Method and system for cooperative stochastic positioning in a mobile environment

• US 9,219,985 B2
• Filed: 07/27/2010
• Issued: 12/22/2015
• Est. Priority Date: 07/27/2010
• Status: Active Grant

• Current Assignee: University of Regina
• Original Assignee: University of Regina
• Inventors: Morgan, Yasser
• Primary Examiner(s): Siddiqui, Kashif
• Application Number:
  US13/812,739
• Publication Number:
  US 20130178231A1
• Time in Patent Office: 1,974 Days
• US Class Current: 1/1
• CPC Class Codes: G01S 19/51 Relative positioning
• CPC Class Codes: G01S 5/0278 involving statistical or probabilistic considerations G01S5/0252, G01S5/0294 take precedence
• CPC Class Codes: G01S 5/0284 Relative positioning
• CPC Class Codes: G01S 5/0289 of multiple transceivers, e.g. in ad hoc networks
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds

Abstract

Cooperative stochastic positioning in a mobile environment is provided. Position announcements transmitted wirelessly from objects in the immediate area are received at an object of interest or primary object. The position announcements provide the current position data of the respective object in relation to a common coordinate system. The received position announcements are discretizing to obtain a plurality of data groupings based upon a clustering criteria applied to the received position data. Clustering of the data groupings is performed to determine which clusters dataset from the data groupings provide sufficient and consistent position accuracy to determine a relative position of the primary object. A stochastic automata model is then applied to selected cluster dataset to evaluate relative cluster weights in order to determine the relative position of the object of interest. The accuracy of a current position of the object of interest can then be updated based upon determined relative position.

Claims

1. A method for cooperative stochastic positioning in a mobile environment executed by a processor in an object of interest, the method comprising:

receiving position announcements transmitted wirelessly from a plurality of objects, the position announcements providing the current position data of the respective object in relation to a common coordinate system;

discretizing the received position announcements to obtain a plurality of data groupings based upon a clustering criteria applied to the received position data;

performing clustering of the data groupings to determine which cluster datasets from the data groupings provide sufficient and consistent position accuracy to determine a relative position of the object of interest;

applying stochastic automata model to selected cluster dataset to evaluate relative cluster weights in order to determine the relative position of the object of interest; and

updating accuracy of a current position of the object of interest based upon determined relative position.

2. The method of claim 1 wherein discretizing the received position data further comprises applying a channel propagation model on the received object position data to determine range data associated with the respective received object position data.

3. The method of claim 1 wherein discretizing the received position data further comprises receiving range data associated with the received object position data associated with the respective received object position data.

4. The method of claim 1 wherein the performing clustering further comprises:

applying classifiers to the determined clusters based upon selected criteria;

determining if the selected cluster dataset provides object position data accuracy within a defined position tolerance; and

dividing the cluster dataset if the determined accuracy is less than the defined tolerances.

5. The method of claim 1 wherein the performing clustering further comprises:

selecting clustering criteria to be applied to the received positioning announcements;

selecting a cluster dataset from the data groupings;

minimizing error of the selected cluster dataset using selected criteria;

determining average normalized intergroup scatter between position data provided in the cluster dataset;

determining a normalized scatter matrix of the position data provided in the cluster dataset;

determining if criteria should be adjusted based upon normalized scatter matrix;

determining if cluster dataset should be combined with previously analysed cluster based upon a defined criteria accuracy;

determining if the cluster dataset provides object position data accuracy within defined position tolerances; and

dividing the cluster dataset if the determined accuracy is less than the defined tolerances to perform clustering on each of the divided clusters.

6. The method of claim 1 wherein applying stochastic automata model to selected clusters to determine the relative position further comprises:

calculating a weighted multi-lateration position using the provided cluster datasets;

applying weighted ranges and a set of multi-lateration to determine an accurate position from the position data in the cluster dataset;

validate accuracy of obtained position relative to current object of interest position wherein least weighted data is removed from the dataset when dataset positions accuracy is less than required accuracy threshold and current position data is updated with obtained position.

7. The method of claim 1 wherein the clustering criteria is a function of position announcement data properties either determinable by the received announcement data or associated with previously determined characteristics associated with the object providing the position announcement.

8. The method of claim 7 wherein the clustering criteria is selected from the group comprising:

least-mean-square (LMS), arrival delay and position announcement data confidence.

9. The method of claim 1 further comprising receiving positioning information at the object of interest from a global positioning system (GPS) and wherein receiving the positioning announcements transmitted from the plurality of objects is via a short range inter-object communication system and the relative object positioning is determined relative to a fixed coordinate system.

10. The method of claim 9 wherein the wireless technology is selected from the group comprising:

Bluetooth, Zigbee, IEEE 802.11, 802.16, and cellular based wireless communication technologies.

11. A system for cooperative stochastic positioning in a mobile environment the system comprising:

a first receiver for receiving positioning announcements from a plurality of objects, the position announcements providing the current position data of the respective object in relation to a common coordinate system;

processor coupled to the first receiver; and

a memory comprising instructions for execution by the processor, the instructions comprising;

discretizing the received position announcements to obtain a plurality of data groupings based upon a clustering criteria applied to the received position data;

performing clustering of the data groupings to determine which cluster datasets from the data groupings provide sufficient and consistent position accuracy to determine a relative position of the object of interest;

applying stochastic automata model to selected cluster dataset to evaluate relative cluster weights in order to determine the relative position of an object of interest; and

updating accuracy of a current position of the object of interest based upon determined relative position.

12. The system of claim 11 further comprising a second receiver for receiving positioning data from a global navigation satellite system for determining a position of the object of interest relative to a defined global coordinate system for the current position.

13. The system of claim 12 wherein the first receiver is a short range wireless communication system for communication between objects in an immediate are near the object of interest.

14. The system of claim 13 wherein the short range wireless communication system is a dedicated short-range communications (DSRC) for vehicular applications.

15. The system of claim 11 further wherein receiving the positioning announcements transmitted from the plurality of objects is a dedicated short range wireless technology and the relative object positioning is determined relative to a fixed coordinate system.

16. The system of claim 15 wherein the wireless technology is selected from the group comprising:

Bluetooth, Zigbee, IEEE 802.11, 802.16, and cellular based wireless communication technologies.

17. The system of claim 11 wherein discretizing the received position data further comprises applying a channel propagation model on the received object position data to determine range data associated with the respective received object position data.

18. The system of claim 11 wherein discretizing the received position data further comprises receiving range data associated with the received object position data associated with the respective received object position data.

19. The system of claim 11 wherein the performing clustering further comprises:

applying classifiers to the determined clusters based upon selected criteria;

determining if the selected cluster dataset provides object position data accuracy within a defined position tolerance; and

dividing the cluster dataset if the determined accuracy is less than the defined tolerances.

20. The system of claim 11 wherein the performing clustering further comprises:

selecting clustering criteria to be applied to the received positioning announcements;

selecting a cluster dataset from the data groupings;

minimizing error of the selected cluster dataset using selected criteria;

determining average normalized intergroup scatter between position data provided in the cluster dataset;

determining a normalized scatter matrix of the position data provided in the cluster dataset;

determining if criteria should be adjusted based upon normalized scatter matrix;

determining if cluster dataset should be combined with previously analysed cluster based upon a defined criteria accuracy;

determining if the cluster dataset provides object position data accuracy within defined position tolerances; and

dividing the cluster dataset if the determined accuracy is less than the defined tolerances to perform clustering on each of the divided clusters.

21. The system of claim 11 wherein applying stochastic automata model to selected clusters to determine the relative position further comprises:

calculating a weighted multi-lateration position using the provided cluster datasets;

applying weighted ranges and a set of multi-lateration to determine an accurate position from the position data in the cluster dataset;

validate accuracy of obtained position relative to current object of interest position wherein least weighted data is removed from the dataset when dataset positions accuracy is less than required accuracy threshold and current position data is updated with obtained position.

22. The system of claim 11 wherein the clustering criteria is a function of position announcement data properties either determinable by the received announcement data or associated with previously determined characteristics associated with the object providing the position announcement.

23. The system of claim 22 wherein the clustering criteria is selected from the group comprising:

least-mean-square (LMS), arrival delay and position announcement data confidence.

24. A computer readable memory device providing instructions for performing cooperative stochastic positioning in a mobile environment, that when executed by a processor in an object of interest performing the method comprising:

receiving wirelessly, position announcements transmitted from a plurality of objects, the position announcements providing the current position data of the respective object in relation to a common coordinate system;

discretizing the received position announcements to obtain a plurality of data groupings based upon a clustering criteria applied to the received position data;

performing clustering of the data groupings to determine which cluster datasets from the data groupings provide sufficient and consistent position accuracy to determine a relative position of the object of interest;

applying stochastic automata model to selected cluster dataset position to evaluate relative cluster weights in order to determine the relative position of the object of interest; and

updating accuracy of a current position of the object of interest based upon determined relative position.