CN115017386A - Cluster-based observation data and target library root association method and device - Google Patents
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Abstract
A method and a device for correlating observed data and target library root based on clustering are disclosed, firstly, acquiring the track root of a spatial target at a first moment according to the observed data of the spatial target; secondly, converting the number of the tracks of the space target at the first moment into the number of the tracks at the second moment; then, 3 parameters are selected from the number of the tracks to form two parameter spaces; finally, performing twice clustering operation on the track number of the space target at the second moment and the track number of the cataloged targets in the preset target library at the second moment in two parameter spaces in sequence, so as to find the cataloged targets related to the space target in the preset target library; therefore, the invention forms a parameter space for clustering operation through 3 parameters in the number of the tracks, has good compatibility, high running speed, simple and visual result and is easy for engineering realization.
Description
Technical Field
The invention relates to the field of astronomy, in particular to a method and a device for correlating observed data and a target library root number based on clustering.
Background
Along with the rapid development of the aerospace technology and the space application field, the number of on-orbit spacecrafts is more and more, the situation of severe space operation safety is also faced, and meanwhile, certain requirements are put forward for space target cataloging. In the spatial target cataloging work, the observed data needs to be associated with the number of roots in the target library in order to find the cataloged targets corresponding to the observed data.
Therefore, how to accurately and quickly correlate the observation data with the number of the roots in the cataloging library is a problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
The invention mainly solves the technical problem of accurately and quickly correlating the observation data with the target library root number.
According to a first aspect, an embodiment provides a method for associating cluster-based observation data with a target library root, including:
acquiring the number of tracks of a space target at a first moment; the number of the tracks comprises 6 parameters, and the 6 parameters are used for representing the track information of the space target;
converting the track number of the space target at a first moment into a track number corresponding to a preset target library at a second moment; the preset target library comprises the track number of a plurality of catalogued targets at a second moment;
selecting 3 parameters from 6 parameters in the number of the tracks as a parameter space;
and performing clustering operation on the track root of the space target at the second moment and the track root of a plurality of catalogued targets in the preset target library at the second moment in the parameter space to obtain the catalogued targets related to the space target in the preset target library.
In one embodiment, performing a clustering operation on the number of tracks of the spatial target at the second time and the number of tracks of the plurality of cataloged targets in the preset target library at the second time in the parameter space includes:
performing primary clustering operation on the track root of the space target at the second moment and the track root of a plurality of cataloged targets in a preset target library at the second moment in the parameter space, and acquiring the cataloged targets in the preset target library, which belong to one class with the space target under the parameter space, as first clustering targets;
if the number of the first clustering targets is less than 1, outputting a target association failure result;
if the number of the first clustering targets is equal to or larger than 1, updating the parameter space;
performing primary clustering operation on the track root of the space target at the second moment and the track root of the first clustering target at the second moment in the updated parameter space, and acquiring a cataloged target which belongs to one class under the updated parameter space in the first clustering target and the space target as a second clustering target;
if the number of the second clustering targets is less than 1, outputting a target association failure result;
and if the number of the second clustering targets is equal to or larger than 1, taking the second clustering targets as the cataloged targets which are associated with the space targets in a preset target library.
In one embodiment, the updating the parameter space comprises:
and taking the remaining 3 parameters which are not contained in the current parameter space in the 6 parameters in the number of the tracks as the updated parameter space.
In one embodiment, the 6 parameters of the number of tracks comprise: semi-major axis, eccentricity, orbital inclination, elevation node longitude, isocenter argument and plano-isocenter angle.
In one embodiment, the clustering operation comprises: minkowski distance, euclidean distance, mahalanobis distance, cosine distance, and chebyshev distance.
According to a second aspect, an embodiment provides an apparatus for associating cluster-based observation data with a target library root, including:
the track number acquisition module is used for acquiring the track number of the space target at a first moment; the number of the tracks comprises 6 parameters, and the 6 parameters are used for representing the track information of the space target;
the track root conversion module is used for converting the track root of the space target at a first moment into the track root at a second moment corresponding to a preset target library; the preset target library comprises the track number of a plurality of cataloged targets at a second moment;
the parameter space forming module is used for selecting 3 parameters from 6 parameters in the number of the tracks as parameter space;
and the clustering module is used for performing clustering operation on the track root of the space target at the second moment and the track root of a plurality of cataloged targets in the preset target library at the second moment in the parameter space to obtain the cataloged targets related to the space target in the preset target library.
In one embodiment, performing a clustering operation on the number of tracks of the spatial target at the second time and the number of tracks of the plurality of cataloged targets in the preset target library at the second time in the parameter space includes:
performing primary clustering operation on the track root of the space target at a second moment and the track root of a plurality of cataloged targets in a preset target library at the second moment in the parameter space to obtain the cataloged targets in the preset target library and the space target which belong to one type in the parameter space as first clustering targets;
if the number of the first clustering targets is less than 1, outputting a target association failure result;
if the number of the first clustering targets is equal to or larger than 1, updating the parameter space;
performing primary clustering operation on the track root of the space target at the second moment and the track root of the first clustering target at the second moment in the updated parameter space, and acquiring a cataloged target which belongs to one class under the updated parameter space in the first clustering target and the space target as a second clustering target;
if the number of the second clustering targets is less than 1, outputting a target association failure result;
and if the number of the second clustering targets is equal to or larger than 1, taking the second clustering targets as the cataloged targets which are associated with the space targets in a preset target library.
In one embodiment, the updating the parameter space comprises:
and taking the rest 3 parameters which do not belong to the parameter space in the 6 parameters in the number of the tracks as the updated parameter space.
In one embodiment, the 6 parameters of the number of tracks comprise: semi-major axis, eccentricity, track inclination, elevation node longitude, apocenter argument and apocenter angle.
According to a second aspect, an embodiment provides a computer-readable storage medium having a program stored thereon, the program being executable by a processor to implement the method according to the above embodiment.
According to the method/device for associating the cluster-based observation data with the target library root number, firstly, the track root number of a space target at a first moment is obtained according to the observation data of the space target; secondly, converting the number of the tracks of the space target at the first moment into the number of the tracks at the second moment; then, 3 parameters are selected from the number of the tracks to form two parameter spaces; finally, performing twice clustering operation on the track number of the space target at the second moment and the track number of the cataloged targets in the preset target library at the second moment in two parameter spaces in sequence, so as to find the cataloged targets related to the space target in the preset target library; therefore, the invention forms a parameter space for clustering operation through 3 parameters in the number of the tracks, has good compatibility, high running speed, simple and visual result and is easy for engineering realization.
Drawings
FIG. 1 is a schematic structural diagram of an embodiment of an apparatus for associating cluster-based observation data with a target library root number;
FIG. 2 is a flowchart of an embodiment of a method for associating cluster-based observation data with a target library root;
FIG. 3 is a schematic diagram of a simulation scenario in which a ground radar observes a spatial target;
FIG. 4 is a simulated observation of the ground radar of FIG. 3 on a spatial target;
FIG. 5 is a diagram illustrating the results of a first clustering operation;
fig. 6 is a diagram illustrating the result of the second clustering operation.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings. Wherein like elements in different embodiments are numbered with like associated elements. In the following description, numerous details are set forth in order to provide a better understanding of the present application. However, those skilled in the art will readily recognize that some of the features may be omitted or replaced with other elements, materials, methods in different instances. In some instances, certain operations related to the present application have not been shown or described in detail in order to avoid obscuring the core of the present application from excessive description, and it is not necessary for those skilled in the art to describe these operations in detail, so that they may be fully understood from the description in the specification and the general knowledge in the art.
Furthermore, the described features, operations, or characteristics may be combined in any suitable manner to form various embodiments. Also, the various steps or actions in the method descriptions may be transposed or transposed in order, as will be apparent to one of ordinary skill in the art. Thus, the various sequences in the specification and drawings are for the purpose of describing certain embodiments only and are not intended to imply a required sequence unless otherwise indicated where such sequence must be followed.
The numbering of the components as such, e.g., "first", "second", etc., is used herein only to distinguish the objects as described, and does not have any sequential or technical meaning. The term "connected" and "coupled" when used in this application, unless otherwise indicated, includes both direct and indirect connections (couplings).
First, technical terms used in the present application will be explained:
the orbit number is six parameters necessary for describing a celestial body or a spacecraft under the action of Newton's law of motion and Newton's law of universal gravitation and determining the orbit of the spacecraft when the spacecraft moves on the Kepler orbit.
The space target refers to a celestial body or a spacecraft.
In the embodiment of the invention, two groups of parameter spaces are established by utilizing 6 parameters of the track number, each group of parameter spaces comprises 3 parameters in the track number, and the track number of the space target and the track number of the cataloged target in the preset target library are subjected to twice clustering operation in the two groups of parameter spaces respectively to obtain the cataloged target which is associated with the space target in the preset target library.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an embodiment of an apparatus for associating cluster-based observation data with a target base root, where the apparatus for associating cluster-based observation data with a target base root provided in this embodiment includes: the system comprises a track number acquisition module 101, a track number conversion module 102, a parameter space forming module 103 and a clustering module 104.
The track number obtaining module 101 is configured to obtain a track number of the space target at a first time; the number of the tracks comprises 6 parameters, and the 6 parameters are used for representing the track information of the space target. The 6 parameters of the number of the tracks are respectively a semi-major axis, eccentricity, track inclination angle, elevation point longitude, an apocenter spoke angle and an apocenter angle. In this embodiment, the number of tracks of the spatial target at the first time is calculated according to the observation data of the spatial target, and the calculation method may be: laplace (Laplace) method, gaussian (Gauss) method, and numerical method, etc. Wherein the observation data is usually acquired by a radar detection device or an optical detection device, for example: the observation data acquired by the radar detection device typically includes: time, distance, method, and pitch. The observation data acquired by the optical detection device generally include: time, longitude and latitude data, and declination data. The track calculation module 101 obtains 6 parameters of the number of tracks at a first time through observation data, where the first time is a current time corresponding to the observation data.
The track number conversion module 102 is configured to convert the track number of the space target at a first time into a track number at a second time corresponding to a preset target library; the preset target library comprises the track number of the plurality of cataloged targets at the second moment. Since the time corresponding to the number of tracks of the invented target stored in the preset target library is the second time, and the time corresponding to the number of tracks of the spatial target calculated according to the observation data is the first time, when the target track number is associated, the time corresponding to the number of tracks of the spatial target needs to be adjusted to the time corresponding to the number of tracks of the invented target in the preset target library, that is, the number of tracks of the spatial target at the first time is converted into the number of tracks at the second time. In this embodiment, conversion at different times of the number of tracks can be performed by using Runge-Kutta, SGP4, SDP4, GP4, SGP8, PPT2, PPT3, hand, SALT, SP, SST models or software.
The parameter space forming module 103 is configured to select 3 parameters from 6 parameters of the track number as a parameter space. In this embodiment, the parameter space is optionally combined with:
，
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，
，
，
，
，
，
，
，
，
，
，
，
，
，
，
，
，
. Wherein,
is a semi-long shaft, and is provided with a semi-long shaft,
in order to obtain the eccentricity ratio, the eccentric ratio,
in order to obtain the inclination angle of the track,
for the ascending point longitude to be the point of intersection longitude,
the radial angle of the proximal point is shown as the radial angle of the proximal point,
is a flat proximal angle.
The clustering module 104 is configured to perform clustering operation on the track number of the spatial target at the second time and the track numbers of the multiple cataloged targets in the preset target library at the second time in the parameter space, so as to obtain the cataloged targets in the preset target library, where the cataloged targets are associated with the spatial target.
In this embodiment, for convenience of description, a parameter space formed by optionally selecting 3 parameters from 6 parameters in the number of tracks for the first time is referred to as a first parameter space; the updated parameter space formed by the remaining three parameters from the 6 parameters in the track heel for the second time is called the second parameter space.
The following describes how to perform twice clustering operations in the first parameter space and the second parameter space based on the number of tracks of the spatial target at the second time and the number of tracks of the plurality of cataloged targets in the preset target library at the second time.
In an embodiment, performing a clustering operation on the number of tracks of the spatial target at the second time and the number of tracks of the plurality of cataloged targets in the preset target library at the second time in the parameter space includes:
and performing clustering operation on the track number of the space target at the second moment and the track numbers of a plurality of cataloged targets in the preset target library at the second moment in the first parameter space once, and acquiring the cataloged targets in the preset target library, which belong to one type with the space target in the parameter space, as the first clustering target. The first clustering operation performed in the first parameter space is to perform primary screening on a plurality of already-catalogued targets in the preset target library to obtain a primary screening result set, that is, the primary screening result set is the already-catalogued targets in the preset target library that can be grouped with the space targets into one type, and the primary screening result set includes one or more first clustering targets, in other words, the first clustering targets are the already-catalogued targets in the preset target library that can be grouped with the space targets into one type.
And if the number of the first clustering targets is less than 1, determining that the associated cataloged targets cannot be found in the preset target library, and outputting a target association failure result.
And if the number of the first clustering targets is equal to or larger than 1, updating the parameter space to obtain a second parameter space. In this embodiment, if the number of the first clustering targets is equal to 1, it indicates that there are 1 cataloged targets in the preset target library that may be clustered with the spatial targets into one type, and at this time, secondary screening needs to be performed in the second parameter space. If the number of the first clustering targets is greater than 1, it indicates that at least two cataloged targets possibly exist in the preset target library and can be clustered with the spatial target into one class, and at this time, secondary screening needs to be performed on the at least two cataloged targets. During secondary screening, the parameter space needs to be updated once, and the implementation selects the remaining three parameters not included in the first parameter space to form a second parameter space, for example: when the first parameter space is
Then secondThe parameter space may be
。
And performing second clustering operation on the track root of the space target at the second moment and the track root of the first clustering target at the second moment in a second parameter space, and acquiring the cataloged targets which belong to one type with the space target in the first clustering target in the second parameter space as second clustering targets. The clustering operation in the second parameter space is to perform secondary screening on the first clustering targets to find a cataloged target from at least one first clustering target as an associated target of the space target.
And if the number of the second clustering targets is less than 1, determining that the associated cataloged targets cannot be found in the preset target library, and outputting a target association failure result.
And if the number of the second clustering targets is equal to or larger than 1, taking the second clustering targets as the cataloged targets associated with the space targets in the preset target library, and outputting the second clustering targets marked as the associated targets.
Therefore, in the embodiment, the three-dimensional parameter space formed by 3 parameters in the number of the tracks is adopted for clustering operation, 6 parameters do not need to be considered at one time, the calculation complexity is reduced, and the calculation speed is increased; and moreover, the accuracy of calculation is improved by performing twice clustering operations in two different three-dimensional parameter spaces.
In this embodiment, the basic methods for performing clustering operations on the track number of the spatial target at the second time and the track number of the cataloged target in the preset target library in the first parameter space and the second parameter space are as follows: within a given space, a certain distance between data points is defined (e.g., Minkowski (Minkowski) distance, Euclidean (Euclidean) distance, Mahalanobis (Mahalanobis) distance, Cosine (Cosine) distance, Chebyshev (Chebyshev) distance), and then the data is classified into several data sets having the smallest distance, where the number of orbital elements belonging to the same class as the spatial target at the second time instant is the first or second clustered target.
Set parameter space of
Three parameters, wherein any three parameters of six parameters in the three parameters form two groups of parameters which are respectively
And
then, then
And
minkowski distance between two points
Comprises the following steps:
wherein,
the value of the distance parameter is 2 or 1.
setting the clustering threshold value to
When the distance between two points
The two points are considered to be of the same type, i.e. within this parameter space, the association is successful. If it is
The two points are not considered to be of the same type, i.e. within this parameter space, the association fails.
In the embodiment of the invention, firstly, the number of tracks of a space target at a first moment is obtained according to observation data of the space target; secondly, converting the number of the tracks of the space target at the first moment into the number of the tracks at the second moment; then, 3 parameters are selected from the number of the tracks to form two parameter spaces; finally, performing twice clustering operation on the track number of the space target at the second moment and the track number of the cataloged targets in the preset target library at the second moment in two parameter spaces in sequence, so as to find the cataloged targets related to the space target in the preset target library; therefore, the invention forms a parameter space through 3 parameters in the number of the tracks to perform clustering operation, has good compatibility, high running speed, simple and visual result and is easy to realize in engineering.
Referring to fig. 2, fig. 2 is a flowchart illustrating an embodiment of a method for associating cluster-based observation data with a target library root, where the method for associating cluster-based observation data with a target library root includes the following steps:
step 201: acquiring the number of tracks of a space target at a first moment; the number of the tracks comprises 6 parameters, the 6 parameters are respectively a semi-major axis, an eccentricity, a track inclination angle, a longitude of a rising intersection point, a radial angle of a near center point and a flat angle of the near center point, and the 6 parameters are used for representing track information of a space target.
Step 202: converting the track number of the space target at a first moment into a track number corresponding to a preset target library at a second moment; the preset target library comprises the track number of the plurality of cataloged targets at the second moment.
Step 203: and 3 optional parameters from 6 parameters in the number of the tracks are taken as a parameter space.
Step 204: and performing clustering operation on the track number of the space target at the second moment and the track numbers of the plurality of cataloged targets in the preset target library at the second moment in the parameter space to obtain the cataloged targets which are associated with the space target in the preset target library.
In an embodiment, in step 204, performing a clustering operation on the number of tracks of the spatial target at the second time and the number of tracks of the plurality of cataloged targets in the preset target library at the second time in the parameter space, includes:
and performing primary clustering operation on the track number of the space target at the second moment and the track numbers of a plurality of cataloged targets in the preset target library at the second moment in the parameter space, and acquiring the cataloged targets which belong to one type with the space target in the preset target library under the parameter space as first clustering targets.
And if the number of the first clustering targets is less than 1, determining that the associated cataloged targets cannot be found in the preset target library, and outputting a target association failure result.
And if the number of the first clustering targets is equal to or larger than 1, updating the parameter space to obtain a second parameter space. In this embodiment, if the number of the first clustering targets is equal to 1, it indicates that there are 1 cataloged targets in the preset target library that may be clustered with the spatial targets into one type, and at this time, secondary screening needs to be performed in the second parameter space. If the number of the first clustering targets is greater than 1, it indicates that at least two cataloged targets possibly exist in the preset target library and can be clustered with the spatial target into one class, and at this time, secondary screening needs to be performed on the at least two cataloged targets. During secondary screening, the parameter space needs to be updated once, and the implementation selects the remaining three parameters not included in the first parameter space to form a second parameter space, for example: when the first parameter space is, then the second parameter space may be.
And performing primary clustering operation on the track number of the space target at the second moment and the track number of the first clustering target at the second moment in the updated parameter space to obtain a cataloged target which belongs to the same class as the space target in the updated parameter space in the first clustering target as a second clustering target.
And if the number of the second clustering targets is less than 1, outputting a target association failure result.
And if the number of the second clustering targets is equal to or larger than 1, taking the second clustering targets as the cataloged targets which are associated with the space targets in the preset target library.
Referring to fig. 3 and fig. 4, fig. 3 is a schematic diagram of a simulation scene in which a ground radar observes a space target, and fig. 4 is simulation observation data of the ground radar of fig. 3 on the space target. Based on the observation data shown in fig. 4, the number of tracks of the space target at the second time can be obtained, 7 cataloged targets are arranged in the preset target library, three parameters, namely, a track inclination angle, a rising intersection declination and a near-location argument, are selected to form a parameter space, the number of tracks of the space target at the second time and the number of tracks of the 7 cataloged targets at the second time are subjected to clustering operation, the clustering operation result is shown in fig. 5, wherein 99999 is the space target, the other targets (41434, 44204, 43539, 44337, 44709, 45807 and 45344) are the cataloged targets, and the final clustering result is as follows: 41434. 44204 and 99999 are of one type, i.e., 41434 and 44204 are both first clustering targets. As the number of the first clustering targets is greater than 1, as shown in fig. 6, clustering operation is performed again on the track roots of the first clustering targets and the space targets in the updated parameter space composed of the semimajor axis, the eccentricity ratio and the approximate point angle, and the final clustering result is: 41434 and 99999 are one type, that is, 41434 is the associated target in the preset target library with the spatial target (99999).
Those skilled in the art will appreciate that all or part of the functions of the various methods in the above embodiments may be implemented by hardware, or may be implemented by computer programs. When all or part of the functions of the above embodiments are implemented by a computer program, the program may be stored in a computer-readable storage medium, and the storage medium may include: a read only memory, a random access memory, a magnetic disk, an optical disk, a hard disk, etc., and the program is executed by a computer to realize the above functions. For example, the program may be stored in a memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above can be implemented. In addition, when all or part of the functions in the above embodiments are implemented by a computer program, the program may be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash disk, or a removable hard disk, and may be downloaded or copied to a memory of a local device, or may be version-updated in a system of the local device, and when the program in the memory is executed by a processor, all or part of the functions in the above embodiments may be implemented.
The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For a person skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.
Claims (10)
1. A clustering-based observation data and target library root association method is characterized by comprising the following steps:
acquiring the number of tracks of a space target at a first moment; the number of the tracks comprises 6 parameters, and the 6 parameters are used for representing the track information of the space target;
converting the track number of the space target at a first moment into a track number corresponding to a preset target library at a second moment; the preset target library comprises the track number of a plurality of cataloged targets at a second moment;
selecting 3 parameters from 6 parameters in the number of the tracks as a parameter space;
and performing clustering operation on the track root of the space target at the second moment and the track root of a plurality of catalogued targets in the preset target library at the second moment in the parameter space to obtain the catalogued targets related to the space target in the preset target library.
2. The method as claimed in claim 1, wherein the clustering the orbit root of the spatial target at the second time and the orbit root of the plurality of cataloged targets in the preset target library at the second time in the parameter space comprises:
performing primary clustering operation on the track root of the space target at the second moment and the track root of a plurality of cataloged targets in a preset target library at the second moment in the parameter space, and acquiring the cataloged targets in the preset target library, which belong to one class with the space target under the parameter space, as first clustering targets;
if the number of the first clustering targets is less than 1, outputting a target association failure result;
if the number of the first clustering targets is equal to or larger than 1, updating the parameter space;
performing primary clustering operation on the track root of the space target at the second moment and the track root of the first clustering target at the second moment in the updated parameter space, and acquiring a cataloged target which belongs to one class under the updated parameter space in the first clustering target and the space target as a second clustering target;
if the number of the second clustering targets is less than 1, outputting a target association failure result;
and if the number of the second clustering targets is equal to or larger than 1, taking the second clustering targets as the cataloged targets which are associated with the space targets in a preset target library.
3. The method of claim 2, wherein said updating the parameter space comprises:
and taking the remaining 3 parameters which are not contained in the current parameter space in the 6 parameters in the number of the tracks as the updated parameter space.
4. The method of claim 1, wherein 6 parameters of the orbit root comprise: semi-major axis, eccentricity, orbital inclination, elevation node longitude, isocenter argument and plano-isocenter angle.
5. The method of claim 1 or 2, wherein the clustering operation comprises: minkowski distance, euclidean distance, mahalanobis distance, cosine distance, and chebyshev distance.
6. A device for associating cluster-based observation data with target library root numbers is characterized by comprising:
the track number acquisition module is used for acquiring the track number of the space target at a first moment; the number of the tracks comprises 6 parameters, and the 6 parameters are used for representing the track information of the space target;
the track root conversion module is used for converting the track root of the space target at a first moment into a track root at a second moment corresponding to a preset target library; the preset target library comprises the track number of a plurality of cataloged targets at a second moment;
the parameter space forming module is used for selecting 3 parameters from 6 parameters in the number of the tracks as parameter space;
and the clustering module is used for performing clustering operation on the track root of the space target at the second moment and the track root of a plurality of cataloged targets in the preset target library at the second moment in the parameter space to obtain the cataloged targets related to the space target in the preset target library.
7. The apparatus as claimed in claim 6, wherein the clustering operation is performed on the number of tracks of the spatial target at the second time and the number of tracks of the plurality of cataloged targets in the preset target library at the second time in the parameter space, and comprises:
performing primary clustering operation on the track root of the space target at the second moment and the track root of a plurality of cataloged targets in a preset target library at the second moment in the parameter space, and acquiring the cataloged targets in the preset target library, which belong to one class with the space target under the parameter space, as first clustering targets;
if the number of the first clustering targets is less than 1, outputting a target association failure result;
if the number of the first clustering targets is equal to or larger than 1, updating the parameter space;
performing primary clustering operation on the track root of the space target at the second moment and the track root of the first clustering target at the second moment in the updated parameter space, and acquiring a cataloged target which belongs to one class under the updated parameter space in the first clustering target and the space target as a second clustering target;
if the number of the second clustering targets is less than 1, outputting a target association failure result;
and if the number of the second clustering targets is equal to or larger than 1, taking the second clustering targets as the cataloged targets which are associated with the space targets in a preset target library.
8. The apparatus for associating cluster-based observation data with target library root of claim 7, wherein said updating the parameter space comprises:
and taking the remaining 3 parameters which do not belong to the parameter space in the 6 parameters in the number of the tracks as the updated parameter space.
9. The apparatus for correlating cluster-based observation data to target bank numbers according to claim 6, wherein 6 of the track numbers comprise: semi-major axis, eccentricity, orbital inclination, elevation node longitude, isocenter argument and plano-isocenter angle.
10. A computer-readable storage medium, characterized in that the medium has stored thereon a program which is executable by a processor to implement the method according to any one of claims 1-5.
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