STATE VECTOR ESTIMATION USING A CONSTRAINT DERIVED FROM A MAP

• US 20110196647A1
• Filed: 07/30/2008
• Published: 08/11/2011
• Est. Priority Date: 07/30/2008
• Status: Active Grant

• Current Assignee: RPX Corporation
• Original Assignee: Nokia Corporation
• Inventors: Perälä, Tommi, Ali-Löytty, Simo
• Granted Patent: US 8,892,392 B2
• Time in Patent Office: Days
• US Class Current: 702/150
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G01C 21/30 Map- or contour-matching
• CPC Class Codes: G01S 19/072 Ionosphere corrections
• CPC Class Codes: G01S 19/42 Determining position
• CPC Class Codes: G01S 19/50 whereby the position solution is constrained to lie upon a particular curve or surface, e.g. for locomotives on railway tracks
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

It is disclosed to estimate a state vector of an object, wherein the state vector comprises a state variable related to a position or an orientation of the object, based on at least one of a probability density function of the state vector truncated under consideration of a constraint for the state variable, wherein the constraint is derived from a map, and properties of the truncated probability density function.

Claims

1. An apparatus comprising:

a processor configured to estimate a state vector of an object, wherein the state vector comprises a state variable related to a position or an orientation of the object, based on at least one of

a probability density function of the state vector truncated under consideration of a constraint for the state variable derived from a map, and

properties of the truncated probability density function.

2. The apparatus according to claim 1, further comprising a processor configured to derive the constraint or a processor configured to receive the constraint.

3. The apparatus according to any of the claims 1-2, further comprising a processor configured to truncate a probability density function to obtain the probability density function truncated under consideration of the constraint.

4. The apparatus according to any of the claims 1-3, further comprising an antenna.

5. The apparatus according to any of the claims 1-4, wherein the apparatus is a navigation device.

6. The apparatus according to any of the claims 1-5, wherein the estimation of the state vector is performed recursively.

7. The apparatus according to claim 6, wherein the estimation of the state vector involves Kalman filtering.

8. The apparatus according to any of the claims 6-7, wherein an estimate of the state vector that has been obtained in the estimation of the state vector under consideration of a constraint serves as a basis for a subsequent state vector estimation.

9. The apparatus according to any of the claims 1-8, wherein in the estimation at least two state vector estimates are calculated for one point in time and the first state vector estimate is characterized by a first probability density function, while the second state vector estimate is characterized by a second probability density function.

10. The apparatus according to claim 9, wherein the constraint matches at least two disconnected spaces and a probability density function is assigned to each of the disconnected spaces.

11. The apparatus according to any of the claims 9-10, further comprising a processor configured to assign a first weight to the first probability density function and to assign a second weight to the second probability density function.

12. The apparatus according to claim 11, wherein a weight corresponds to the probability mass of an unscaled truncated probability density function the probability density function truncated under consideration of the constraint is derived from or is identical to.

13. The apparatus according to any of the claims 11-12, wherein in the estimation of the state vector a weight is updated in an estimation iteration that follows an estimation iteration in which the weight has been initially assigned to a probability density function.

14. The apparatus according to any of the claims 1-13, wherein the constraint is a linear constraint.

15. The apparatus according to claim 14, wherein the linear constraint is derived from a nonlinear constraint.

16. The apparatus according to any of the claims 1-15, wherein the state vector is described by means of a probability density function of a preset class of probability density functions.

17. The apparatus according to claim 16, wherein the probability density function is Gaussian.

18. The apparatus according to any of the claims 16-17, wherein a probability density function of the preset class of probability density functions is derived from the truncated probability density function or properties thereof.

19. The apparatus according to any of the claims 1-18, further comprising a processor configured to derive properties of the truncated probability density function of the state vector from properties of the probability density function that can be truncated to obtain the truncated probability density function.

20. A method comprising:

estimation of a state vector of an object, wherein the state vector comprises a state variable related to a position or an orientation of the object, based on at least one of

a probability density function of the state vector truncated under consideration of a constraint for the state variable derived from a map, and

properties of the truncated probability density function.

21. The method of claim 20, further comprising deriving the constraint or receiving the constraint.

22. The method according to any of the claims 20-21, further comprising truncating a probability density function to obtain the probability density function truncated under consideration of the constraint.

23. The method according to any of the claims 20-22, wherein the estimation of the state vector is performed recursively.

24. The method of claim 23, wherein the estimation of the state vector involves Kalman filtering.

25. The method according to any of the claims 23-24, wherein an estimate of the state vector that has been obtained in the estimation of the state vector under consideration of a constraint serves as a basis for a subsequent state vector estimation.

26. The method according to any of the claims 20-25, wherein in the estimation at least two state vector estimates are calculated for one point in time and the first state vector estimate is characterized by a first probability density function, while the second state vector estimate is characterized by a second probability density function.

27. The method of claim 26, wherein the constraint matches at least two disconnected spaces and a probability density function is assigned to each of the disconnected spaces.

28. The method according to any of the claims 26-27, wherein a first weight is assigned to the first probability density function and a second weight is assigned to the second probability density function.

29. The method of claim 28, wherein a weight corresponds to the probability mass of an unscaled truncated probability density function the probability density function truncated under consideration of the constraint is derived from or is identical to.

30. The method according to any of the claims 28-29, wherein in the estimation of the state vector a weight is updated in an estimation iteration that follows an estimation iteration in which the weight has been initially assigned to a probability density function.

31. The method according to any of the claims 28-30, wherein a state vector estimate is discarded if the weight of the probability density function that characterizes it falls below a preset value.

32. The method according to any of the claims 26-31, wherein different state models are assigned to the estimation of the first state vector estimate and to the estimation of the second state vector estimate.

33. The method according to any of the claims 20-32, wherein the constraint is a linear constraint.

34. The method of claim 33, wherein the linear constraint is derived from a nonlinear constraint.

35. The method according to any of the claims 20-34, wherein the state vector is described by means of a probability density function of a preset class of probability density functions.

36. The method of claim 35, wherein the probability density function is Gaussian.

37. The method according to any of the claims 35-36, wherein a probability density function of the preset class of probability density functions is derived from the truncated probability density function or properties thereof.

38. The method according to any of the claims 20-37, further comprising deriving properties of the truncated probability density function of the state vector from properties of the probability density function that can be truncated to obtain the truncated probability density function.

39. A program comprising:

program code for performing the method according to any of the claims 20-38, when the program is executed on a processor.

40. A readable medium having a program according to claim 39 stored thereon.

41. An apparatus comprising:

means for estimating a state vector of an object, wherein the state vector comprises a state variable related to a position or an orientation of the object, based on at least one of

a probability density function of the state vector truncated under consideration of a constraint for the state variable derived from a map, and

properties of the truncated probability density function.