METHOD FOR FUSING DATA FROM SENSORS USING A CONSISTENCY CRITERION

• US 20150148997A1
• Filed: 11/25/2014
• Published: 05/28/2015
• Est. Priority Date: 11/28/2013
• Status: Active Grant

• Current Assignee: Airbus Operations (European Aeronautic Defence and Space Company EADS NV)
• Original Assignee: Airbus Operations (European Aeronautic Defence and Space Company EADS NV)
• Inventors: Mailhes, Corinne, Tourneret, Jean-Yves, Brot, Patrice, Fall, Emmanuel
• Granted Patent: US 9,268,330 B2
• Time in Patent Office: Days
• US Class Current: 701/3
• CPC Class Codes: G01D 3/08 with provision for safeguarding the apparatus, e.g. against abnormal operation, against breakdown
• CPC Class Codes: G01P 13/025 indicating air data, i.e. flight variables of an aircraft, e.g. angle of attack, side slip, shear, yaw
• CPC Class Codes: G01P 21/025 for measuring speed of fluids; for measuring speed of bodies relative to fluids for measuring volume flow G01F25/10
• CPC Class Codes: G05D 1/00 Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots drive control systems specially adapted for autonomous road vehicles B60W60/00

Abstract

The disclosure relates to a data fusion method for fusing the measurements of a parameter, for example an aircraft flight parameter, that are taken by a plurality of sensors comprising a set of main sensors and sets of secondary sensors. The discrepancy between each main measurement and the secondary measurements is determined and a score of consistency with them is deduced therefrom. For each fault configuration of the main sensors, a first estimation, so-called conditional, of the parameter is performed and a weighting coefficient relating to this fault is calculated, taking account of the consistency scores of the main measurements. The parameter is thereafter estimated by performing a combination of the conditional measurements with the associated weighting coefficients.

Claims

1. A method for fusing measurements of an aircraft flight parameter on a basis of a first set of main measurements taken by a first set of so-called main sensors, and of a plurality of second sets of so-called secondary measurements taken by second sets of secondary sensors, the main measurements exhibiting a greater degree of accuracy than the secondary measurements, wherein:

a discrepancy between each main measurement and the secondary measurements is calculated;

a score of consistency of each main measurement with the secondary measurements is determined by the discrepancy thus obtained;

for each possible fault configuration of the main sensors, a first estimation, so-called conditional, of the parameter is performed on the basis of the main measurements taken by the sensors which are not faulty in the configuration;

for each fault configuration, a weighting coefficient is determined on the basis of the consistency scores of the main measurements obtained previously;

an estimation of the parameter is performed by weighting the conditional estimations relating to the various fault configurations by the weighting coefficients corresponding to these configurations.

2. The method for fusing measurements according to claim 1, wherein, for each set of secondary measurements, calculation of the discrepancy between a main measurement and the secondary measurements comprises calculation of the discrepancy between the main measurement and each secondary measurement of the set.

3. The method for fusing measurements according to claim 1, wherein the score of consistency of a main measurement with the secondary measurements is obtained:

by calculating, on the basis of the discrepancy between the main measurement and each secondary measurement, masses respectively allocated to a first focal set corresponding to a first hypothesis of consistency of the main measurement with the secondary measurement, to a second focal set corresponding to a second hypothesis of absence of consistency of the main measurement with the secondary measurement and to a third hypothesis corresponding to an uncertainty of the consistency of the main measurement with the secondary measurement;

by estimating a belief that the main measurement is consistent with at least one of the secondary measurements on the basis of the masses thus obtained;

by estimating a plausibility that the main measurement is consistent with at least one of the secondary measurements on the basis of the masses thus obtained;

by calculating the consistency score by combining the belief and the plausibility by a combination function.

4. The method for fusing measurements according to claim 1, wherein, for each set of secondary measurements, calculation of the discrepancy between a main measurement and the secondary measurements comprises calculation of the discrepancy between the main measurement and a fused secondary measurement obtained by fusing the secondary measurements of the set.

5. The method for fusing measurements according to claim 4, wherein the score of consistency of a main measurement with the secondary measurements is obtained:

by calculating, on the basis of the discrepancy between the main measurement and each fused secondary measurement, masses respectively allocated to a first focal set corresponding to a first hypothesis of consistency of the main measurement with the fused secondary measurement, to a second focal set corresponding to a second hypothesis of absence of consistency of the main measurement with the fused secondary measurement and to a third hypothesis corresponding to an uncertainty of the consistency of the main measurement with the fused secondary measurement;

by estimating a belief that the main measurement is consistent with at least one of the fused secondary measurements on the basis of the masses thus obtained;

by estimating a plausibility that the main measurement is consistent with at least one of the fused secondary measurements on the basis of the masses thus obtained;

by calculating the consistency score by combining the belief and the plausibility by a combination function.

6. The method for fusing measurements according to claim 3, wherein the combination function is an average.

7. The method for fusing measurements according to claim 2, wherein the score of consistency of a main measurement with the secondary measurements is obtained:

by calculating fuzzy values of strong consistency, average consistency, weak consistency between the main measurement and each secondary measurement on the basis of the discrepancy between the main measurement and this secondary measurement;

by calculating fuzzy consistency rules operating on the fuzzy values;

by calculating the consistency score by combining the fuzzy rules thus calculated.

8. The method for fusing measurements according to claim 4, wherein the score of consistency of a main measurement with the secondary measurements is obtained:

by calculating fuzzy values of strong consistency, average consistency, weak consistency between the main measurement and each fused secondary measurement on the basis of the discrepancy between the main measurement and this fused secondary measurement;

by calculating fuzzy consistency rules operating on the fuzzy values;

by calculating the consistency score by combining the fuzzy rules thus calculated with the aid of a combination operator.

9. The method for fusing measurements according to claim 7, wherein the fuzzy consistency rules use Lukasiewicz OR, AND and NOT fuzzy operators.

10. The method for fusing measurements according to claim 9, wherein the combination operator is an OR function.

11. The method for fusing measurements according to claim 10, wherein the fuzzy rules are weighted with weighting factors before the combination, a weighting factor of a rule being all the higher the more the latter involves a fuzzy value of strong consistency with a larger number of elementary measurements.

12. The method for fusing measurements according to claim 2, wherein the score of consistency of a main measurement with the secondary measurements is obtained by a supervised learning method of “

one class SVM”

type.

13. The method for fusing measurements according to claim 4, wherein the score of consistency of a main measurement with the fused secondary measurements is obtained by a supervised learning method of “

one class SVM”

type.

14. The method for fusing measurements according to claim 13, wherein the conditional estimation, relating to a fault configuration of the main sensors, is obtained as the median of the main measurements of the functioning sensors of this configuration, when the number of the functioning sensors is odd.

15. The method for fusing measurements according to claim 1, wherein the conditional estimation, relating to a fault configuration of the main sensors, is obtained as the average of the main measurements of the functioning sensors of this configuration.