CN115543637B

CN115543637B - Method and device for associating space targets and storage medium

Info

Publication number: CN115543637B
Application number: CN202211519425.4A
Authority: CN
Inventors: 吴凌根; 赵磊; 董玮; 吴新林; 何镇武; 吴琳琳; 陈倩茹
Original assignee: Emposat Co Ltd
Current assignee: Emposat Co Ltd
Priority date: 2022-11-30
Filing date: 2022-11-30
Publication date: 2023-03-31
Anticipated expiration: 2042-11-30
Also published as: CN115543637A

Abstract

The invention relates to the technical field of satellite measurement and control, and provides a correlation method, a device and a storage medium of a space target, wherein the method comprises the following steps: acquiring a space target inventory library and arc segment data observed by an observation station; opening up a thread of a GPU; each thread performs forecast calculation on the space target based on arc data corresponding to the space target in the arc data observed by the observation station aiming at the corresponding space target in the catalogued space targets to obtain the position data of the space target; and calculating the spherical distance between the catalogued space target and the arc segment data under the thread under each thread, and associating the arc segment data under the thread with the catalogued space target according to all the spherical distances so as to obtain the arc segment data which is successfully associated or the arc segment data which is failed to be associated. According to the scheme, the calculation work related to the determination and extrapolation of the initial orbit parameters can be omitted, and the efficiency of space target cataloging can be improved.

Description

Correlation method and device of space target and storage medium

Technical Field

The invention relates to the technical field of satellite measurement and control, in particular to a spatial target association method, a spatial target association device and a storage medium, and particularly relates to a spatial target association method, a spatial target association device and a storage medium which are suitable for GPU acceleration.

Background

The European Space Agency (ESA for short) estimates that the total number of Space targets with a size greater than 1cm exceeds 75 ten thousand, with the vast majority of Space targets being Space fragments without any utility. The space debris is widely distributed in the high, medium and low orbits of the earth, wherein the amount of the space debris positioned in the low orbit (LEO) accounts for 67.5 percent of the total amount. Very few of these spatial objects have been cataloged, even the largest global spatial object cataloging library, the cataloging library maintained by North American aviation space demand Command (NORAD), currently contains only about 2 thousand cataloged spatial objects, and most of them are larger than 10cm in size.

Unedited space debris can cause different degrees of damage and even functional failure of the spacecraft, and in fact debris of size 1cm can cause devastating impacts on a normally operating satellite. A large amount of Space debris also affects the normal operation of a running artificial satellite or spacecraft, and for example, an International Space Station (ISS) is taken as an example, in order to avoid the collision threat of the Space debris, the ISS has performed 25 orbital maneuvers (that is, the spacecraft actively changes the maneuvering flight of the original orbit purposefully according to the design). The increasing threat facing human space activities has attracted more and more attention to Space Situational Awareness (SSA), which can be roughly defined as the ability to perceive information about the space environment and space activities. The construction and maintenance of a larger-scale spatial target cataloguing library are an important development direction in the field of spatial situation perception.

In the related scheme, when a space target is cataloged, the initial orbit parameters are generally determined for each arc segment firstly, the space target or other initial orbit data in the cataloging library are matched, due to the fact that the data volume is large, the space targets are multiple, the calculation is time-consuming, the space target is not updated timely, and due to the fact that the initial orbit error is large, the possibility that the initial orbit parameter calculation fails exists, under the condition that problems occur again, manual intervention is needed, and the efficiency is low.

Therefore, it is highly desirable to develop a method, an apparatus and a storage medium for associating spatial targets, so as to solve the problem of low efficiency of spatial target cataloging due to large data volume and manual intervention required when problems occur, after determining initial track parameters for each arc segment, matching spatial targets or other initial track data in the cataloging library, and avoid calculation work for determining initial track parameters and extrapolating associations, thereby improving efficiency of spatial target cataloging.

Disclosure of Invention

The invention aims to provide a correlation method, a correlation device and a storage medium of a space target, which solve the problem of low space target cataloging efficiency due to large data volume and manual intervention required when problems occur by determining initial track parameters of arc sections and then matching the space target or other initial track data in a cataloging library when the space target is cataloged, and can avoid calculation work of determining the initial track parameters and extrapolating correlation, thereby improving the efficiency of the space target cataloging.

To solve the above technical problem, as an aspect of the present invention, there is provided a method for associating a spatial object, including the steps of:

acquiring a space target cataloging library and acquiring arc section data observed by an observation station;

under the condition that the GPU completes initialization, opening up a thread of the GPU; the number of the threads of the GPU is more than one, and the number of the threads of the GPU is more than or equal to the number of the space targets which are already cataloged in the space target cataloging library;

for each thread of more than one GPU, performing forecast calculation on a corresponding space target in the cataloged space targets in the space target cataloging library based on arc segment data corresponding to the space target in the arc segment data observed by the observation station to obtain position data of the space target;

and under each thread, calculating the spherical distance between the sorted space targets in the space target sorting library and the arc segment data under the thread, and associating the arc segment data under the thread with the sorted space targets according to the spherical distances with the same number as the sorted space targets so as to obtain the arc segment data which is successfully associated or the arc segment data which is failed to associate.

According to an example embodiment of the present invention, the arc segment data observed by the observation station includes: more than one arc segment data; each arc segment data comprising: time series, right ascension series, and declination series.

According to an example embodiment of the present invention, opening up a thread of the GPU includes:

storing the time sequence in the arc segment data observed by the observation station and TLE data of the spatial target in the spatial target cataloging library into a memory of the GPU, so that the GPU can be directly called and a CPU (central processing unit) cannot be called;

and opening up the number of the threads of the GPU so that the number of the threads of the GPU is larger than or equal to the number of the space targets which are already catalogued in the space target cataloguing library.

According to an example embodiment of the present invention, in the memory of the GPU, storage information of a catalogued space object is defined as shared content; and placing other sequences which are irrelevant to the time sequence in the arc segment data observed by the observation station into a register memory.

According to an exemplary embodiment of the present invention, for each of the more than one GPU threads, for a corresponding spatial target in the already-cataloged spatial targets in the spatial target cataloging library, performing forecast calculation on the spatial target based on arc data corresponding to the spatial target in the arc data observed by the observation station, to obtain position data of the spatial target, includes:

under each thread, according to TLE track information of a corresponding space target and a time sequence in corresponding arc segment data, SGP4 track prediction suitable for GPU acceleration is carried out, and position data of the corresponding space target are calculated;

and converting the position data of the satellite corresponding to the space target in the inertial coordinate system into right ascension and declination.

According to an exemplary embodiment of the present invention, in each thread, calculating a spherical distance between an already-cataloged spatial target in the spatial target cataloging library and arc segment data under the thread, and associating the arc segment data under the thread with the already-cataloged spatial target according to the spherical distances with the same number as the already-cataloged spatial targets to obtain successfully-associated arc segment data or unsuccessfully-associated arc segment data, includes:

under each thread, aiming at the time information in the time sequence of the arc segment data corresponding to the thread; extracting the position data of the corresponding space target under the thread under the time information; the position data under the time information comprises: right ascension and declination under the time information;

calculating observation data of the corresponding arc section under the time information according to the position data of the corresponding space target under the thread under the time information;

aiming at all threads, determining the distance between the observation data of the corresponding arc segment under all time information and the catalogued space target on the spherical surface, and recording as the spherical surface distance;

and according to all the spherical distances, associating the arc segment data under the thread with the catalogued space target to obtain the arc segment data with successful association or the arc segment data with failed association.

According to an exemplary embodiment of the present invention, associating the arc segment data under the thread with the catalogued space targets according to all spherical distances to obtain successfully associated arc segment data or unsuccessfully associated arc segment data, includes:

sequencing all spherical distances to obtain the minimum spherical distance;

extracting the catalogued space target corresponding to the minimum spherical distance, and converting the minimum spherical distance into the space distance based on the track height of the catalogued space target;

and if the spatial distance is smaller than the set distance threshold and the standard deviation of the spatial distance is smaller than the set standard deviation threshold, the association between the cataloged spatial target and the corresponding arc segment data is considered to be successful, otherwise, the association is considered to be unsuccessful, and the arc segment data with successful association or the arc segment data with failed association are obtained.

According to an example embodiment of the present invention, further comprising:

grouping the arc segment data successfully associated according to corresponding space targets in all the catalogued space targets;

and temporarily setting the arc segment data which is not successfully correlated as a new space target, and if the arc segment data which is not successfully correlated is still determined subsequently, determining the arc segment data which is not successfully correlated as the new space target.

As a second aspect of the present invention, there is provided an apparatus for associating a spatial object, comprising:

the acquisition unit is configured to acquire a space target inventory library and acquire arc segment data observed by the observation station;

the control unit is configured to open up a thread of the GPU under the condition that the GPU completes initialization; the number of the threads of the GPU is more than one, and the number of the threads of the GPU is more than or equal to the number of the space targets which are already cataloged in the space target cataloging library;

the control unit is further configured to perform forecast calculation on a spatial target in the spatial target inventory library for a corresponding spatial target in the already-inventorized spatial targets in each of the more than one GPU threads based on arc data corresponding to the spatial target in the arc data observed by the observation station, so as to obtain position data of the spatial target;

the control unit is further configured to calculate, under each thread, a spherical distance between an already-catalogued spatial target in the spatial target cataloging library and arc segment data under the thread, and associate the arc segment data under the thread with the already-catalogued spatial target according to the spherical distances with the same number as the already-catalogued spatial targets, so as to obtain successfully-associated arc segment data or unsuccessfully-associated arc segment data.

As a third aspect of the present invention, the present invention provides a storage medium comprising a stored program, wherein when the program runs, a device on which the storage medium is located is controlled to execute an association method that is the spatial target.

The invention has the beneficial effects that:

according to the method, TLE root information in the space target cataloging library is utilized to directly correlate data of each observation arc segment, so that the problem that space target cataloging efficiency is low due to the fact that space target or other initial track data in the cataloging library are matched after initial track parameters of each arc segment are determined when a space target is cataloged, manual intervention is needed when problems occur due to the fact that the data size is large, calculation work of determining the initial track parameters and extrapolating correlation can be omitted, and therefore the efficiency of space target cataloging can be improved.

Drawings

Fig. 1 schematically shows a step diagram of a method of associating spatial objects;

FIG. 2 is a diagram schematically illustrating the steps of tunneling the thread of the GPU in the method of associating spatial objects;

FIG. 3 is a diagram schematically illustrating steps of a forecast calculation for a spatial target in a correlation method for the spatial target;

FIG. 4 is a diagram schematically illustrating the steps of associating arc segment data under the thread with an already-catalogued space target in the association method of the space target;

FIG. 5 is a diagram schematically illustrating a step of associating arc segment data under the thread with an already catalogued space target according to all spherical distances in the association method for the space target;

FIG. 6 schematically illustrates a composition diagram of an apparatus for associating spatial objects;

FIG. 7 schematically illustrates a component diagram of GPU accelerated computing hardware;

fig. 8 schematically shows a step diagram of a spatial target association procedure suitable for GPU acceleration.

102-acquisition unit, 104-control unit.

Detailed Description

The following detailed description of embodiments of the invention, but the invention can be practiced in many different ways, as defined and covered by the claims.

In order to build and maintain a larger-scale space target cataloging library, the acquisition of monitoring data of space targets is the basis for space target cataloging. The optical observation has low cost, easy realization and wider application. The optical observation can only obtain the right ascension and the declination of the space target, or obtain the angle data of the space target, such as the altitude angle, the azimuth angle and the like, and has no distance information. The method comprises the following steps that two kinds of angle data, namely right ascension and declination of a space target, and an altitude angle and an azimuth angle of the space target, are obtained under different coordinate systems and are initial observation data for determining the position of the space target subsequently, and the simple understanding is that a spherical coordinate system is constructed by taking an optical observation instrument as a center, and two angles are used for representing the direction of the space target in the instrument; then the observation instrument can observe a period of time, namely similar to a camera, and take a picture at intervals, and a period of observed right ascension and declination, or an elevation angle and an azimuth angle can be obtained.

Because the LEO space target moves at a very fast speed, the observation arc section obtained by observing the LEO space target through the space-based or ground-based optics is often shorter. For the space target of space-based optical monitoring of the LEO, a satellite serving as a monitoring platform is generally in the LEO orbit, so that the relative operation speed between the satellite and the LEO is high, and the arc length of the obtained angle data is often short. Simulation experiments have shown that the majority of the observed arc is within 20s, and such arc segments with very short arc lengths are generally referred to as very short arcs.

For the very short arc angle data, the error of the initial orbit parameters obtained by the calculation of the initial orbit algorithm in the related scheme is large, and the initial orbit parameters cannot be directly catalogued, that is, the initial orbit parameters (or the corresponding angle observation data) cannot be further utilized. To utilize such initial orbit parameters (or corresponding angle observations), it is necessary to associate them with other initial orbit parameters, thereby creating a problem of association of the initial orbit parameters. The purpose of associating the initial orbit parameters is to determine whether two independent initial orbit parameters belong to the same target. Here, the determining whether two independent initial track parameters belong to the same target may specifically be: the initial orbit is expressed by 6 parameters, namely six elements of the orbit, and the two initial orbit parameters can be preliminarily judged to be not the same target by mainly judging whether the difference of the three parameters in the two initial orbit parameters is less than a given threshold value or not through three parameters (such as the semimajor axis represents the height, the ascent intersection ascent and the inclination represent the orbital plane, and the orbital plane is the plane of the satellite rotating around the earth) of the semimajor axis, the ascent intersection declination and the inclination; if the two initial orbit parameters belong to the same target, the orbit parameters with higher precision can be obtained by processing the angle data of the two sets of initial orbit parameters. With the increasing number of space fragments, the demand for cataloging of the space fragments is more urgent, which makes the association problem of the very short arc initial orbit data (i.e. initial orbit parameters) more and more important.

The very short arc means that the observed data are few, because the visual field of the optical observation space target is small, the observation time length is short, and the error is too large for determining the initial orbit parameter, the observed data of the same target on different instruments at different times need to be associated together to construct a large amount of data to determine the accurate orbit.

In the related scheme, the optical observation of the space target is a main means for associating and cataloging the space target, and the optical photography technology is utilized to directly or indirectly determine the angle (or direction) observed value of the target on an optical telescope Charge Coupled Device (CCD) image by utilizing the position of a star. The space debris has no light emitting property, but can reflect sunlight and be received by a ground optical observation system sensor, so that the space debris is one of effective means for detecting the space debris. However, because the visual angle is relatively small, the observation time is only about 20s for low-orbit satellites, and the single observation time can be within 1 to 2min for high-orbit satellites.

In the related scheme, data association is performed based on a very short arc section orbit determination method, and the process can be divided into the following steps:

step 11, calculating a possibly observed space target set according to the position of the optical equipment

。

Step 12, utilizing the observationsCalculating the obtained very short arc observation data to obtain initial orbit parameters, and comparing the initial orbit parameters with the space target set

The number of the tracks is calculated, the right ascension and the inclination difference of the ascending intersection point are calculated, the space target with the difference smaller than a given threshold value is extracted, and a set (or a set) is constructed>

。

Step 13, collecting

The space target in (1) predicts the observation time, and then calculates the position of the space target to be associated according to the initial orbit parameter in the observation data, but the calculation error is larger, and further calculates the set ^ and ^ based on the calculated position>

Comparing the position of the space target with the position of the space target to be associated, determining the nearest space target, analyzing whether the residual quantity is smaller than a threshold value, if the residual quantity is smaller than the given threshold value and only one space target smaller than the threshold value is provided, indicating that the association is successful, and if the residual quantity is larger than the given threshold value or larger than two space targets smaller than the given threshold value, failing to associate, and judging the space target to be a new space target.

Thus, the initial orbit parameters are compared with the initial orbit parameters in the target library or other observation data to carry out primary correlation processing, then the difference between the initial orbit parameters and the orbit parameters in the target library or other observation data in the directions of the orbit, the normal direction and the radial direction is analyzed, the same space target is considered when the difference is smaller than a given threshold value, a plurality of observation data are combined to obtain more accurate orbit data, and the more accurate orbit data are compared with the space target in the catalog library to finally complete the updating of the space target catalog library.

Considering that the number of the target-cataloged space targets is more than 2 ten thousand, the space monitoring network in the united states is the largest space situation perception network in the world at present, comprises two parts of a ground-based system and a space-based system, and can generate massive observation data by carrying out space detection tens of thousands of times every day. In the related scheme, the initial orbit parameters are generally determined for each arc segment, and the spatial targets or other initial orbit data in the inventory library are matched, so that the spatial targets are not updated timely due to large data volume and more spatial targets, such calculation is time-consuming, and the initial orbit error is large, the possibility of failure in calculation of the initial orbit parameters exists, and the efficiency is low due to the fact that manual intervention is needed under the condition that problems occur again. Therefore, the scheme of the invention provides a spatial target association method, a spatial target association device and a storage medium, and particularly provides a spatial target association method suitable for GPU acceleration, so that the problem of low spatial target cataloging efficiency due to the fact that the data volume is large and manual intervention is needed when problems occur due to the fact that the spatial target or other initial track data in a cataloging library are matched after the initial track parameters of each arc segment are determined when the spatial target is cataloged is solved, calculation work of determining and extrapolating association of the initial track parameters can be omitted, and therefore the efficiency of spatial target cataloging can be improved.

As a first embodiment of the present invention, there is provided a method for associating spatial objects, as shown in fig. 1, including the steps of: step S110 to step S140.

At step S110, a spatial target inventory library is obtained, and arc segment data observed by the observation station (e.g., extracted arc segment data of the observation station) is obtained.

In some embodiments, the arc segment data observed by the observation station in step S110 includes: more than one arc segment data. Each arc segment data comprising: time series, right ascension series, and declination series.

The scheme of the invention provides a space target association method suitable for GPU acceleration, in particular to a very short arc matching method based on GPU acceleration, which is suitable for target association without initial orbit parameters of GPU acceleration. Fig. 8 schematically shows a step diagram of a spatial target association procedure suitable for GPU acceleration. As shown in fig. 8, the spatial target association process applicable to GPU acceleration includes:

and step 21, acquiring a space target inventory library, and extracting arc section data of the observation station.

Arc segment data for an observation station, comprising: more than one arc segment data. Each arc segment data comprising: time series

(e.g., the time of each observation), the right ascension sequence @>

And a declination sequence>

. Wherein +>

Is the first->

A strip observation arc section with the value range of 1 to>

，/>

Is a positive integer. />

Is a data serial number with a value range of 1 to>

，/>

Is a positive integer.

Wherein the time sequence

(e.g., the time of each observation), the right ascension sequence @>

And a declination sequence>

The sequence length of each sequence in the method can be determined according to the observation duration and the observation frequency of each arc segment by the observation equipment. Here, each arc segment has a certain observation duration, which is determined primarily by the height of the spatial target and the observation range of the observation instrument. The observation frequency mainly refers to the observation frequency device of the observation device.

At step S120, in the event that the GPU completes initialization, the thread for the GPU is spawned. The number of the threads of the GPU is more than one, and the number of the threads of the GPU is larger than or equal to the number of the space targets which are already cataloged in the space target cataloging library.

In some embodiments, the specific process of opening up the thread of the GPU in step S120 is as follows.

The following further describes a specific process of opening up the thread of the GPU in step S120, with reference to a schematic flow chart of an embodiment of opening up the thread of the GPU in the method of the present invention shown in fig. 2, including: step S210 to step S220.

And S210, storing the time sequence in the arc segment data observed by the observation station and TLE data of the spatial target in the spatial target cataloging library into a memory of the GPU, so that the GPU can be directly called and a CPU (central processing unit) cannot be called.

In some embodiments, in step S210, in the memory of the GPU, the storage information of the cataloged space objects is defined as the shared content. And placing other sequences which are irrelevant to the time sequence in the arc segment data observed by the observation station into a register memory.

S220, opening up the number of the threads of the GPU so that the number of the threads of the GPU is larger than or equal to the number of the space targets which are already catalogued in the space target cataloguing library.

Specifically, in the scheme of the invention, the accelerated calculation based on the GPU is adopted. Fig. 7 schematically shows a composition diagram of the GPU acceleration computing hardware. As shown in fig. 7, the GPU accelerates the computing hardware, comprising: a GPU and a CPU. On the CPU side, the method comprises the following steps: a controller, an ALU (i.e., arithmetic logic unit), a Cache (i.e., cache), and a memory. On the GPU side, the method comprises the following steps: CUDA (computer Unified Device Architecture) and video memory.

The GPU is connected with the CPU by a PCI bus. Although the CPU has fewer computational cores, it can implement complex logic operations, and thus it is suitable for control-intensive tasks. In addition, the threads on the CPU are heavyweight and the context switch overhead is large, whereas the threads of the GPU are lightweight due to the presence of many cores.

The memory composition of the GPU has a certain particularity, and can be roughly classified into the following memory types, which can be referred to as the GPU memory classification table shown in table 1.

Table 1: GPU memory classification

The register memory and the shared memory are arranged in a chip, the access speed is very high, but the capacity is small, other memories are related to the size of a GPU (graphics processing unit) video memory, the access speed is low, and in GPU programming, the register memory and the shared memory are fully utilized as far as possible, so that the time consumption of data access is reduced, and the parallel efficiency is improved.

With reference to the example shown in fig. 8, the time-series data of the arc segment data and the space target TLE data are copied to the GPU memory (the GPU can only call the GPU memory data, and the CPU memory cannot call the GPU memory, so the data are copied to the GPU memory first), the number of GPU threads is developed, and the total number of threads is required to be an integral multiple of 32 due to the limitation of hardware, so that the GPU thread number can be developed here

And (4) rounding upwards for each thread, and ensuring that the number of the opened threads is more than or equal to the number of the space targets.

For example: the thread of the GPU comprises: and L is a positive integer and is the same as the number of the spatial targets which are already catalogued in the spatial target cataloguing library. And performing parallel computation on the first thread to the lth thread, wherein for each thread from the first thread to the lth thread, for a corresponding spatial target in the L spatial targets cataloged in the spatial target cataloging library, performing forecast computation on the spatial target based on arc data corresponding to the spatial target in the arc data observed by the observation station, and obtaining position data of the spatial target. In this way, through the first thread to the lth thread, the parallel forecasting calculation of the L space targets is realized, and the position data of the L space targets is obtained.

In some embodiments, in step S220, in combination with the example shown in fig. 8, parallelizing the task of satellite prediction may specifically include: the time sequence of the observed data is

The total number of observation data is>

The number of the classified space targets is->

All spatial objects are predicted to the observation data point in time, i.e. need to be->

(i.e. is->

*/>

) Sub-computation, for parallel computation based on GPU, here subdivided according to space target, one thread block responsible for computation of one space targetForecast is based on>

The number of sub-bus and bus-line is>

. Because the GPU parallel computation is different from the CPU parallel computation, the time cost for acquiring variable data in the GPU computation needs to be considered, data exchange with a memory needs to be reduced as much as possible in each thread, and the use of a global memory, a constant memory, and the like of the GPU is avoided. Therefore, in the scheme of the invention, the cataloged space target information is defined as a shared memory (the cataloged space target data needs to be reused in the forecast calculation and is defined as the shared memory to improve the use efficiency), and the intermediate variable irrelevant to time in the track forecast is placed in the register memory, so that the acceleration efficiency is improved through reasonable GPU video memory distribution.

In step S130, for each of the more than one GPU threads, for a corresponding spatial target in the already-cataloged spatial targets in the spatial target cataloging library, performing forecast calculation on the spatial target based on arc data corresponding to the spatial target in the arc data observed by the observation station, so as to obtain position data of the spatial target. In this way, through more than one thread of the GPU, parallel forecasting calculation of the spatial target which is already cataloged in the spatial target cataloging library is achieved, and the position data of the spatial target which is already cataloged in the spatial target cataloging library is obtained.

In some embodiments, in step S130, for each of the more than one GPU threads, for a corresponding spatial target in the already-cataloged spatial targets in the spatial target cataloging library, based on arc segment data corresponding to the spatial target in the arc segment data observed by the observation station, performing a forecast calculation on the spatial target to obtain position data of the spatial target, see the following exemplary description.

The following further describes a specific process of performing the forecast calculation on the spatial target in step S130, with reference to a flowchart of an embodiment of performing the forecast calculation on the spatial target in the method of the present invention shown in fig. 3, including: step S310 to step S320.

S310, under each thread, according to TLE track information of the corresponding space target and the time sequence in the corresponding arc segment data, SGP4 track forecast applicable to GPU acceleration is carried out, and position data of the corresponding space target are calculated.

And S320, converting the position data of the satellite corresponding to the space target in the inertial coordinate system into right ascension and declination.

Specifically, as shown in fig. 8, the spatial target association process applicable to GPU acceleration further includes:

and step 22, finishing initialization by the GPU, performing forecast calculation on a developed thread (such as thread 1, thread 2, … …, thread L and the like), and performing SGP4 track forecast suitable for GPU acceleration according to the extracted arc segment data of the observation station. The method comprises the following steps: and forecasting the time of observing arc data for a corresponding space target based on each GPU thread, and converting the position in the inertial coordinate system into right ascension and declination data.

The SGP4 orbit prediction is an analytic method orbit prediction method, and inputs parameters are TLE and a prediction time, and satellite position and velocity information (i.e. satellite ephemeris) at the time can be obtained through calculation. Wherein, TLE in the input parameter is the track information in the space target cataloging library. The forecast time in the input parameter is a time series in the arc data of the observation station

(e.g., the time of each observed data). Right ascension sequence in arc segment data of an observation station->

And the declination sequence->

The method is obtained by matching the right ascension and the right ascension predicted by TLE with the observed right ascension and right ascension.

In step 22, because the SGP4 orbit prediction is adopted, the orbit prediction calculation at each time is independent, and the satellites are also independent, the task of satellite prediction can be parallelized. Here, the scheme of the present invention is mainly for adapting to the operation of a Graphics Processing Unit (GPU), and an initial orbit parameter is not constructed by using observation data. In the related scheme, when the initial orbit parameters are constructed by using the observation data in the process of cataloging the space targets, on one hand, the errors are large, and on the other hand, some calculation contents cannot be paralleled in the correlation process. According to the scheme, SGP4 orbit prediction suitable for GPU acceleration is sampled, so that the correlation of air conditioner targets can be realized by adopting a completely parallel means, and the efficiency of space target cataloging can be improved.

Specifically, the parallel flow in step 22 can be expressed as: each thread is responsible for the calculation work of a space target, and the position of the space target in the observation time is calculated and converted into the right ascension and the declination according to the time sequence of the arc data.

After the SGP4 orbit prediction work is completed, the position information of the satellite in the inertial coordinate system needs to be converted into right ascension and declination:

。

wherein, the first and the second end of the pipe are connected with each other,

for the position of the satellite in the inertial frame, <' >>

For the position of the observation station in the inertial frame, in combination with>

Is the red meridian->

It is declination.

At step S140, under each thread, calculating a spherical distance between the cataloged spatial targets in the spatial target cataloging library and the arc segment data under the thread, and associating the arc segment data under the thread with the cataloged spatial targets according to the spherical distances with the same number as the cataloged spatial targets, so as to obtain the arc segment data successfully associated or the arc segment data failed to be associated.

In the scheme of the invention, a space target association method suitable for GPU (graphic processing unit, abbreviated as "GPU") acceleration is characterized in that Two Lines of Element (TLE) orbit information in a space target inventory library is utilized to calculate theoretical observed values (such as right ascension and declination of a space target) of a satellite under a given optical observation instrument, then association matching is carried out by combining an actual observed value and the theoretical observed values to determine the space target in the space target inventory library to which an arc segment belongs, and if an unmatched arc segment is found, a new space target can be judged, so that the space target inventory efficiency can be improved in the association process of space target optical measurement data.

In some embodiments, in step S140, under each thread, a spherical distance between an already-cataloged spatial target in the spatial target cataloging library and arc segment data under the thread is calculated, and according to the same number of spherical distances as the number of already-cataloged spatial targets, the arc segment data under the thread and the already-cataloged spatial target are associated to obtain a specific process of associating successful arc segment data or associating failed arc segment data, see the following exemplary description.

With reference to the schematic flow chart of an embodiment of associating the arc data under the thread with the catalogued space target in the method of the present invention shown in fig. 4, a specific process of associating the arc data under the thread with the catalogued space target in step S140 is further described, which includes: step S410 to step S440.

Step S410, under each thread, aiming at the time information in the time series of the arc segment data corresponding to the thread. And extracting the position data of the corresponding space target under the thread under the time information. The position data under the time information comprises: right ascension and declination under this time information.

Step S420, calculating the observation data of the corresponding arc segment under the time information according to the position data of the corresponding space target under the thread under the time information.

Step S430, aiming at all threads, according to the observation data of the corresponding arc segments under all time information, determining the distance between the observation data of the corresponding arc segments under all time information and the sorted space target on the spherical surface, and recording as the spherical surface distance.

And step S440, according to all the spherical distances, associating the arc segment data under the thread with the catalogued space targets to obtain successfully associated arc segment data or unsuccessfully associated arc segment data.

Specifically, as shown in fig. 8, the spatial target association process applicable to GPU acceleration further includes: step 23, the spatial target association method applicable to GPU acceleration does not rely on using the initial orbit information (i.e., initial orbit parameters) of the arc segment of the observation data, but directly associates from the observation data, and can convert the association problem into a sequence matching problem on a spherical surface with the earth as the center of sphere and the radius as the unit length, thereby avoiding an error caused by calculating the initial orbit using the observation data. The specific association flow is as follows:

231, performing linear fitting on arc data corresponding to each thread under each thread, extracting the right ascension and the declination of the cataloged space target at the time in the space target cataloging library according to the time information of the arc data, extracting the arc observation data, and determining the distance between the observation data of each time point of the arc data and the forecast data of the cataloged space target on the spherical surface, namely the spherical surface distance

Dividing the distance into in the direction of the arc section>

And a vertical arc direction->

Two parts, because the forecast of the target in the cataloging space has a certain error and the speed along the speed direction is larger than the error along other directions, because a weight with the value of 10 is applied to the distance in the vertical arc section direction, the distance is defined as being/based on the weight processing>

Calculating the average distance ^ over the entire arc segment>

And standard deviation->

。

Wherein, the linear fitting specifically means: the imaging result of the space target on the optical camera is one point which is similar to a straight line, the data points are fitted into the straight line, the direction of the straight line is the direction of the arc section, and the vertical direction is the direction of the vertical arc section, so that the distance information is conveniently decomposed.

The prediction of the inventory space target has certain error, and the error of which the speed is greater than that of other directions along the speed direction is a problem in track prediction.

In some embodiments, in step S440, according to all spherical distances, the arc segment data under the thread is associated with the already-cataloged spatial target, so as to obtain a specific process of associating successful arc segment data or associating failed arc segment data, see the following exemplary description.

With reference to the schematic flow chart of an embodiment of associating the arc segment data under the thread with the cataloged space target according to all the spherical distances in the method shown in fig. 5, a specific process of associating the arc segment data under the thread with the cataloged space target according to all the spherical distances in step S440 is further described, which includes: step S510 to step S530.

Step S510, sorting all spherical distances to obtain a minimum spherical distance.

Step S520, extracting the sorted space target corresponding to the minimum spherical distance, and converting the minimum spherical distance into a space distance based on the track height of the sorted space target.

Step S530, if the spatial distance is smaller than the set distance threshold and the standard deviation of the spatial distance is smaller than the set standard deviation threshold, the association between the cataloged spatial target and the corresponding arc segment data is considered to be successful, otherwise, the association is considered to be unsuccessful, so as to obtain the arc segment data with successful association or the arc segment data with failed association.

Specifically, as shown in fig. 8, the spatial target association process applicable to GPU acceleration further includes: step 232, distance (e.g. spherical distance)

) Sorting, extracting the minimum-distance target in the cataloging space, and according to the track height of the cataloging space

Based on the calculated spherical distance>

Approximate conversion to spatial distance data>

。

When the distance is less than

km, and the standard deviation of the distance is less than 0.5km, the association between the inventory space target and the arc segment data is considered successful, otherwise, the association is considered unsuccessful. Wherein +>

For observing the moment, is>

The track information time of the space target is in units of days.

In the above steps, it can be seen that the associated processes of each arc segment data are independent from each other, so that GPU parallelism can be realized, and the parallel process is expressed as: copying the arc data and the forecasted time, right ascension and declination data of the space target into a GPU memory, and because the association of the arc data is mutually independent, parallel processing can be carried out according to the arcs, each thread processes the association problem of one arc, and the number of the developed threads is

Each thread may be processed according to the above-described flow.

In some embodiments, the method for associating a spatial object according to the aspect of the present invention further includes: the following process for associating successful and unsuccessful arc segment data is specifically as follows:

in a first aspect, for the subsequent processing procedure of successfully associated arc segment data: and grouping the successfully associated arc segment data according to corresponding space targets in all the cataloged space targets.

In a second aspect, for the subsequent processing procedure of successfully associated arc segment data: and temporarily setting the arc segment data which is not successfully correlated as a new space target, and if the arc segment data which is not successfully correlated is still determined subsequently, determining the arc segment data which is not successfully correlated as the new space target.

As shown in fig. 8, the spatial target association process applicable to GPU acceleration further includes: and 24, grouping the arc sections successfully associated according to the space targets for subsequent orbit determination processing, wherein the arc sections correspond to the targets in the library, and then splicing and determining the arc sections.

According to the space target association method applicable to GPU acceleration, TLE root number information (namely TLE track information) in a space target cataloging library is utilized to directly associate data of each observation arc segment, determination of initial orbit parameters and extrapolation association calculation work are not needed, and efficiency of space target cataloging can be improved. The TLE root information in the space target cataloging library is obtained by long-time observation, and the space target is numbered and can be regarded as directly obtained data.

In the related scheme, the error of the initial orbit parameter is large, and when the initial orbit parameter is calculated, an uncertainty factor is intangibly introduced to introduce the error, and the situation that the initial orbit calculation fails possibly exists.

In the related scheme, some screening needs to be performed to determine one factor in the initial orbit parameters, for example, judgment is performed according to the ascent point right ascension and the inclination angle, and some screening is performed to related objects, so that the subsequent calculation amount can be reduced, but errors caused by initial orbit determination are introduced. The scheme of the invention directly uses the GPU to accelerate the calculation, can greatly improve the speed, and can avoid the problem of introducing the initial orbit reference calculation.

According to a second embodiment of the present invention, there is provided a spatial object association apparatus using the spatial object association method of the first embodiment, as shown in fig. 6, including: an acquisition unit 102 and a control unit 104.

The obtaining unit 102 is configured to obtain a spatial target inventory library, and obtain arc segment data observed by an observation station (e.g., extracted arc segment data of the observation station).

A control unit 104 configured to tunnel a thread of the GPU in case the GPU completes initialization. The number of the threads of the GPU is more than one, and the number of the threads of the GPU is larger than or equal to the number of the spatial targets which are already cataloged in the spatial target cataloging library.

The control unit 104 is further configured to, for each of the more than one GPU threads, perform prediction calculation on a corresponding spatial target in the already-cataloged spatial targets in the spatial target cataloging library based on arc segment data corresponding to the spatial target in arc segment data observed by the observation station, so as to obtain position data of the spatial target. In this way, through more than one thread of the GPU, parallel forecasting calculation of the spatial target which is already cataloged in the spatial target cataloging library is achieved, and the position data of the spatial target which is already cataloged in the spatial target cataloging library is obtained.

The control unit 104 is further configured to calculate, under each thread, a spherical distance between an already-cataloged spatial target in the spatial target cataloging library and the arc segment data under the thread, and associate the arc segment data under the thread with the already-cataloged spatial target according to the spherical distances with the same number as the already-cataloged spatial targets, so as to obtain the arc segment data with successful association or the arc segment data with failed association.

In the scheme of the invention, a spatial target association device suitable for GPU (graphic processing unit, abbreviated as "GPU") acceleration is characterized in that Two Lines of Element (TLE) orbit information in a spatial target cataloging library is utilized to calculate theoretical observed values (such as right ascension and declination of a spatial target) of a satellite under a given optical observation instrument, then association matching is carried out by combining an actual observed value and the theoretical observed values to determine the spatial target in the spatial target cataloging library to which an arc segment belongs, and if an unmatched arc segment is found, a new spatial target can be judged, so that the efficiency of spatial target cataloging can be improved in the association process of spatial target optical measurement data.

Since the processes and functions implemented by the apparatus of this embodiment substantially correspond to the embodiments, principles and examples of the method, reference may be made to the related descriptions in the embodiments without being detailed in the description of this embodiment, which is not described herein again.

According to a third embodiment of the present invention, there is also provided a storage medium corresponding to a method for associating a space object, the storage medium including a stored program, wherein when the program runs, a device on which the storage medium is located is controlled to execute the method for associating a space object.

Since the processing and functions implemented by the storage medium of this embodiment substantially correspond to the embodiments, principles, and examples of the foregoing method, reference may be made to the related descriptions in the foregoing embodiments without being detailed in the description of this embodiment.

The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for associating spatial objects, comprising the steps of:

under each thread, calculating the spherical distance between the sorted space target in the space target sorting library and the arc segment data under the thread, and associating the arc segment data under the thread with the sorted space target according to the spherical distances with the same number as the sorted space targets so as to obtain the arc segment data which is successfully associated or the arc segment data which is failed to associate;

wherein, under each thread, calculating the spherical distance between the cataloged space targets in the space target cataloging library and the arc segment data under the thread, and associating the arc segment data under the thread with the cataloged space targets according to the spherical distances with the same number as the cataloged space targets so as to obtain the arc segment data successfully associated or the arc segment data failed to be associated, the method comprises the following steps:

under each thread, aiming at the time information in the time sequence of the arc segment data corresponding to the thread; extracting position data of the corresponding space target under the thread under the time information; the position data under the time information comprises: right ascension and declination under the time information;

calculating the observation data of the corresponding arc section under the time information according to the position data of the corresponding space target under the thread under the time information;

2. The method of correlating spatial objects of claim 1, wherein the arc segment data observed by the observation station comprises: more than one arc segment data; each arc segment data comprising: time series, right ascension series, and declination series.

3. The method according to claim 1 or 2, wherein tunneling the thread of the GPU comprises:

and opening up the number of the threads of the GPU so that the number of the threads of the GPU is larger than or equal to the number of the cataloged space targets in the space target cataloging library.

4. The method according to claim 3, wherein the storage information of the cataloged spatial objects is defined as shared content in the memory of the GPU; and placing other sequences which are irrelevant to the time sequence in the arc segment data observed by the observation station into a register memory.

5. The method according to claim 1 or 2, wherein for each of the more than one GPU threads, for a corresponding spatial target in the spatial target inventory, performing a forecast calculation on the spatial target based on arc segment data corresponding to the spatial target in the arc segment data observed by the observation station, to obtain position data of the spatial target, the method includes:

under each thread, according to TLE track information of a corresponding space target and a time sequence in corresponding arc segment data, SGP4 track forecasting suitable for GPU acceleration is carried out, and position data of the corresponding space target are calculated;

6. The method according to claim 3, wherein for each of the more than one GPU threads, for a corresponding spatial target in the spatial target cataloging library, performing forecast calculation on the spatial target based on arc segment data corresponding to the spatial target in the arc segment data observed by the observation station to obtain position data of the spatial target, the method includes:

and converting the position data of the satellite corresponding to the space target under the inertial coordinate system into right ascension and declination.

7. The method according to claim 4, wherein, in each of the more than one GPU threads, for a corresponding spatial target in the spatial targets cataloged in the spatial target cataloging library, performing forecast calculation on the spatial target based on arc data corresponding to the spatial target in the arc data observed by the observation station, to obtain position data of the spatial target, the method includes:

8. The method for associating the spatial target according to claim 1, wherein associating the arc segment data under the thread with the cataloged spatial target according to all the spherical distances to obtain the arc segment data with successful association or the arc segment data with failed association comprises:

sequencing all spherical distances to obtain the minimum spherical distance;

9. The method of associating spatial objects according to any one of claims 1, 2, 8, further comprising:

grouping the successfully associated arc segment data according to corresponding space targets in all the cataloged space targets;

10. The method of associating spatial objects of claim 3, further comprising:

and temporarily setting the arc segment data with unsuccessful association as a new space target, and if the arc segment data with unsuccessful association is still determined subsequently, determining the arc segment data with unsuccessful association as the new space target.

11. The method of associating spatial objects of claim 4, further comprising:

12. The method of associating spatial objects of claim 5, further comprising:

13. The method of associating spatial objects of claim 6, further comprising:

14. The method of associating spatial objects of claim 7, further comprising:

15. An apparatus for associating a spatial object, comprising:

the control unit is further configured to, for each of the more than one GPU threads, perform prediction calculation on a corresponding spatial target in the already-cataloged spatial targets in the spatial target cataloging library based on arc segment data corresponding to the spatial target in the arc segment data observed by the observation station, so as to obtain position data of the spatial target;

the control unit is further configured to calculate a spherical distance between an already-catalogued spatial target in the spatial target cataloguing library and arc segment data under the thread under each thread, and associate the arc segment data under the thread with the already-catalogued spatial target according to the spherical distances with the same number as the already-catalogued spatial targets, so as to obtain successfully-associated arc segment data or unsuccessfully-associated arc segment data;

the method for obtaining the arc segment data successfully associated or unsuccessfully associated by the control unit includes the steps of:

16. A storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, a device in which the storage medium is located is controlled to execute the method for associating a space object according to any one of claims 1 to 14.