Method and apparatus for mitigating multipath effects and smoothing groundtracks in a GPS receiver

• US 5,883,595 A
• Filed: 09/15/1997
• Issued: 03/16/1999
• Est. Priority Date: 09/15/1997
• Status: Expired due to Term

• Current Assignee: CSR Technology Incorporated (Qualcomm, Inc.)
• Original Assignee: Rockwell International Corporation
• Inventors: Colley, Jaime B.
• Primary Examiner(s): Tarcza, Thomas
• Assistant Examiner(s): PHAN, DAO LINDA
• Application Number:
  US08/929,694
• Time in Patent Office: 547 Days
• Field of Search: 342/357, 701/213, 375/207, 375/350, 455/65, 455/213
• US Class Current: 342/357.29
• CPC Class Codes: G01S 19/42 Determining position
• CPC Class Codes: G01S 19/428 using multipath or indirect path propagation signals in position determination

Abstract

A method of smoothing Kalman Filter position states forming a "groundtrack" in a receiver used in a satellite based positioning system (e.g. GPS). In such systems, data pairs including an incoming value and a "raw" reliability estimate (e.g. a standard deviation) are normally fed directly to the Kalman Filter. The Kalman Filter computes the resultant and an overall uncertainty estimate by applying a "weight" to each successive incoming value based on its reliability. The Kalman Filter also estimates incoming values based on past values.

The method involves the unique steps of replacing the raw reliability with a "modified" reliability if the incoming value is too far from the estimate in view of an adjustable limit envelope defined by the current uncertainty estimate and reliability value. If the difference between what we get and what we expect is small, then the reliability value is passed without modification. If the difference is large, however, then we decrease the reliability value to tell the Kalman Filter that the value is less reliable. In other words, we "de-weight" implausible values. The modified reliability value is preferably scaled or decreased in proportion to the amount by which the square of the incoming value is outside of the limit envelope. This even minimizes the effect of values that are very strong (have high "reliability"), but still very wrong, and thereby smoothes the resultants produced by the Kalman Filter.

Claims

1. A method of smoothing discontinuities in a series of resultant states in a system of the type that uses a Kalman Filter to statistically derive the resultant states along with corresponding uncertainty estimates based on a succession of incoming data pairs comprising measurement values and corresponding reliability estimates which the Kalman Filter uses in order to establish a weight for the measurement value, the method comprising the steps of:

establishing a limit value based on the uncertainty estimate of the resultant state;

comparing a first comparison value based on the measurement value with a second comparison value based on the limit value;

setting a modified reliability value if the first comparison value is greater than the second comparison value; and

providing the modified reliability value to the Kalman Filter in lieu of the reliability estimate in order to de-weight the corresponding measurement value.

2. The method of claim 1 wherein the modified reliability value is equal to the reliability estimate plus a scaled portion of the amount by which the first comparison value exceeds the second comparison value if the first comparison value is greater than the second comparison value.

3. The method of claim 2 wherein the scaled portion used in the setting step is determine empirically in order to maximize a desired system function.

4. The method of claim 1 wherein the step of establishing a limit value is also based on the reliability estimate of the measurement value.

5. The method of claim 4 wherein the uncertainty estimate and the reliability estimate are squared standard deviations and wherein the limit value is the sum of the squared standard deviations.

6. The method of claim 1 wherein the first comparison value is the square of a residual value corresponding to the difference between the measurement value and an a priori estimate of the measurement value provided by the Kalman Filter and wherein the second comparison value is the limit value.

7. The method of claim 1 further comprising the step of providing the reliability estimate to the Kalman Filter without modification if the first comparison value is less than the second comparison value such that the Kalman Filter gives the corresponding measurement value its natural weight.

8. The method of claim 1 wherein the resultant states are position estimates in a satellite based positioning system.

9. The method of claim I wherein the resultant states are velocity estimates in a satellite based positioning system.

10. A method of smoothing discontinuities in a series of resultant states in a satellite based positioning system that uses a Kalman Filter to statistically derive the resultant states along with corresponding uncertainty estimates based on a succession of incoming data pairs comprising pseudoranges and corresponding reliability estimates which the Kalman Filter uses in order to establish a weight for the pseudoranges, the method comprising the steps of:

establishing a limit value based on the uncertainty estimate of the resultant state and the reliability estimate of the pseudorange;

comparing a first comparison value based on the pseudorange with a second comparison value based on the limit value;

setting a modified reliability value if the first comparison value is greater than the second comparison value; and

providing the modified reliability value to the Kalman Filter in lieu of the reliability estimate in order to de-weight the corresponding pseudorange.

11. The method of claim 10 wherein the modified reliability value is equal to the reliability estimate plus a scaled portion of the amount by which the first comparison value exceeds the second comparison value.

12. The method of claim 11 wherein the scaled portion used in the setting step is determine empirically in order to maximize a desired system function.

13. The method of claim 10 wherein the uncertainty estimate and the reliability estimate are squared standard deviations and wherein the limit value is the sum of the squared standard deviations.

14. The method of claim 10 wherein the first comparison value is the square of a residual value corresponding to the difference between the pseudorange and an a priori estimate of the pseudorange provided by the Kalman Filter and wherein the second comparison value is the limit value.

15. The method of claim 10 further comprising the step of providing the reliability estimate to the Kalman Filter without modification if the first comparison value is less than the second comparison value such that the Kalman Filter gives the corresponding pseudorange its natural weight.

16. The method of claim 10 wherein the resultant states are position estimates.

17. The method of claim 10 wherein the resultant states are velocity estimates.

18. An improved satellite based positioning system receiver having a Kalman Filter that statistically derives a succession of position states along with corresponding uncertainty estimates based on a succession of incoming pseudoranges and corresponding reliability estimates which the Kalman Filter uses in order to establish a weight for the pseudoranges, the improvement comprising:

means for establishing a limit value based on the uncertainty estimate of the position state and the reliability estimate of the pseudorange;

means for comparing a first comparison value based on the pseudorange with a second comparison value based on the limit value;

means for setting a modified reliability value if the first comparison value is greater than the second comparison value; and

means for providing the modified reliability value to the Kalman Filter in lieu of the reliability estimate in order to de-weight the corresponding pseudorange.

19. The improved satellite based positioning system receiver of claim 18 wherein the modified reliability value is equal to the reliability estimate plus a scaled portion of the amount by which the first comparison value exceeds the second comparison value.

20. The improved satellite based positioning system receiver of claim 19 wherein the scaled portion used by the means for setting is an empirically determined value that maximizes a desired system function.

21. The improved satellite based positioning system receiver of claim 18 wherein the uncertainty estimate and the reliability estimate are squared standard deviations and wherein the limit value is the sum of the squared standard deviations.

22. The improved satellite based positioning system receiver of claim 18 wherein the first comparison value is the square of a residual value corresponding to the difference between the pseudorange and an a priori estimate of the pseudorange provided by the Kalman Filter and wherein the second comparison value is the limit value.

23. The improved satellite based positioning system receiver of claim 18 further comprising means for providing the reliability estimate to the Kalman Filter without modification if the first comparison value is less than the second comparison value such that the Kalman Filter gives the corresponding pseudorange its natural weight.

24. A method of smoothing discontinuities in position estimate solutions in a satellite positioning system (SATPS) receiver that receives a plurality of SATPS signals from a plurality of satellites and uses a Kalman Filter to update a current position estimate (x y z), the method comprising the steps of:

determining a new pseudorange ρ

_n and a corresponding new standard deviation σ

.sub.ρ

n based on the SATPS signal from an n^th one of the plurality of satellites;

calculating a measurement residual Δ

ρ

_n between the new pseudorange ρ

_n and an a priori pseudorange estimate ρ

_n received from the Kalman Filter;

calculating a pseudorange limit ρ

_LIMIT by adding a variance of the current position estimate σ

_POS² to a variance of the new pseudorange σ

.sub.ρ

n² ;

comparing the square of the measurement residual Δ

ρ

_n² to the square of the pseudorange limit ρ

_LIMIT which defines a permissible value envelope and,

if the square of the measurement residual Δ

ρ

_n² is within the permissible value envelope defined by the pseudorange limit Σ

_LIMIT, setting a variably weighted variance value VAR.sub.ρ

n for use with the new pseudorange ρ

_n equal to the variance of the new pseudorange σ

.sub.ρ

n² and,

if the square of the measurement residual Δ

ρ

_n² exceeds the permissible value envelope defined by the pseudorange limit ρ

_LIMIT , de-weighting the effect of the new pseudorange ρ

_n without ignoring it altogether, by setting the variably weighted variance value VAR.sub.ρ

n for use with the new pseudorange ρ

_n equal to the standard deviation of the new pseudorange σ

.sub.ρ

n plus a scaling constant "K" times the difference between the measurement residual Δ

ρ

_n and the pseudorange limit ρ

_LIMIT ; and

filtering the measurement residual Δ

ρ

_n and the variably weighted variance value VAR.sub.ρ

n with the Kalman Filter to produce a next position estimate (x y z), a standard deviation of the next position estimate σ

_POS, and a next a priori pseudorange estimate ρ

_n for use in acting upon and potentially de-weighting the effect of a next pseudorange ρ

_n and a corresponding standard deviation σ

.sub.ρ

n from the n^th one of the plurality of satellites.

25. The method of claim 24 comprising the further step of displaying the position estimate solutions on a display as groundtracks.

26. The method of claim 24 wherein the scaling constant "K" is determine empirically in order to maximize a desired system function.