Position tracking device and method

• US 8,773,303 B2
• Filed: 06/22/2007
• Issued: 07/08/2014
• Est. Priority Date: 06/22/2007
• Status: Active Grant

• Current Assignee: Trimble Navigation Limited (Trimble Inc.)
• Original Assignee: Trimble Navigation Limited (Trimble Inc.)
• Inventors: Doucet, Ken, Vollath, Ulrich
• Primary Examiner(s): LIU, HARRY K
• Application Number:
  US12/451,513
• Publication Number:
  US 20100141515A1
• Time in Patent Office: 2,573 Days
• Field of Search: None
• US Class Current: 342/357.25
• CPC Class Codes: G01S 19/393 Trajectory determination or predictive tracking, e.g. Kalman filtering
• CPC Class Codes: G01S 19/40 Correcting position, velocity or attitude

Abstract

The present application relates to tracking a position of a device, e.g. for detecting slow and rapid earth deformation, by making use of a recursive filter having the filter characteristic adapted to a detected type of motion. If the motion of the position tracking device is rapid, the filter characteristic is set such that the rapid motion can be tracked with the necessary speed. On the other hand, if the motion is slow, e.g. during times of a normal tectonic drift, the filter characteristic is set such that the motion is slowly tracked with the advantage of efficient noise reduction, i.e. noise in the input signal is effectively barred and does not pass through the filter to the output signal. Thus, in times of rapid motion the convergence speed of the filter output signal to the input signal is set high for fast convergence and in times of slow motion the convergence speed of the filter output signal to the input signal is set low for a slow convergence. The filter may be a Kalman filter.

Claims

1. Position tracking device, including

means for receiving positioning signals from a plurality of satellites at known positions;

means for determining a position estimate using a recursive filter adapted to receive as an input the positioning signals and a convergence parameter to adapt the filter characteristic of the recursive filter;

means for obtaining a displacement estimate of the position tracking device;

wherein the means for determining the position estimate is adapted to adjust the convergence parameter based on the displacement estimate to indicate a first velocity state of the position tracking device and a second velocity state of the position tracking device.

2. Position tracking device according to claim 1, wherein the recursive filter is a Kalman filter and wherein the convergence parameter is constituted by a position noise input to the Kalman filter.

3. Position tracking device according to claim 2, wherein the convergence parameter is constituted by the displacement estimate multiplied by a constant.

4. Position tracking device according to claim 3, wherein the displacement estimate is determined based on at least one of:

a time sequence of displacement measurements;

a velocity vector describing a movement of the position tracking device;

a phase of the positioning signals from the satellites; and

a signal from a motion sensor.

5. Position tracking device according to claim 1, wherein the means for determining a position estimate is adapted to set the convergence parameter to a value indicating slow convergence, if the displacement estimate is below a predetermined detect threshold, and to set the convergence parameter to a value indicating fast convergence, if the displacement estimate exceeds the predetermined detect threshold.

6. Position tracking device according to claim 1, wherein for indicating fast convergence the convergence parameter is determined based on the displacement estimate and for indicating slow convergence the convergence parameter is constituted by a predetermined constant.

7. Position tracking device according to claim 1, wherein the means for determining a position estimate is adapted to:

perform a first recursive filtering process based on a first group of time sequences of positioning signals spaced by a first measurement time interval;

perform a second recursive filtering process based on a second group of time sequences of positioning signals spaced by a second measurement time interval longer than the first measurement interval; and

use the determination result of the first recursive filtering process, if the displacement estimate exceeds a predetermined threshold, and to use the determination result of the second recursive filtering process, if the displacement estimate is below the predetermined threshold.

8. Position tracking device according to claim 1, wherein the convergence parameter includes covariances of a displacement in different spatial directions.

9. System for tracking tectonic drifts including a plurality of position tracking devices according to claim 1.

10. Position tracking device according to claim 1, wherein the first velocity state represents a stationary state of the position tracking device and the second velocity state represents a non-stationary state of the position tracking device.

11. Position tracking device according to claim 1, wherein the first velocity state represents a slow moving state of the position tracking device and the second velocity state represents a fast moving state of the position tracking device.

12. Method for tracking a position, including

receiving positioning signals from a plurality of satellites at known positions at a position tracking device;

obtaining a displacement estimate of the position tracking device;

adjusting a convergence parameter based on the displacement estimate; and

determining a position estimate using a recursive filter receiving as an input the positioning signals and the convergence parameter to indicate a first velocity state of the position tracking device and a second velocity state of the position tracking device.

13. Method of claim 12, wherein the recursive filter is a Kalman filter and wherein the convergence parameter is constituted by a position noise input to the Kalman filter.

14. Method of claim 12, further including determining the convergence parameter by multiplying the displacement estimate with a constant.

15. Method of claim 12, further including determining the displacement estimate based on at least one of:

a time sequence of displacement measurements;

a velocity vector describing a movement of the position tracking device;

a phase of the positioning signals from the satellites; and

a signal from a motion sensor.

16. Method of claim 12, further including setting the convergence parameter to a value indicating slow convergence, if the displacement estimate is below a predetermined detect threshold, and setting the convergence parameter to a value indicating fast convergence, if the displacement estimate exceeds the predetermined detect threshold.

17. Method of claim 12, further including determining the convergence parameter based on the displacement estimate for indicating fast convergence of the convergence parameter and defining the convergence parameter as a predetermined constant for indicating slow convergence.

18. Method of claim 12, further including:

performing a first recursive filtering process based on a first group of time sequences of positioning signals spaced by a first measurement time interval;

performing a second recursive filtering process based on a second group of time sequences of positioning signals spaced by a second measurement time interval longer than the first measurement interval; and

using the determination result of the first recursive filtering process, if the displacement estimate exceeds a predetermined threshold, and using the determination result of the second recursive filtering process, if the displacement estimate is below the predetermined threshold.

19. Method of claim 12, wherein the convergence parameter includes covariances of a displacement in different spatial directions.

20. A non-transitory computer readable medium, in which a program is embodied, where the program is adapted to make a computer execute the method of claim 12.

21. A computer program product comprising the non-transitory computer readable medium according to claim 20.

22. Method of claim 12, wherein the first velocity state represents a stationary state of the position tracking device and the second velocity state represents a non-stationary state of the position tracking device.

23. Method of claim 12, wherein the first velocity state represents a slow moving state of the position tracking device and the second velocity state represents a fast moving state of the position tracking device.

24. Position tracking device, comprising:

a receiver to receive positioning signals from a plurality of satellites at known positions;

a recursive filter adapted to determine a position estimate using the positioning signals and a convergence parameter to adapt the filter characteristics of the recursive filter; and

a displacement estimation unit to obtain a displacement estimate of the position tracking device;

wherein the recursive filter is adapted to adjust the convergence parameter based on the displacement estimate to indicate a first velocity state of the position tracking device and a second velocity state of the position tracking device.

25. Position tracking device according to claim 24, wherein the recursive filter is a Kalman filter and wherein the convergence parameter is constituted by a position noise input to the Kalman filter.

26. Position tracking device according to claim 24, wherein the convergence parameter is constituted by the displacement estimate multiplied by a constant.

27. Position tracking device according to claim 24 wherein the displacement estimate is determined based on at least one of:

a time sequence of displacement measurements;

a phase of the positioning signals from the satellites;

a signal from a motion sensor; and

a velocity vector describing a movement of the position tracking device.

28. Position tracking device according to claim 24, wherein the convergence parameter is set to a value indicating slow convergence if the displacement estimate is below a predetermined detect threshold, and to value indicating fast convergence if the displacement estimate exceeds the predetermined detect threshold.

29. Position tracking device according to claim 24, wherein for indicating fast convergence the convergence parameter is determined based on the displacement estimate and for indicating slow convergence the convergence parameter is constituted by a predetermined constant.

30. Position tracking device according to claim 24, wherein the recursive filter is adapted to:

perform a first recursive filtering process based on a first group of time sequences of positioning signals spaced by a first measurement time interval;

perform a second recursive filtering process based on a second group of time sequences of positioning signals spaced by a second measurement time interval longer than the first measurement interval; and

use the determination result of the first recursive filtering process if the displacement estimate exceeds a predetermined threshold, and to use the determination result of the second recursive filtering process if the displacement estimate is below the predetermined threshold.

31. Position tracking device according to claim 24, wherein the convergence parameter includes covariances of a displacement in different spatial directions.

32. Position tracking device according to claim 24, wherein the first velocity state represents a stationary state of the position tracking device and the second velocity state represents a non-stationary state of the position tracking device.

33. Position tracking device according to claim 24, wherein the first velocity state represents a slow moving state of the position tracking device and the second velocity state represents a fast moving state of the position tracking device.