METHOD AND ARRANGEMENT FOR LOCATING A MOBLE DEVICE

• US 20170064515A1
• Filed: 08/27/2015
• Published: 03/02/2017
• Est. Priority Date: 08/27/2015
• Status: Active Grant

• Current Assignee: Glopos FZC
• Original Assignee: Glopos FZC
• Inventors: HEIKKILA, Timo, VAINIO, Mikael
• Granted Patent: US 9,743,253 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01S 19/252 Employing an initial estimate of location in generating assistance data
• CPC Class Codes: G01S 5/017 Detecting state or type of motion
• CPC Class Codes: G01S 5/02522 The radio-map containing measured values of non-radio values
• CPC Class Codes: G01S 5/0278 involving statistical or probabilistic considerations G01S5/0252, G01S5/0294 take precedence
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/026 using orientation information, e.g. compass
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information

Abstract

A method for locating an electronic mobile device capable of wireless communication, the method including obtaining a probability map structure for the area of interest, the probability map structure associating environment data values, preferably including cellular network based data, with location data, obtaining a number of probabilistic temporospatial motion models for different types of motion, the models at least partly defining transition probabilities between location estimates relative to time, obtaining at least environment data captured or determined by the mobile device at a plurality of sequential time instances, and determining the most probable location estimate of the mobile device based on combining probability data from both the probability map structure and the motion models, wherein the at least environment data is best fitted in the motion models and probability map structure according to predefined criteria. Related two arrangements and a computer program are presented.

Claims

1. A method for locating an electronic mobile device capable of wireless communication, the method comprising

obtaining a probability map structure for the area of interest, said probability map structure associating environment data values, preferably including cellular network based data, with location data,

obtaining a number of probabilistic temporospatial motion models for different types of motion, said models at least partly defining transition probabilities between location estimates relative to time,

obtaining at least environment data captured or determined by the mobile device at a plurality of sequential time instances, and

determining the most probable location estimate of the mobile device based on combining probability data from both the probability map structure and said motion models, wherein the at least environment data is best fitted in the motion models and probability map structure according to predefined criteria.

2. The method of claim 1, wherein at least one temporospatial motion model of said number of models includes one or more elements indicative at least one motion characteristic selected from the group consisting of:

speed, velocity, speed fluctuation, velocity fluctuation, acceleration, deceleration, turn rate, turn frequency, linearity of motion, stop, stop duration, stop frequency, direction change, direction change frequency, and direction reversal.

3. The method of claim 1, wherein said number of temporospatial motion models comprises a model, optionally a dedicated model, for at least one motion class selected from the group consisting of:

walking, crawling, immobility, running, cycle, motor vehicle, car, motorcycle, train, bus.

4. The method of claim 1, wherein said number of temporospatial motion models comprises a model, optionally a dedicated model, for at least one geographical area or area type selected from the group consisting of:

urban area, countryside, pedestrian area, pedestrian street, highway, forested area, and indoor area.

5. The method of claim 1,

wherein at least one temporospatial motion model of said number of models defines a limit or range for a motion characteristic beyond which the probability of the characteristic is substantially zero in said model.

6. The method of claim 1, comprising mapping activity incorporating determination of at least initial probability map structure or a source data structure therefor through obtaining environment data, preferably including cellular network based data, and temporally substantially corresponding, location-indicating reference data, preferably including satellite positioning data, and linking the environment data with the location data provided by the reference data,

wherein the environment data and the reference or location data linked therewith are pair-wise served by a plurality of terminal devices having regard to a plurality of locations at different time instants for determining the probability map structure.

7. The method of claim 1, comprising determination of at least initial motion models through obtaining environment data and location-indicating reference data from a plurality of mobile devices, and determining motion statistics yielding said transition probabilities based on the obtained data.

8. The method of claim 1, comprising obtaining probabilistic route data indicating at least one element selected from the group consisting of:

probability of a plurality of predefined sequential location transitions within an area, and probability of presence within a predefined area of a greater area, hot spot with probabilistically higher average concentration of mobile devices within an area, and cold spot with probabilistically lower average concentration of mobile devices within an area,

wherein the route data is utilized in determining the most probable location estimate.

9. The method of claim 1, wherein probabilistic route data indicative of probabilities of routes or locations of mobile devices within an area is determined at least partially based on at least one element selected from the group consisting of:

pre-known area characteristics preferably derived from map, street plan or floor plan, and monitoring of movement or presence of mobile devices within the area.

10. The method of claim 1, wherein the environment data comprises sensor data from a sensor of the mobile device, preferably magnetometer data configured to indicate location-associated disturbances in the magnetic field of the area.

11. The method of claim 1, comprising obtaining inertial sensor data and determining the most probable location estimate of the mobile device also based thereon.

12. The method of claim 1, wherein data considered less accurate, or position estimated derived therefrom, is adjusted by data considered more accurate.

13. The method of claim 1, comprising logging position or route data.

14. The method of claim 1, comprising detecting offset in the obtained environment or positioning data and compensating the offset.

15. A computer program comprising program code means adapted to execute the method items of claim 1 when the program is run on a computer.

16. A non-transitory carrier medium, such as a memory card or optical disc, comprising the program of claim 15.

17. An arrangement for locating a mobile device operable in a wireless network, comprising

a data repository comprising a probability map structure for the area of interest, said probability map structure associating environment data values, preferably including cellular network based data, with location data, and further comprising a number of probabilistic temporospatial motion models for multiple different types of motion, said models at least partly defining transition probabilities between location estimates relative to time,

a communication interface for obtaining at least environment data perceived at the mobile device at a plurality of sequential time instances, and

a positioning entity configured to determine the most probable location estimate of the mobile device based on combining probability data from both the probability map structure and said motion models, wherein the at least environment data is best fitted in the motion models and probability map structure according to predefined criteria.

18. The arrangement of claim 17, wherein at least one temporospatial motion model of said number of models includes one or more elements indicative at least one motion characteristic selected from the group consisting of:

speed, velocity, speed fluctuation, velocity fluctuation, acceleration, deceleration, turn rate, turn frequency, linearity of motion, stop, stop duration, stop frequency, direction change, direction change frequency, and direction reversal.

19. The arrangement of claim 17, wherein said number of temporospatial motion models comprises a model, optionally a dedicated model, for at least one motion class selected from the group consisting of:

walking, crawling, immobility, cycling, running, motor vehicle, car, motorcycle, train, bus.

20. The arrangement of claim 17, wherein said number of temporospatial motion models comprises a model, optionally a dedicated model, for at least one geographical area or area type selected from the group consisting of:

urban area, countryside, pedestrian area, pedestrian street, highway, forested area, and indoor area.

21. The arrangement of claim 17, wherein at least one temporospatial motion model of said number of models defines a limit or range for a motion characteristic beyond which the probability of the characteristic is substantially zero in said model.

22. The arrangement of claim 17, configured to determine or update the probability map structure or a source data structure therefor through obtaining environment data, preferably including cellular network based data, and temporally substantially corresponding, location-indicating reference data, preferably including positioning data, most preferably satellite positioning data, and link the environment data with the location data provided by the reference data, said environment and reference data being collected by a number of mobile devices.

23. The arrangement of claim 22, wherein the environment data and the associated reference or location data are pair-wise served by a plurality of terminal devices having regard to a plurality of locations at different time instants for determining the probability map structure.

24. The arrangement of claim 17, configured to determine at least initial motion models through obtaining environment data and location-indicating reference data from a plurality of mobile devices, and determining motion statistics yielding said transition probabilities based on the obtained data.

25. The arrangement of claim 17, configured to obtain probabilistic route data indicating at least one element selected from the group consisting of:

probability of a plurality of predefined sequential location transitions within an area, and probability of presence within a predefined area of a greater area, hot spot with probabilistically higher average concentration of mobile devices within an area, and cold spot with probabilistically lower average concentration of mobile devices within an area, and further configured to utilize the route data in determining the most probable location estimate.

26. The arrangement of claim 17, wherein probabilistic route data indicative of probabilities of routes or locations of mobile devices within an area is determined at least partially based on at least one element selected from the group consisting of:

pre-known area characteristics preferably derived from map, street plan or floor plan, and monitoring of movement or presence of mobile devices within the area.

27. The arrangement of claim 17, wherein the environment data comprises sensor data from a sensor of the mobile device, preferably magnetometer data configured to indicate location-associated disturbances in the magnetic field of the area.

28. The arrangement of claim 17, configured to obtain inertial sensor data and determine the most probable location estimate of the mobile device also based thereon.

29. The arrangement of claim 17, configured to adjust data, optionally environment data such as cellular network based or sensor data, considered less accurate according to predefined criterion, or position estimated derived therefrom, by data, optionally reference considered more accurate.

30. The arrangement of claim 17, configured to log, in a data repository, route or position estimate data.

31. The arrangement of claim 17, comprising at least one communications network, preferably the Internet, accessible server.

32. The arrangement of claim 17, consisting of or at least comprising the mobile device.

33. An arrangement, preferably comprising at least one server, for use in positioning, comprising

a mapping entity configured to determine

for the area of interest, a probability map structure or a source data structure therefor, said probability map or related source data structure associating environment data values, preferably including cellular network based data, with location data, through obtaining environment data, preferably including cellular network based data, and temporally substantially corresponding, location-indicating reference data, preferably including positioning data, most preferably satellite positioning data, and linking the environment data with the location data provided by the reference data, said environment and related reference data being collected by a number of mobile devices;

and

a number of probabilistic temporospatial motion models for multiple different types of motion, said models at least partly defining transition probabilities between location estimates relative to time, and

a communication interface configured to signal at least part of said probability map structure and motion models to an electronic device for positioning.