EKF); and

estimate the position of the mobile device by tightly-coupled integration of the MEMS sensors based ranges and WLAN based ranges.

13. An apparatus for enhanced Microelectromechanical (MEMS)-based navigation in a mobile device, comprising:

an Inertial Navigation System (INS) mechanization, coupled to the one or more MEMS-based navigation sensors onboard the mobile device, and configured to calculate position, velocity, and attitude (PVA) values in response to navigation data received from one or more MEMS-based navigation sensors;

a PDR processing element configured to calculate Pedestrian Dead Reckoning (PDR) values in response to the navigation data received from the one or more MEMS-based navigation sensors;

a Wireless Local Area Network (WLAN) unit configured to calculate ranges between one or more WLAN access points and the mobile device or a WLAN position solution for the mobile device; and

a navigation processing element configured to determine one or more navigation values in response to a combination of the PVA values calculated by the INS mechanization and the PDR values in combination with at least one of the WLAN range or WLAN position solution;

wherein the one or more navigation values calculated in response to the combination of PVA values, PDR values and either the WLAN range or the WLAN position solution are more accurate than a navigation value calculated in response to any one of the PVA values, PDR values, WLAN range, or WLAN position solution alone.

14. The apparatus of claim 13, further configured to detect whether the mobile device is moving or static and using motion constraints on the solution if the mobile device is moving.

15. The apparatus of claim 14, further configured to calculate the navigation data using a Zero velocity updates (ZUPT) algorithm and a Zero Angular Rate Updates (ZARU) algorithm if the mobile device is static.

16. The apparatus of claim 13, further configured to:

calculate a set of WLAN positioning solutions and estimated accuracies by trilateration;

select a solution from the set of solutions in response to the estimated accuracies; and

estimate a position and attitude of the mobile device by a loosely-coupled integration of the navigation values and the selected WLAN positioning solution.

17. The apparatus of claim 13, further configured to:

calculate the WLAN positioning solution according to a fingerprinting process; and

estimate the position and attitude of the mobile device by the loosely-coupled integration of the navigation values and the WLAN positioning solution.

18. The apparatus of claim 13, further configured to:

determine MEMS sensors based ranges between the mobile device and a plurality of WLAN access points;

determine WLAN based ranges between the mobile device and WLAN access points according to an RSS bias value that is automatically estimated in an Extended Kalman Filter (EKF); and

estimate the position of the mobile device by tightly-coupled integration of the MEMS sensors based ranges and WLAN based ranges.