CN113049004A - Automatic assessment method and device for aeromagnetic compensation calibration quality - Google Patents

Automatic assessment method and device for aeromagnetic compensation calibration quality Download PDF

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CN113049004A
CN113049004A CN202110222203.5A CN202110222203A CN113049004A CN 113049004 A CN113049004 A CN 113049004A CN 202110222203 A CN202110222203 A CN 202110222203A CN 113049004 A CN113049004 A CN 113049004A
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data
fom
representing
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calibration
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韩琦
王艺臻
李尤
李琼
王莘
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Harbin Institute of Technology
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Harbin Institute of Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G—PHYSICS
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Abstract

An automatic assessment method and device for aeromagnetic compensation calibration quality relates to the field of aeromagnetic compensation. The FOM calculation method aims to solve the problems that the manual operation is needed to identify the maneuvering in all directions in the current FOM calculation process, time is consumed, and the automatic application of FOM is limited. The method comprises the following steps: acquiring clustering data of a flat flying ring; deleting data at a turn in the clustering data; establishing a Gaussian mixture model of each course of the flat flying ring; acquiring clustering data of FOM calibration circles; deleting data at a turn in the clustering data; calculating the posterior probability of FOM calibration circle data; and comparing the posterior probability with a preset threshold value, screening out maneuvering data, and calculating the value of the compensation effect evaluation index FOM of the calibration ring according to the maneuvering data. The device comprises: the device comprises a first clustering module, a first deleting module, a training module, a second clustering module, a second deleting module, a posterior probability calculating module, a comparing module and an FOM calculating module.

Description

Automatic assessment method and device for aeromagnetic compensation calibration quality
Technical Field
The invention relates to an assessment technology of aeromagnetic compensation calibration quality, which can be applied to automatically computing assessment indexes of aeromagnetic compensation effect of a calibration ring and belongs to the field of aeromagnetic compensation.
Background
Airborne magnetic compensation is a technique aimed at reducing the magnetic interference generated by aircraft flying in the earth's magnetic field. The method comprises the steps of establishing an aviation platform magnetic interference mathematical model by analyzing the type and the property of the aviation platform magnetic interference, measuring a magnetic total field and three-component data according to a specified method in a calibration flight process, and calculating coefficients of the aviation platform magnetic interference mathematical model by using the magnetic total field and the three-component data. The data used to solve the compensation model coefficients come from the FOM calibration loop, a complete FOM calibration loop comprising four orthogonal directions, in each of which the aerial platform performs three maneuvers, pitch, roll and yaw. The FOM is an index for measuring the ability of the compensation algorithm and is defined as the sum of the peak and peak values of the compensated results corresponding to all maneuvers in four directions. However, the FOM calculation process requires manual operation to identify the maneuvers in all directions, and the work is time-consuming and limits the automatic application of FOM. There is therefore a need for automated identification of maneuvers in various directions in the process of calculating FOM to achieve an automated assessment of the quality of the aeromagnetic compensation calibration.
Disclosure of Invention
The invention aims to solve the problems that the FOM calculation process needs manual operation to identify the maneuvering in each direction, not only is time-consuming, but also the automatic application of the FOM is limited, and provides an automatic evaluation method and device for aeromagnetic compensation calibration quality.
The invention relates to an automatic evaluation method of aeromagnetic compensation calibration quality, which comprises the following steps:
according to the formula
Figure BDA0002955399780000011
Obtaining the clustering center of the flat flying ring as csCluster data of
Figure BDA0002955399780000012
Wherein m represents the number of headings contained in the flying circle, DsClustering of sampled data representing heading s, nsRepresents DsThe number of sample points that are involved,
Figure BDA0002955399780000013
is DsData corresponding to the ith sampling point; (ii) a
Will fly round DsDeleting the turning data far away from the clustering center to obtain effective clustering data of different courses of the flat flying ring
Figure BDA0002955399780000014
Wherein lsAnd rsRespectively representing the number of deleted sampling points at two ends of the course s;
according to the formula
Figure BDA0002955399780000015
Obtaining a Gaussian mixture model corresponding to each course of the flat flying ring, wherein p (b)s|Gs) Denotes the Gaussian mixture density, bsElement (1) of
Figure BDA0002955399780000021
Representing a combination of X, Y and Z three-component magnetic field characteristics in a rectangular spatial coordinate system, X representing a direction parallel to the transverse axis of the platform, Y representing a direction parallel to the longitudinal axis of the platform, Z representing a direction perpendicular to the horizontal plane, GsThe parameters of the gaussian model are represented by,
Figure BDA0002955399780000022
wherein the content of the first and second substances,
Figure BDA0002955399780000023
is that the heading s satisfies the constraint
Figure BDA0002955399780000024
K represents the number of gaussian distributions,
Figure BDA0002955399780000025
and
Figure BDA0002955399780000026
respectively is the mean and covariance matrix of the jth Gaussian distribution of the course s;
according to the formula
Figure BDA0002955399780000027
Obtaining the clustering center of FOM calibration circle as csCluster data of
Figure BDA0002955399780000028
Aligning FOM with circle DsDeleting the turning data far away from the clustering center to obtain effective clustering data of FOM calibration circles with different courses
Figure BDA0002955399780000029
According to the formula
Figure BDA00029553997800000210
Calculating posterior probability
Figure BDA00029553997800000211
Will satisfy
Figure BDA00029553997800000212
As maneuver data for different headings of the calibration circle, wherein ThIs a preset threshold value;
and calculating the sum of the peak value and the peak value of the obtained maneuvering data, and taking the sum as the value of the compensation effect evaluation index FOM of the calibration loop.
Alternatively,
Figure BDA00029553997800000213
wherein the content of the first and second substances,
Figure BDA00029553997800000214
representing the scalar version of the earth's magnetic field H corresponding to the ith sample point of the heading s,
Figure BDA00029553997800000215
indicating the heading angle corresponding to the ith sampling point of the heading s,
Figure BDA00029553997800000216
and representing the inclination angle of the geomagnetic field corresponding to the ith sampling point of the heading s.
Optionally, the Gaussian mixture model parameter GsAnd estimating by using an EM algorithm.
Optionally, different headings and different sampling points correspond to each other
Figure BDA00029553997800000217
The values of the magnetic field are equal, the geomagnetic field inclination angles corresponding to different courses and different sampling points are different
Figure BDA00029553997800000218
Are equal in value.
The invention relates to an automatic evaluation device for aeromagnetic compensation calibration quality, which comprises:
a first clustering module configured to calculate a first cluster value based on a formula
Figure BDA00029553997800000219
Obtaining the clustering center of the flat flying ring as csCluster data of
Figure BDA00029553997800000220
Wherein m represents the number of headings contained in the flying circle, DsClustering of sampled data representing heading s, nsRepresents DsThe number of sample points that are involved,
Figure BDA0002955399780000031
is DsData corresponding to the ith sampling point;
a first deletion module configured to delete the flying circle DsDeleting turn data far away from clustering center to obtain flat flying circleCo-heading efficient clustering data
Figure BDA0002955399780000032
Wherein lsAnd rsRespectively representing the number of deleted sampling points at two ends of the course s;
a Gaussian mixture model obtaining module configured to obtain a model from a formula
Figure BDA0002955399780000033
Obtaining a Gaussian mixture model corresponding to each course of the flat flying ring, wherein p (b)s|Gs) Denotes the Gaussian mixture density, bsElement (1) of
Figure BDA0002955399780000034
Representing a combination of X, Y and Z three-component magnetic field characteristics in a rectangular spatial coordinate system, X representing a direction parallel to the transverse axis of the platform, Y representing a direction parallel to the longitudinal axis of the platform, Z representing a direction perpendicular to the horizontal plane, GsThe parameters of the gaussian model are represented by,
Figure BDA0002955399780000035
wherein the content of the first and second substances,
Figure BDA0002955399780000036
is that the heading s satisfies the constraint
Figure BDA0002955399780000037
K represents the number of gaussian distributions,
Figure BDA0002955399780000038
and
Figure BDA0002955399780000039
respectively is the mean and covariance matrix of the jth Gaussian distribution of the course s;
a second clustering module configured to be based on a formula
Figure BDA00029553997800000310
Obtaining FOM calibration circle clusteringThe heart is csCluster data of
Figure BDA00029553997800000311
A second deletion module configured to calibrate the FOM by a circle DsDeleting the turning data far away from the clustering center to obtain effective clustering data of FOM calibration circles with different courses
Figure BDA00029553997800000312
A posterior probability calculation module configured to calculate a posterior probability based on the formula
Figure BDA00029553997800000313
Calculating posterior probability
Figure BDA00029553997800000314
A comparison module configured to satisfy
Figure BDA00029553997800000315
As maneuver data for different headings of the calibration circle, wherein ThIs a preset threshold value; and
and the FOM calculation module is configured to calculate the sum of peak values and peak values of the obtained maneuvering data, and the sum is used as the value of the compensation effect evaluation index FOM of the calibration loop.
Optionally, in the first clustering module,
Figure BDA00029553997800000316
wherein the content of the first and second substances,
Figure BDA00029553997800000317
representing the scalar version of the earth's magnetic field H corresponding to the ith sample point of the heading s,
Figure BDA0002955399780000041
indicating the heading angle corresponding to the ith sampling point of the heading s,
Figure BDA0002955399780000042
and representing the inclination angle of the geomagnetic field corresponding to the ith sampling point of the heading s.
Optionally, the Gaussian mixture model parameter GsAnd estimating by using an EM algorithm.
Optionally, different headings and different sampling points correspond to each other
Figure BDA0002955399780000043
The values of the magnetic field are equal, the geomagnetic field inclination angles corresponding to different courses and different sampling points are different
Figure BDA0002955399780000044
Are equal in value.
According to the automatic evaluation method and device for the aeromagnetic compensation calibration quality, a Gaussian Mixture Model (GMM) Model is used for identifying the data section of the aeromagnetic platform in the flat flight state at each course according to the characteristics of the data of the flat flight state of the aeromagnetic platform, so that the data section of the aeromagnetic platform in the maneuvering state at each course is obtained, an index FOM for evaluating the compensation effect of a calibration circle is calculated according to the data of the maneuvering state, and the automatic evaluation of the aeromagnetic compensation calibration quality is realized.
Drawings
FIG. 1 is a functional block diagram of an automatic assessment method of aeromagnetic compensation calibration quality according to an embodiment;
FIG. 2 is a schematic flow chart of a method for automatically evaluating the quality of an aeromagnetic compensation calibration according to an embodiment;
fig. 3 is a schematic structural diagram of an automatic evaluation apparatus for aeromagnetic compensation calibration quality according to an embodiment.
Detailed Description
The present embodiment provides an automatic assessment method of aeromagnetic compensation calibration quality, which may generally include:
step S1, according to the formula
Figure BDA0002955399780000045
Obtaining the clustering center of the flat flying ring as csCluster of (2)Data of
Figure BDA0002955399780000046
Wherein, aiIs the total X and Y components of the three-component magnetometer output,
Figure BDA0002955399780000047
is the data of each type of heading s obtained by the k-means algorithm,
Figure BDA0002955399780000048
representing the scalar version of the earth's magnetic field H corresponding to the ith sample point of the heading s,
Figure BDA0002955399780000049
indicating the heading angle corresponding to the ith sampling point of the heading s,
Figure BDA00029553997800000410
representing the inclination angle of the geomagnetic field corresponding to the ith sampling point of the course s, wherein m represents the number of courses contained in the flat flying ring, and for a standard flying ring, m is 4, and if the flying ring contains a plurality of courses, m is equal to the number of courses actually contained in the flat flying ring;
Dsclustering of sampled data representing heading s, nsRepresents DsThe number of sample points that are involved,
Figure BDA00029553997800000411
is DsData corresponding to the ith sampling point;
csthe K-means algorithm obtains the clustering center, and the K-means algorithm is optimized
Figure BDA00029553997800000412
D is the set of all s clusters;
step S2, the flying circle DsDeleting the turning data far away from the clustering center to obtain effective clustering data of different courses of the flat flying ring
Figure BDA0002955399780000051
Wherein，lsAnd rsRespectively representing the number of deleted sampling points at two ends of the course s, wherein the specific deletion method comprises the following steps: setting a threshold for Euler distance, and comparing the distance csDeleting data with Euler distance exceeding threshold;
step S3, according to the formula
Figure BDA0002955399780000052
Obtaining a Gaussian mixture model corresponding to each course of the flat flying ring, wherein p (b)s|Gs) Denotes the Gaussian mixture density, bsElement (1) of
Figure BDA0002955399780000053
Representing the combination of X, Y and Z three-component magnetic field characteristics of the output of a three-component magnetometer in a rectangular spatial coordinate system, X representing a direction parallel to the transverse axis of the platform, Y representing a direction parallel to the longitudinal axis of the platform, Z representing a direction perpendicular to the horizontal plane, GsThe parameters of the gaussian model are represented by,
Figure BDA0002955399780000054
according to the formula
Figure BDA0002955399780000055
Constructing a likelihood function, estimating G using EM algorithms，
Figure BDA0002955399780000056
Is that the heading s satisfies the constraint
Figure BDA0002955399780000057
K represents the number of gaussian distributions,
Figure BDA0002955399780000058
and
Figure BDA0002955399780000059
respectively is the mean and covariance matrix of the jth Gaussian distribution of the course s; it should be noted that the number of headings contained in a flying circle is typically four, but in some casesIf the number of the routes is not four, the step S3 is to obtain a gaussian mixture model corresponding to each route of the planar flight circle, and if only the obtained routes corresponding to all the gaussian mixture models include the route included in the FOM maneuvering circle to be calculated, all posterior probabilities can be calculated according to the existing gaussian mixture models to obtain which gaussian mixture model corresponds to a certain route (here, a clustering algorithm is applied to separate different route data) in the FOM maneuvering circle, and then the planar flight part corresponding to the route is determined, and then the maneuvering part is determined;
step S4, according to the formula
Figure BDA00029553997800000510
Obtaining the clustering center of FOM calibration circle as csCluster data of
Figure BDA00029553997800000511
Step S5, calibrating FOM to circle DsDeleting the turning data far away from the clustering center to obtain effective clustering data of FOM calibration circles with different courses
Figure BDA00029553997800000512
Step S6, according to the formula
Figure BDA00029553997800000513
Calculating posterior probability
Figure BDA00029553997800000514
The purpose of step S6 is to calculate which Gaussian mixture model belongs to, here
Figure BDA00029553997800000515
The magnetic field data of a FOM motor coil (also called FOM calibration coil) in a certain direction;
step S7, will satisfy
Figure BDA0002955399780000061
As maneuver data for different headings of the calibration circle, wherein ThIs a preset threshold value;
and step S8, calculating the sum of the peak value and the peak value of the obtained maneuvering data, and taking the sum as the value of the compensation effect evaluation index FOM of the calibration loop.
As the preferred embodiment of the invention, the different headings and the different sampling points correspond to each other
Figure BDA0002955399780000062
The values of the magnetic field are equal, the geomagnetic field inclination angles corresponding to different courses and different sampling points are different
Figure BDA0002955399780000063
Are equal in value.
The automatic evaluation method for the aeromagnetic compensation calibration quality in the embodiment has the following principle:
the vector magnetic field measured by the magnetometer sensor is the projection of the geomagnetic field on the three main axes of the aviation platform. Let the component parallel to the horizontal axis of the platform be X, the component parallel to the longitudinal axis of the platform be Y, and the component perpendicular to the horizontal plane be Z. The inclination of the geomagnetic field H is recorded as φ, and the heading angle is θ, defined as the angle from north to the Y-axis. Thus, the three components of the total field can be expressed as:
Figure BDA0002955399780000064
where H is the data measured by the total field magnetometer, H can be viewed approximately as the vector sum of X, Y, Z three-direction magnetic field data measured by the three-component magnetometer, and H is the scalar form of H.
For a data set with n sample points, use ai＝(Xi,Yi)T(i ═ 1,2, …, n) denotes the set of X and Y components. According to the formula (1), a can be obtainedi＝Hi cosφi(sinθi,cosθi)T。
Assuming that the maneuvering coil follows four headings of north, east, south and west in turn, the four different headings correspond to each other (sin theta)i,cosθi) Convergence into four parts. Since the flight area is limited, H and φ can be considered constant. Thus, aiThe range of variation at a certain heading is also small. Clustering methods can be used to distinguish data corresponding to different headings, such as k-means algorithm.
Suppose heading s contains nsA sampling point corresponding to a magnetic field component of
Figure BDA0002955399780000065
The cluster center can be expressed as:
Figure BDA0002955399780000066
wherein D issA cluster of sampled data representing a heading s, the cluster comprising nsA number of sample points are sampled at the time of sampling,
Figure BDA0002955399780000067
is DsThe data corresponding to the ith sampling point.
And (4) iteratively calculating according to the Euler distance from each sampling data to the cluster center until the cluster center is not changed any more. Finally, deleting the turning data far away from the clustering center to obtain clusters of sampling data representing different courses, wherein the corresponding magnetic field characteristics are
Figure BDA0002955399780000071
Wherein lsAnd rsIs the number of samples removed.
During a calibration flight, the aerial platform performs three sets of maneuvers (roll, pitch, yaw) at each heading, which cause changes in the magnetometer output signals. When the aviation platform executes the maneuvering action, the corresponding data are dispersed, and the data distribution is very concentrated in the flat flight. Thus, if the data distribution in the time of a flat flight is known, the maneuver can be indirectly identified. A gaussian mixture model can be used to fit the data distribution in the time of flight with the maneuver identification principle as shown in fig. 1.
To more accurately characterize the maneuver data, the Z component is also considered and willCombination of three components (X)i,Yi,Zi)TIs marked as
Figure BDA0002955399780000072
For the three-component magnetic field characteristic of the heading s, the Gaussian mixture density is expressed as:
Figure BDA0002955399780000073
where k is a constant representing the number of gaussian distributions.
Figure BDA0002955399780000074
And
Figure BDA0002955399780000075
respectively mean and covariance matrix of j-th Gaussian distribution of course s, wjIs to satisfy
Figure BDA0002955399780000076
Constrained blending weights. Gaussian mixture model parameter corresponding to horizontal flight data of course s
Figure BDA0002955399780000077
Can be estimated using EM algorithms.
And identifying the data segment of the aviation platform in level flight at each heading by using the trained GMM model.
The posterior probability of the level flight data segment corresponding to the course s accords with the following decision function:
Figure BDA0002955399780000078
assume a priori probabilities of all headings (i.e., p (G)s) Equal (i.e., the probability of each course of the FOM moving coil occurring is equal), uniform magnetic field distribution, then p (G)s) And
Figure BDA0002955399780000079
which can be considered as a constant, the decision function can be simplified to:
Figure BDA00029553997800000710
for each heading of a calibration flight, the data for an aircraft in flat flight is generally satisfied
Figure BDA00029553997800000711
Wherein T ishA very small constant close to 0. According to the above process, the maneuver portions in the respective headings of the calibration flight can be automatically identified.
In summary, the method of the embodiment first obtains a maneuvering segment in each heading of the maneuvering coil; and obtaining a total field by a total field magnetometer, and corresponding the obtained maneuvering section to the total field to obtain the value of the compensation effect evaluation index FOM of the calibration ring.
The automatic assessment method for the aeromagnetic compensation calibration quality in the embodiment comprises the following steps:
firstly, mounting a three-component magnetometer and a total field magnetometer on an airplane;
secondly, enabling the plane to finish plane flight in four orthogonal directions (such as north, east, south and west);
thirdly, obtaining a flat flying ring clustering center c according to a formula (2)sCluster data of
Figure BDA0002955399780000081
Deleting the turning data far away from the clustering center to obtain effective clustering data of different courses of the flying circle
Figure BDA0002955399780000082
Fourthly, obtaining a Gaussian mixture model corresponding to four courses of the flat flying ring according to a formula (3), wherein the model parameters are
Figure BDA0002955399780000083
Fifthly, enabling the airplane to finish FOM calibration circle flight in four orthogonal directions (such as north, east, south and west);
sixthly, obtaining the clustering center c of the FOM calibration circle according to the formula (2)sCluster data of
Figure BDA0002955399780000084
Removing the turning data far away from the clustering center to obtain effective clustering data of FOM calibration circles with different courses
Figure BDA0002955399780000085
Seventh, the posterior probability is calculated according to the formula (5)
Figure BDA0002955399780000086
Will be provided with
Figure BDA0002955399780000087
With a predetermined threshold value ThMaking comparison, and is greater than threshold value ThIs/are as follows
Figure BDA0002955399780000088
The corresponding data is the level flight data of the calibration ring, and the rest data is the maneuvering data of the calibration ring;
and step eight, calculating the sum of the peak value and the peak value of the maneuvering data obtained in the step one, and taking the sum as the value of the evaluation index FOM of the magnetic compensation calibration quality of the calibration ring.
The present embodiment provides an apparatus for automatically evaluating the quality of an aeromagnetic compensation calibration, as shown in fig. 3, the apparatus may generally include:
a first clustering module 1 configured to calculate a first cluster value
Figure BDA0002955399780000089
Obtaining the clustering center of the flat flying ring as csCluster data of
Figure BDA00029553997800000810
Wherein m represents the number of headings contained in the flying circle, DsClustering of sampled data representing heading s, nsRepresents DsThe number of sample points that are involved,
Figure BDA00029553997800000811
is DsData corresponding to the ith sampling point;
a first deletion module 2 configured to delete the flying circle DsDeleting the turning data far away from the clustering center to obtain effective clustering data of different courses of the flat flying ring
Figure BDA0002955399780000091
Wherein lsAnd rsRespectively representing the number of deleted sampling points at two ends of the course s;
a Gaussian mixture model obtaining module 3 configured to obtain a model from a formula
Figure BDA0002955399780000092
Obtaining a Gaussian mixture model corresponding to each course of the flat flying ring, wherein p (b)s|Gs) Denotes the Gaussian mixture density, bsElement (1) of
Figure BDA0002955399780000093
Representing a combination of X, Y and Z three-component magnetic field characteristics in a rectangular spatial coordinate system, X representing a direction parallel to the transverse axis of the platform, Y representing a direction parallel to the longitudinal axis of the platform, Z representing a direction perpendicular to the horizontal plane, GsThe parameters of the gaussian model are represented by,
Figure BDA0002955399780000094
wherein the content of the first and second substances,
Figure BDA0002955399780000095
is that the heading s satisfies the constraint
Figure BDA0002955399780000096
K represents highThe number of the distribution of the gaussian components,
Figure BDA0002955399780000097
and
Figure BDA0002955399780000098
respectively is the mean and covariance matrix of the jth Gaussian distribution of the course s;
a second clustering module 4 configured to calculate a cluster of clusters based on the formula
Figure BDA0002955399780000099
Obtaining the clustering center of FOM calibration circle as csCluster data of
Figure BDA00029553997800000910
A second deletion module 5 configured to calibrate the FOM by a circle DsDeleting the turning data far away from the clustering center to obtain effective clustering data of FOM calibration circles with different courses
Figure BDA00029553997800000911
A posterior probability calculation module 6 configured to calculate the posterior probability according to the formula
Figure BDA00029553997800000912
Calculating posterior probability
Figure BDA00029553997800000913
A comparison module 7 configured to satisfy
Figure BDA00029553997800000914
As maneuver data for different headings of the calibration circle, wherein ThIs a preset threshold value; and
and the FOM calculation module 8 is configured to calculate the sum of peak values and peak values of the obtained maneuvering data as the value of the compensation effect evaluation index FOM of the calibration loop.
In a preferred embodiment of the present invention, in the first clustering module,
Figure BDA00029553997800000915
wherein the content of the first and second substances,
Figure BDA00029553997800000916
representing the scalar version of the earth's magnetic field H corresponding to the ith sample point of the heading s,
Figure BDA00029553997800000917
indicating the heading angle corresponding to the ith sampling point of the heading s,
Figure BDA0002955399780000101
and representing the inclination angle of the geomagnetic field corresponding to the ith sampling point of the heading s.
As a preferred embodiment of the present invention, the Gaussian mixture model parameter GsAnd estimating by using an EM algorithm.
As the preferred embodiment of the invention, the different headings and the different sampling points correspond to each other
Figure BDA0002955399780000102
The values of the magnetic field are equal, the geomagnetic field inclination angles corresponding to different courses and different sampling points are different
Figure BDA0002955399780000103
Are equal in value.
The automatic evaluation device for the aeromagnetic compensation calibration quality of the present embodiment can implement the steps of the automatic evaluation method for the aeromagnetic compensation calibration quality of the present embodiment, and the principle thereof is the same as that of the automatic evaluation method for the aeromagnetic compensation calibration quality of the present embodiment, and thus, the details thereof are not repeated.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed by a computer, cause the computer to perform, in whole or in part, the procedures or functions described in accordance with the embodiments of the application. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by a program, and the program may be stored in a computer-readable storage medium, where the storage medium is a non-transitory medium, such as a random access memory, a read only memory, a flash memory, a hard disk, a solid state disk, a magnetic tape (magnetic tape), a floppy disk (floppy disk), an optical disk (optical disk), and any combination thereof.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (8)

1. An automatic assessment method for aeromagnetic compensation calibration quality is characterized by comprising the following steps:
according to the formula
Figure FDA0002955399770000011
Obtaining the clustering center of the flat flying ring as csCluster data of
Figure FDA0002955399770000012
Wherein m represents the number of headings contained in the flying circle, DsClustering of sampled data representing heading s, nsRepresents DsThe number of sample points that are involved,
Figure FDA0002955399770000013
is DsData corresponding to the ith sampling point;
will fly round DsDeleting the turning data far away from the clustering center to obtain effective clustering data of different courses of the flat flying ring
Figure FDA0002955399770000014
Wherein lsAnd rsRespectively representing the number of deleted sampling points at two ends of the course s;
according to the formula
Figure FDA0002955399770000015
Obtaining a Gaussian mixture model corresponding to each course of the flat flying ring, wherein p (b)s|Gs) Representing a mixture of Gaussian mixture densitiesDegree b, bsElement (1) of
Figure FDA0002955399770000016
Representing a combination of X, Y and Z three-component magnetic field characteristics in a rectangular spatial coordinate system, X representing a direction parallel to the transverse axis of the platform, Y representing a direction parallel to the longitudinal axis of the platform, Z representing a direction perpendicular to the horizontal plane, GsThe parameters of the gaussian model are represented by,
Figure FDA0002955399770000017
wherein the content of the first and second substances,
Figure FDA0002955399770000018
is that the heading s satisfies the constraint
Figure FDA0002955399770000019
K represents the number of gaussian distributions,
Figure FDA00029553997700000110
and
Figure FDA00029553997700000111
respectively is the mean and covariance matrix of the jth Gaussian distribution of the course s;
according to the formula
Figure FDA00029553997700000112
Obtaining the clustering center of FOM calibration circle as csCluster data of
Figure FDA00029553997700000113
Aligning FOM with circle DsDeleting the turning data far away from the clustering center to obtain effective clustering data of FOM calibration circles with different courses
Figure FDA00029553997700000114
According to the formula
Figure FDA00029553997700000115
Calculating posterior probability
Figure FDA00029553997700000116
Will satisfy
Figure FDA00029553997700000117
As maneuver data for different headings of the calibration circle, wherein ThIs a preset threshold value;
and calculating the sum of the peak value and the peak value of the obtained maneuvering data, and taking the sum as the value of the compensation effect evaluation index FOM of the calibration loop.
2. The method of claim 1,
Figure FDA00029553997700000118
wherein the content of the first and second substances,
Figure FDA00029553997700000119
representing the scalar version of the earth's magnetic field H corresponding to the ith sample point of the heading s,
Figure FDA0002955399770000021
indicating the heading angle corresponding to the ith sampling point of the heading s,
Figure FDA0002955399770000022
and representing the inclination angle of the geomagnetic field corresponding to the ith sampling point of the heading s.
3. The method of claim 1, wherein the Gaussian mixture model parameter GsAnd estimating by using an EM algorithm.
4. The method of claim 1, wherein different headings and different sampling points correspond to each other
Figure FDA0002955399770000023
The values of the magnetic field are equal, the geomagnetic field inclination angles corresponding to different courses and different sampling points are different
Figure FDA0002955399770000024
Are equal in value.
5. An apparatus for automatically evaluating the quality of an aeromagnetic compensation calibration, comprising:
a first clustering module configured to calculate a first cluster value based on a formula
Figure FDA0002955399770000025
Obtaining the clustering center of the flat flying ring as csCluster data of
Figure FDA0002955399770000026
Wherein m represents the number of headings contained in the flying circle, DsClustering of sampled data representing heading s, nsRepresents DsThe number of sample points that are involved,
Figure FDA0002955399770000027
is DsData corresponding to the ith sampling point;
a first deletion module configured to delete the flying circle DsDeleting the turning data far away from the clustering center to obtain effective clustering data of different courses of the flat flying ring
Figure FDA0002955399770000028
Wherein lsAnd rsRespectively representing the number of deleted sampling points at two ends of the course s;
a Gaussian mixture model obtaining module configured to obtain a model from a formula
Figure FDA0002955399770000029
Obtaining a Gaussian mixture model corresponding to each course of the flat flying ring, wherein,p(bs|Gs) Denotes the Gaussian mixture density, bsElement (1) of
Figure FDA00029553997700000210
Representing a combination of X, Y and Z three-component magnetic field characteristics in a rectangular spatial coordinate system, X representing a direction parallel to the transverse axis of the platform, Y representing a direction parallel to the longitudinal axis of the platform, Z representing a direction perpendicular to the horizontal plane, GsThe parameters of the gaussian model are represented by,
Figure FDA00029553997700000211
wherein the content of the first and second substances,
Figure FDA00029553997700000212
is that the heading s satisfies the constraint
Figure FDA00029553997700000213
K represents the number of gaussian distributions,
Figure FDA00029553997700000214
and
Figure FDA00029553997700000215
respectively is the mean and covariance matrix of the jth Gaussian distribution of the course s;
a second clustering module configured to be based on a formula
Figure FDA00029553997700000216
Obtaining the clustering center of FOM calibration circle as csCluster data of
Figure FDA00029553997700000217
A second deletion module configured to calibrate the FOM by a circle DsDeleting the turning data far away from the clustering center to obtain effective clustering data of FOM calibration circles with different courses
Figure FDA0002955399770000031
A posterior probability calculation module configured to calculate a posterior probability based on the formula
Figure FDA0002955399770000032
Calculating the posterior probability p (b)i s|Gs)；
A comparison module configured to satisfy
Figure FDA0002955399770000033
As maneuver data for different headings of the calibration circle, wherein ThIs a preset threshold value;
and the FOM calculation module is configured to calculate the sum of peak values and peak values of the obtained maneuvering data, and the sum is used as the value of the compensation effect evaluation index FOM of the calibration loop.
6. The apparatus of claim 5, wherein in the first clustering module,
Figure FDA0002955399770000034
wherein the content of the first and second substances,
Figure FDA0002955399770000035
representing the scalar version of the earth's magnetic field H corresponding to the ith sample point of the heading s,
Figure FDA0002955399770000036
indicating the heading angle corresponding to the ith sampling point of the heading s,
Figure FDA0002955399770000037
and representing the inclination angle of the geomagnetic field corresponding to the ith sampling point of the heading s.
7. The apparatus of claim 5, wherein the Gaussian mixture model parameter GsAnd estimating by using an EM algorithm.
8. The device of claim 5, wherein different headings and different sampling points correspond to each other
Figure FDA0002955399770000038
The values of the magnetic field are equal, the geomagnetic field inclination angles corresponding to different courses and different sampling points are different
Figure FDA0002955399770000039
Are equal in value.
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