COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR LOCATING AN INDOOR OBJECT

• US 20100295733A1
• Filed: 07/23/2009
• Published: 11/25/2010
• Est. Priority Date: 05/19/2009
• Status: Active Grant

• Current Assignee: National Taiwan University (Government of The Republic of China)
• Original Assignee: National Taiwan University (Government of The Republic of China)
• Inventors: LUO, Ren-Chyuan, CHEN, Chien-Chung
• Granted Patent: US 8,325,090 B2
• Time in Patent Office: Days
• US Class Current: 342/451
• CPC Class Codes: G01S 13/825 with exchange of information between interrogator and responder
• CPC Class Codes: G01S 13/86 Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
• CPC Class Codes: G01S 13/878 Combination of several spaced transmitters or receivers of known location for determining the position of a transponder or a reflector G01S13/874 takes precedence
• CPC Class Codes: G01S 5/0264 at least one of the systems being a non-radio wave positioning system

Abstract

A computer-implemented method for locating an indoor object includes: a) receiving an RF coordinate signal and a detecting signal; b) providing information of a located region with reference to the RF coordinate and the detecting signal; c) obtaining a motion sensor coordinate according to the information of the located region; d) providing weights for the RF coordinate and the motion sensor coordinate; e) applying the weights to the RF coordinate and the motion sensor coordinate; and f) combining the weighted RF coordinate and the weighted motion sensor coordinate to generate a fused coordinate corresponding to the position of the indoor object. A system for locating an indoor object is also disclosed.

Claims

1. A computer-implemented method for locating an indoor object based on a radio frequency (RF) coordinate signal from an RF coordinate estimation unit, and at least one detecting signal from a motion sensor array upon detection of movement of the indoor object, the RF coordinate signal carrying information of an RF coordinate on a coordinate plane estimating a position of the indoor object, said method comprising:

a) receiving the RF coordinate signal and the detecting signal;

b) providing information of a located region on the coordinate plane with reference to the RF coordinate and the detecting signal;

c) obtaining a motion sensor coordinate on the coordinate plane according to the information of the located region;

d) providing weights for the RF coordinate and the motion sensor coordinate;

e) applying the weights to the RF coordinate and the motion sensor coordinate, respectively; and

f) combining the weighted RF coordinate and the weighted motion sensor coordinate to generate a fused coordinate on the coordinate plane corresponding to the position of the indoor object.

2. The method of claim 1, wherein when only one detecting signal is received in step a), step b) is performed by providing information of a predetermined sensing area of the motion sensor array on the coordinate plane in which the RF coordinate falls, followed by defining the predetermined sensing area as the located region.

3. The method of claim 2, wherein the motion sensor coordinate is a geometric center of the predetermined sensing area.

4. The method of claim 1, wherein when more than one detecting signals are received in step a), step b) is performed by the sub-steps of:

b-1) providing information of a plurality of pre-determined sensing areas of the motion sensor array on coordinate plane, such that at least two of the pre-determined sensing areas overlap each other so as to divide the pre-determined sensing areas into an overlapped region and non-overlapped regions; and

b-2) determining one of the non-overlapped regions and the overlapped region, in which the RF coordinate falls, as the located region.

5. The method of claim 4, wherein the motion sensor coordinate is a geometric center of one of the predetermined sensing areas corresponding to one of the non-overlapped regions when the RF coordinate falls in said one of the non-overlapped regions, and is a geometric center of the overlapped region when the RF coordinate falls in the overlapped region.

6. The method of claim 1, wherein, in step d), a sum of the weights is equal to 1, and in step f), the weighted RF coordinate and the weighted sensor coordinate are summed to generate the fused coordinate.

7. The method of claim 1, wherein step d) adopts a Covariance Intersection estimator for offline training and generating the weights for the RF coordinate and the motion sensor coordinate.

8. The method of claim 1, wherein step d) includes the sub-steps of:

d-1) providing a plurality of actual coordinates corresponding to a plurality of training patterns, respectively;

d-2) providing a plurality of RF training coordinates corresponding to the training patterns, respectively;

d-3) computing an RF coordinate covariance matrix according to the actual coordinates and the RF training coordinates corresponding to the training patterns;

d-4) providing a plurality of motion sensor training coordinates corresponding to the training patterns, respectively;

d-5) computing a motion sensor coordinate covariance matrix according to the actual coordinates and the motion sensor training coordinates corresponding to the training patterns; and

d-6) generating the weights with a Covariance Intersection estimator according to the RF coordinate covariance matrix and the motion sensor coordinate covariance matrix.

9. The method of claim 8, wherein sub-step d-6) includes the sub-steps of:

d-6-1) giving a training weight W;

d-6-2) computing a fused coordinate covariance matrix with the Covariance Intersection estimator according to the training weight W, the RF coordinate covariance matrix, and the motion sensor coordinate covariance matrix;

d-6-3) performing d-6-1) and d-6-2) iteratively until a minimum trace value for the fused coordinate covariance matrix is found; and

d-6-4) taking the last given training weight W and (1-W) as the weights when the minimum trace value of the fused coordinate covariance matrix is found.

10. The method of claim 8, wherein sub-step d-6) includes the sub-steps of:

d-6-1) giving a training weight W;

d-6-2) computing a fused coordinate covariance matrix with the Covariance Intersection estimator according to the training weight W, the RF coordinate covariance matrix, and the motion sensor coordinate covariance matrix;

d-6-3) performing d-6-1) and d-6-2) iteratively until a minimum determinant value for the fused coordinate covariance matrix is found; and

d-6-4) taking the last given training weight W and (1-W) as the weights when the minimum determinant value of the fused coordinate covariance matrix is found.

11. A system for locating an indoor object, comprising:

a radio frequency (RF) coordinate estimation unit configured to output an RF coordinate signal carrying information of an RF coordinate on a coordinate plane estimating a position of the indoor object;

a motion sensor coordinate estimation unit including a motion sensor array and a localization module, said motion sensor array including at least one motion sensor configured to output a detecting signal upon detection of movement of the indoor object, said localization module being configured to receive the RF coordinate signal and the detecting signal, to provide information of a located region on the coordinate plane with reference to the RF coordinate and the detecting signal, and to obtain a motion sensor coordinate on the coordinate plane according to the information of the located region; and

a fusion unit including a weight generation module and a fused coordinate calculation module, said weight generation module being configured to provide weights for the RF coordinate and the motion sensor coordinate, said fused coordinate calculation module being configured to receive the RF coordinate and the motion sensor coordinate, to apply the weights to the RF coordinate and the motion sensor coordinate, respectively, and to combine the weighted RF coordinate and the weighted motion sensor coordinate to generate a fused coordinate on the coordinate plane corresponding to the position of the indoor object.

12. The system of claim 11, wherein said motion sensor array of said motion sensor coordinate estimation unit includes a motion sensor, said localization module being configured to provide information of a pre-determined sensing area of the motion sensor array on the coordinate plane in which the RF coordinate falls, and to define the pre-determined sensing area as the located region.

13. The system of claim 12, wherein the motion sensor coordinate obtained from said localization module according to the information of the located region is a geometric center of the pre-determined sensing area.

14. The system of claim 11, wherein said motion sensor array of said motion sensor coordinate estimation unit includes a plurality of motion sensors, said localization module of said motion sensor coordinate estimation unit being configured to provide information of a plurality of pre-determined sensing areas of the motion sensor array on the coordinate plane in response to the detecting signals generated from said motion sensors, such that at least two of the pre-determined sensing areas overlap each other so as to divide the pre-determined sensing areas into an overlapped region and non-overlapped regions, said localization module being further configured to determine one of the non-overlapped regions and the overlapped region, in which the RF coordinate falls, as the located region.

15. The system of claim 14, wherein said localization module of said motion sensor coordinate estimation unit is further configured in such a manner that when the RF coordinate falls in one of the non-overlapped regions, the motion sensor coordinate obtained from said localization module according to the information of the located region is a geometric center of one of the predetermined sensing areas corresponding to said one of the non-overlapped regions, and is a geometric center of the overlapped region when the RF coordinate falls in the overlapped region.

16. The system of claim 11, wherein a sum of the weights provided by said weight generation module of said fusion unit is equal to 1, and said fused coordinate calculation module is configured to sum the weighted RF coordinate and the weighted motion sensor coordinate to generate the fused coordinate.

17. The system of claim 11, wherein said weight generation module of said fusion unit adopts a Covariance Intersection estimator for offline training and generating the weights for the RF coordinate and the motion sensor coordinate.

18. The system of claim 11, wherein said weight generation module of said fusion unit is configured to compute an RF coordinate covariance matrix according to a plurality of actual coordinates corresponding to a plurality of training patterns, respectively, and a plurality of RF training coordinates corresponding to the training patterns, respectively, further to compute a motion sensor coordinate covariance matrix according to the actual coordinates and a plurality of motion sensor training coordinates corresponding to the training patterns, respectively, and to generate the weights with a Covariance Intersection estimator according to the RF coordinate covariance matrix and the motion sensor coordinate covariance matrix.

19. The system of claim 11, wherein said RF coordinate estimation unit includes at least three reference node RF transceivers, a blind node RF transceiver, and a localization module, each of said reference node RF transceivers corresponding to a pre-determined reference node coordinate on the coordinate plane and being placed at a pre-determined position, said blind node RF transceiver being carried by the indoor object, said localization module being configured to receive Received Signal Strength Index (RSSI) values related respectively to the distances between said at least three of said reference node RF transceivers and said blind node RF transceiver from said blind node RF transceiver, and to generate the RF coordinate on the coordinate plane according to the pre-determined reference node coordinates on the coordinate plane and the RSSI values.