CN114580224A - Distributed pneumatic fusion track coupling attitude perturbation analysis method - Google Patents

Distributed pneumatic fusion track coupling attitude perturbation analysis method Download PDF

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CN114580224A
CN114580224A CN202210498232.9A CN202210498232A CN114580224A CN 114580224 A CN114580224 A CN 114580224A CN 202210498232 A CN202210498232 A CN 202210498232A CN 114580224 A CN114580224 A CN 114580224A
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CN114580224B (en
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高兴龙
李志辉
陈钦
丁娣
彭傲平
蒋新宇
梁杰
唐小伟
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Equipment Design and Testing Technology Research Institute of China Aerodynamics Research and Development Center
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Abstract

The invention discloses a perturbation analysis method for a coupling attitude of a distributed pneumatic fusion orbit, which relates to the field of spacecraft flight research and comprises the following steps: establishing a spacecraft on-orbit flight orbit root perturbation analysis model; describing the pose motion of the spacecraft by using a quaternion method, and establishing a perturbation analysis equation of the coupling attitude of the pneumatic fusion orbit based on an attitude dynamics equation and an on-orbit flight orbit root perturbation analysis model; obtaining multi-source data of the in-orbit flight process of the spacecraft, wherein the multi-source data comprises the following steps: the method comprises the steps that position information of a spacecraft, speed information of the spacecraft, attitude information of the spacecraft and sailboard information of the spacecraft are obtained, multi-source data are processed by utilizing a perturbation analysis equation of a pneumatic fusion orbit coupling attitude to obtain a tracking result of a spacecraft state, and parameters of a solar sailboard angle of the spacecraft are identified to obtain an identification result; and the error factors of the identification result are quantitatively analyzed, so that the calculation precision of the on-orbit flight and reentry forecast of the spacecraft is improved.

Description

Distributed pneumatic fusion track coupling attitude perturbation analysis method
Technical Field
The invention relates to the field of spacecraft flight research, in particular to a perturbation analysis method for coupling attitude of a distributed pneumatic fusion orbit.
Background
The process of the large-scale spacecraft in the end of the service life returning to the earth atmosphere without control needs to undergo an orbit attenuation process, reentry disintegration and disintegration debris scattering, and demarcation points of all stages can be respectively assumed as an orbit degradation reentry point and spacecraft disintegration. In order to reliably forecast the time, position and speed information of the spacecraft in the derailing reentry, a countermeasure must be made in advance and feasible derailing input parameters are provided for the disintegration process and the calculation of the debris scattering, which needs a reliable orbit forecasting model. However, the flight process of the large-scale spacecraft is influenced by the aerodynamic force of a cross-drainage basin in the near-earth orbit atmospheric environment, the attitude of the spacecraft can rotate and have randomness under the action of factors such as space environment and gravity, uncertainty factors such as flight environment and system parameters bring challenges to orbit attenuation prediction of uncontrolled flight of the large-scale spacecraft, accuracy of reentry prediction can be directly influenced, and even great prediction deviation can be brought. Uncertainty analysis is carried out on the uncontrolled flight process of the large spacecraft, and the response of the system to a plurality of uncertainty factors is researched, so that the basis of the whole on-orbit forecasting problem is provided. Therefore, the method has important significance for analyzing the uncertainty of the large spacecraft uncontrolled flight.
In recent years, a large number of methods for analyzing system uncertainty have been developed in engineering application, wherein the Monte Carlo method based on large sample probability statistics is the most widely applied method, the reliability of which is fully verified in engineering practice, but the reliability of the method depends on the huge number of samples, and when the model parameters are more complex, the defects of overlarge calculated amount and low efficiency exist. The input parameters of the pneumatic fusion orbit perturbation model used in the on-orbit flight and reentry forecasting process of the spacecraft are complex and have a close coupling relation, meanwhile, the pneumatic parameters on the local surface elements on the surface of the large-scale spacecraft need to be integrated within each time step in the middle and long-term orbit degradation forecasting process based on the pneumatic fusion orbit perturbation model, the calculation of the dynamic model is very time-consuming, and the uncertainty analysis of the problems is not the optimal choice by adopting a Monte Carlo method. The Polynomial Chaos Expansion (PCE) is a relatively new method in uncertainty analysis, and the method is mainly based on the polynomial chaos theory, has solid mathematical foundation and good performance, and is gradually and widely applied in practice. The PCE method was originally proposed by Wiener in the study of turbulence problems, which is also considered to be the origin of the PCE method, which is then widely used also in the field of computational fluid dynamics. Wu and the like analyze the uncertainty and the sensitivity of transonic aerodynamics by utilizing a non-interference polynomial chaos method, study the influence of the uncertainty of Mach number and airfoil shape on aerodynamic characteristics, and also improve the utilization rate of samples by utilizing a sparse grid method, and find that the method has higher efficiency than an interference polynomial chaos method and a Monte Carlo method. Prabhakar and the like use PCE to analyze the influence of random factors such as trajectory coefficient, atmospheric density, lift-drag ratio and the like on the trajectory of the hypersonic flight vehicle, prove that the PCE method can be used for uncertainty research in hypersonic flight, and also obtain the conclusion that the method has higher efficiency than a Monte Carlo method. In addition, the PCE method is applied to the fields of wing type streaming, Mars landing design, aircraft track optimization and the like, and the reliability and the calculation efficiency of the PCE method are fully proved.
The space environment experienced by the on-orbit flying process of the near-earth orbit spacecraft is complex and changeable, wherein the influence of the aerodynamic characteristics of the space environment acts on a space target in a mode of shooting power and disturbing moment, particularly for a large-scale spacecraft which flies uncontrollably, the flying orbit and attitude characteristics of the spacecraft can change obviously and are difficult to predict accurately under the long-term action of cross-basin aerodynamic force and moment, and the flying orbit and attitude information of the spacecraft is acquired by relying on a powerful ground and space monitoring technology for on-orbit forecasting. However, the current monitoring technical means are difficult to realize high-precision prediction on attitude information of a space non-cooperative target random rolling process, and the obtained data has great uncertainty and random errors, so that the precision of spacecraft on-orbit prediction is influenced.
The traditional orbit forecasting method is mainly used for the controlled flight process of the spacecraft in the moment balance attitude, high-precision orbit determination can be carried out on a near-earth space target without obvious change of windward resistance area through the ground monitoring technology and the orbit calculation capacity, and the influence of cross-basin aerodynamic force and attitude change on orbit perturbation under different orbit heights is small. However, for a large-scale aircraft flying uncontrollably, a random rolling phenomenon of the attitude of the large-scale aircraft is not predicted by a reliable and effective method at present, and meanwhile, a collision phenomenon may occur in a high-speed movement process, wherein the solar sailboard is easy to deflect, so that a certain included angle exists between the solar sailboard and the main body of the aircraft, and the determination of the windward area is influenced.
Disclosure of Invention
The invention aims to improve the calculation accuracy of the on-orbit flight and reentry forecast of the spacecraft.
In order to achieve the above object, the present invention provides a perturbation analysis method for coupling attitude of a distributed pneumatic fusion track, the method comprising:
step 1: establishing a spacecraft on-orbit flight orbit root perturbation analysis model;
step 2: describing the pose motion of the spacecraft by using a quaternion method, and establishing a perturbation analysis equation of the coupling attitude of the pneumatic fusion orbit based on an attitude dynamics equation and the on-orbit flight orbit root perturbation analysis model;
and step 3: obtaining multi-source data of an on-orbit flight process of a spacecraft, wherein the multi-source data comprises: the method comprises the steps that position information of a spacecraft, speed information of the spacecraft, attitude information of the spacecraft and sailboard information of the spacecraft are obtained, perturbation analysis equations of coupling attitudes of the pneumatic fusion orbit are utilized to process multi-source data to obtain a tracking result of a spacecraft state, and parameters of solar sailboard angles of the spacecraft are identified to obtain an identification result;
and 4, step 4: and carrying out quantitative analysis on error factors of the identification result.
Aiming at the defects in the prior art, the applicant researches and discovers that uncertain factors influencing attitude change in the uncontrolled flight process of a spacecraft under the action of pneumatics need to be comprehensively considered, and meanwhile, the calculation efficiency needs to be improved to provide quick response for on-orbit monitoring and real-time forecasting of the spacecraft, so that a distributed pneumatic fusion orbit coupling attitude perturbation analysis method is established, and technical support is provided for improving the monitoring and early warning capacity of the on-orbit flight process of the large spacecraft.
The method is characterized by carrying out mathematical modeling on multi-physical field coupling dynamics behaviors of two types of spacecraft key components in an in-orbit flight process of the space station under a multi-mode uncertain atmospheric mode cross-basin aerodynamic environment through theoretical analysis and simulation evaluation methods, establishing a multi-source data distributed type pneumatic fusion track coupling attitude perturbation simulation evaluation method based on a digital twin technology through a simulation method, realizing intelligent and efficient evaluation of the pneumatic fusion track coupling attitude perturbation method by combining a GPU integrated parallel algorithm, providing a key technical solution for an in-orbit service safety life evaluation theory of a spacecraft metal truss structure material, and supporting development and implementation of intelligent and efficient evaluation key technical research and application of service behaviors of the spacecraft pneumatic environment metal structure material.
Two types of important transportation systems for high-end equipment space stations: the common key technical problems of spacecraft manned ground shuttle, cargo transportation system of cargo spacecraft, key component metal truss structure composite material, service life detection of on-orbit service behavior and safety evaluation of off-orbit reentry are as follows: on the basis of cross-basin aerodynamic unified calculation theoretical modeling, a method of machine learning and adaptive filtering parameter identification is introduced, and an online real-time rapid identification method of spacecraft metal truss structure pose information and solar array operation angle parameters is established; constructing an on-orbit parameter adaptive prediction model of multi-source data acquired based on adaptive filtering signal processing; an integrated modeling method is adopted to establish a filtered and parameter identification error analysis coupling attitude dynamics orbit perturbation model in a cross-basin aerodynamic environment at an orbit degradation stage before the spacecraft leaves the orbit and enters the orbit; aiming at the requirements of rapid response and intelligent efficient evaluation of on-orbit flight safety and service life prediction of a spacecraft metal structure, the timeliness of the track perturbation modeling theoretical simulation evaluation process of a multi-source data and uncertainty analysis method is considered, a GPU integrated parallel acceleration method for pneumatically fusing track coupling attitude perturbation prediction is researched, and a parallel resolving task of the multi-source data uncertainty analysis process is carried out by adopting the integrated acceleration method.
Preferably, the method further comprises:
calculating by using a cross-basin aerodynamic algorithm to obtain a lift coefficient and a drag coefficient of the spacecraft based on spacecraft height information, spacecraft size information, spacecraft shape information and spacecraft deflection angle information of a solar sailboard relative to a spacecraft main body;
and analyzing the influence of the aerodynamic lift and the aerodynamic resistance on the variable rate of the semi-major axis and the variable rate of the eccentricity ratio by using the spacecraft in-orbit flight orbit root perturbation analysis model based on the lift coefficient and the resistance coefficient of the spacecraft.
Preferably, the method further comprises:
obtaining first external measurement data of a spacecraft, and filtering the first external measurement data by adopting a self-adaptive Kalman filtering method to obtain second external measurement data, wherein the first external measurement data comprises: the method comprises the following steps of (1) position information of a spacecraft, speed information of the spacecraft, attitude information of the spacecraft and deflection angle information of a solar sailboard of the spacecraft relative to a main body of the spacecraft;
and analyzing the influence of the spacecraft aerodynamic moment on the spacecraft attitude motion parameters by utilizing an attitude dynamics equation and a cross-basin aerodynamic algorithm based on the second external measurement data.
Preferably, the method adopts a PCE uncertainty analysis method to quantitatively analyze error factors of the identification result.
Preferably, the method utilizes GPU parallel computing to accelerate the analysis process in the method.
Preferably, the spacecraft in-orbit flight orbit root perturbation analysis model specifically comprises:
Figure 160676DEST_PATH_IMAGE001
Figure 990091DEST_PATH_IMAGE002
Figure 49314DEST_PATH_IMAGE003
Figure 622378DEST_PATH_IMAGE004
Figure 122761DEST_PATH_IMAGE005
Figure 388657DEST_PATH_IMAGE006
Figure 731914DEST_PATH_IMAGE007
wherein the content of the first and second substances,
Figure 498881DEST_PATH_IMAGE008
is a transient semi-major axis of the orbit,
Figure 978404DEST_PATH_IMAGE009
in order to determine the eccentricity of the track,
Figure 556147DEST_PATH_IMAGE010
in order to obtain the inclination angle of the track,
Figure 386700DEST_PATH_IMAGE011
the radial angle of the near place is the radial angle of the near place,
Figure 567146DEST_PATH_IMAGE012
for the ascending point longitude to be the point of intersection longitude,
Figure 291388DEST_PATH_IMAGE013
in order to be a true proximal angle,
Figure 899087DEST_PATH_IMAGE014
as the coefficient of the trajectory is given,
Figure 216936DEST_PATH_IMAGE015
is a semi-major axis of the track in a long period of variation,
Figure 76438DEST_PATH_IMAGE016
the track eccentricity which is a state of long period change,
Figure 530554DEST_PATH_IMAGE017
the track inclination angle for the long period change state,
Figure 574733DEST_PATH_IMAGE018
is the perigee argument of the long period changing state,
Figure 504512DEST_PATH_IMAGE019
the ascending node longitude for the long-period changing state,
Figure 292339DEST_PATH_IMAGE020
in a long-period changing stateThe true angle of approach of (a) is,
Figure 600961DEST_PATH_IMAGE021
is the ballistic coefficient of the long-period changing state,
Figure 691408DEST_PATH_IMAGE022
is the orbit semi-major axis of the short period perturbation term,
Figure 718269DEST_PATH_IMAGE023
the track eccentricity for the short period perturbation term,
Figure 309788DEST_PATH_IMAGE024
the inclination of the track for the short period perturbation term,
Figure 597550DEST_PATH_IMAGE025
is the perigee argument of the short period perturbation term,
Figure 983532DEST_PATH_IMAGE026
for the ascending point longitude of the short period perturbation term,
Figure 763269DEST_PATH_IMAGE027
is the true paraxial angle of the short period perturbation term,
Figure 502686DEST_PATH_IMAGE028
is the ballistic coefficient of the short-period perturbation term,
Figure 113796DEST_PATH_IMAGE029
the symbols are changed for a short period.
Preferably, the calculation method of the track semimajor axis variable rate and the eccentricity variable rate is as follows:
Figure 670679DEST_PATH_IMAGE030
Figure 672133DEST_PATH_IMAGE031
Figure 215241DEST_PATH_IMAGE032
wherein the content of the first and second substances,
Figure 352961DEST_PATH_IMAGE033
being instantaneous rail semi-major axis
Figure 346325DEST_PATH_IMAGE008
The rate of change of (a) is,
Figure 694130DEST_PATH_IMAGE034
is a vector of angular momentum
Figure 165562DEST_PATH_IMAGE035
The rate of change of (a) is,
Figure 423368DEST_PATH_IMAGE036
integrating operator for Hamiltonian
Figure 197420DEST_PATH_IMAGE037
The rate of change of (a) is,
Figure 907888DEST_PATH_IMAGE038
in order to obtain the coefficient of the gravity,
Figure 183011DEST_PATH_IMAGE039
is the vector sum of the aerodynamic drag and the aerodynamic lift of the spacecraft,
Figure 419957DEST_PATH_IMAGE040
is a position vector of a spacecraft geocentric inertial coordinate system,
Figure 223965DEST_PATH_IMAGE041
a velocity vector of a spacecraft geocentric inertial coordinate system;
in an elliptical orbit, the long period effect of aerodynamic drag is:
Figure 562674DEST_PATH_IMAGE042
Figure 375909DEST_PATH_IMAGE043
wherein the content of the first and second substances,
Figure 342728DEST_PATH_IMAGE044
is a ballistic coefficient corresponding to the aerodynamic drag coefficient,
Figure 707850DEST_PATH_IMAGE045
for the long-period instantaneous orbit semi-major axis
Figure 658489DEST_PATH_IMAGE008
The rate of change of (a) is,
Figure 9836DEST_PATH_IMAGE046
is the density of the atmosphere at the height of the near site,
Figure 972107DEST_PATH_IMAGE047
is the height of the near-to-location,
Figure 383496DEST_PATH_IMAGE048
is the radius of the orbit at the height of the near point,
Figure 555852DEST_PATH_IMAGE049
is the eccentricity of the track in a long period
Figure 569944DEST_PATH_IMAGE009
The rate of change of (c);
the long period effect of aerodynamic lift is:
Figure 776935DEST_PATH_IMAGE050
wherein the content of the first and second substances,
Figure 359226DEST_PATH_IMAGE051
the ballistic coefficient is corresponding to the aerodynamic lift coefficient;
for a near-circular orbit, the long period effect of aerodynamic drag is:
Figure 894243DEST_PATH_IMAGE052
Figure 649710DEST_PATH_IMAGE053
。
preferably, the perturbation analysis equation of the coupling attitude of the pneumatic fusion orbit is as follows:
Figure 711207DEST_PATH_IMAGE054
wherein the content of the first and second substances,
Figure 593626DEST_PATH_IMAGE055
as are the dual force vectors acting on the rigid body,
Figure 474994DEST_PATH_IMAGE056
is the sum of a dual mass operator and a dual inertia operator,
Figure 971834DEST_PATH_IMAGE057
is the change rate of the rotation of the rigid body,
Figure 746892DEST_PATH_IMAGE058
is the rotation of a rigid body.
Preferably, the identification result is obtained by calculation in the following way:
Figure 936565DEST_PATH_IMAGE059
Figure 570809DEST_PATH_IMAGE060
Figure 746706DEST_PATH_IMAGE061
wherein the content of the first and second substances,
Figure 251637DEST_PATH_IMAGE062
corresponding to the position, the speed, the attitude angle and the attitude angular speed of the spacecraft,
Figure 612211DEST_PATH_IMAGE063
corresponding to the position, the speed, the attitude angle and the change rate of the attitude angular speed of the spacecraft,
Figure 61647DEST_PATH_IMAGE064
the deflection angle of the solar panel relative to the main body,
Figure 510077DEST_PATH_IMAGE065
is an input function;
Figure 400673DEST_PATH_IMAGE066
the observation vectors of the position, the speed, the attitude angle, the attitude angular speed and the deflection angle of the solar sailboard relative to the main body of the spacecraft are obtained;
Figure 932148DEST_PATH_IMAGE067
for the discrete time series obtained by the external measurement,
Figure 134460DEST_PATH_IMAGE068
in order to add a matrix to the process noise,
Figure 42373DEST_PATH_IMAGE069
in order to measure the additive matrix of the noise,
Figure 787475DEST_PATH_IMAGE070
in order to calculate the initial moment of time,
Figure 99639DEST_PATH_IMAGE071
in order to calculate the time of day,
Figure 930191DEST_PATH_IMAGE072
is a state equation for the state variable,
Figure 110637DEST_PATH_IMAGE073
is an observation equation for the state variable,
Figure 569300DEST_PATH_IMAGE074
as a discrete time series.
Preferably, the method utilizes a digital simulation system to perform parameter identification on the solar panel angle of the spacecraft to obtain an identification result.
One or more technical schemes provided by the invention at least have the following technical effects or advantages:
the method considers the influence of the attitude rotation effect of the large uncontrolled spacecraft, the angle of the solar sailboard and other random error factors, and improves the calculation precision of the on-orbit flight and reentry forecast of the spacecraft;
the invention adopts GPU distributed acceleration to improve the calculation acceleration in the method and improve the simulation efficiency.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention;
FIG. 1 is a schematic flow chart of a distributed pneumatic fusion track coupling attitude perturbation analysis method;
FIG. 2 is a schematic diagram of the components of the multi-source data on-line fast identification digital simulation system;
FIG. 3 is a schematic flow chart of the overall technical solution of the invention;
FIG. 4 is a schematic diagram of a track perturbation model GPU integrated parallel acceleration process in the invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflicting with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described and thus the scope of the present invention is not limited by the specific embodiments disclosed below.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a distributed pneumatic fusion track coupling attitude perturbation analysis method, an embodiment of the present invention provides a distributed pneumatic fusion track coupling attitude perturbation analysis method, where the method includes:
step 1: establishing a spacecraft on-orbit flight orbit root perturbation analysis model;
step 2: describing the pose motion of the spacecraft by using a quaternion method, and establishing a perturbation analysis equation of the coupling attitude of the pneumatic fusion orbit based on an attitude dynamics equation and the on-orbit flight orbit root perturbation analysis model;
and step 3: obtaining multi-source data of an on-orbit flight process of a spacecraft, wherein the multi-source data comprises: the method comprises the steps that position information of a spacecraft, speed information of the spacecraft, attitude information of the spacecraft and sailboard information of the spacecraft are obtained, perturbation analysis equations of coupling attitudes of the pneumatic fusion orbit are utilized to process multi-source data to obtain a tracking result of a spacecraft state, and parameters of solar sailboard angles of the spacecraft are identified to obtain an identification result;
and 4, step 4: and carrying out quantitative analysis on error factors of the identification result.
The method is described in detail below:
and establishing a spacecraft in-orbit flight orbit root perturbation analysis model, wherein the spacecraft in-orbit flight orbit root perturbation analysis model corresponds to a following formula 1.
Based on spacecraft height information, spacecraft size information, spacecraft shape information and spacecraft deflection angle information of a solar sailboard of a spacecraft relative to a spacecraft main body, a cross-basin aerodynamic algorithm is utilized to calculate a spacecraft lift coefficient and a resistance coefficient, a track root number model is combined to analyze the influence rule of aerodynamic lift and aerodynamic resistance on a semi-major axis and eccentricity of a track, the cross-basin aerodynamic algorithm is shown in a reference document, namely integrated modeling and calculation research of low-orbit-control aerodynamic characteristics of a spacecraft, manned spaceflight, Lishishishiji, Wujunlin, Penproping and Tangge reality, and specific algorithms are not described in the embodiment.
Carrying out filtering processing on outsourced data based on an adaptive Kalman filtering method, wherein specific algorithms are shown in a document ' large spacecraft uncontrolled flight orbit decay forecast preliminary study-Gaojinglong, Chengxian, Lishihui and Tyjm), the outsourced data comprise the position, the speed and the attitude of a spacecraft and the deflection angle of a solar sailboard relative to the spacecraft body, and by utilizing an attitude dynamics equation and a cross-basin aerodynamic algorithm adopted in a second step, the specific algorithms of the attitude dynamics equation are shown in a reference document ' orbit perturbation method for large spacecraft uncontrolled flight reentry time short-term forecast-manned space-Gaojinglong, Lishihui, Chengxian, Dinghei, Pentao ' and Tiangong aircraft low-orbit control aerodynamic characteristic integrated modeling and calculation study-manned space-Lishihui, Wulin, Pentao Ping and Tang's real time ', three-component pair attitude motion parameters are analyzed, including the law of influence of attitude angle and attitude angular velocity. The three components of the aerodynamic moment refer to the components of the aerodynamic moment in the three directions of XYZ.
The method comprises the following steps of describing pose motion of a spacecraft by utilizing a quaternion method based on an attitude dynamics equation, wherein a specific algorithm of the quaternion method is shown in a reference document, namely researching an orbit perturbation method for short-term prediction of uncontrolled flight reentry time of a large spacecraft, namely carrying out manned spaceflight, Gaoxing, Lishihui, Chengqin, Dixihuan and Pentaoping, combining an established spacecraft in-orbit flight orbit root perturbation analysis model, obtaining a spacecraft dynamics equation by rigid motion characteristics and dual quaternion properties, namely establishing a perturbation analysis equation of a pneumatic fusion orbit coupling attitude, wherein the perturbation analysis equation of the pneumatic fusion orbit coupling attitude corresponds to a subsequent formula 17;
the multi-source data of the in-orbit flight process of the spacecraft comprises spacecraft position speed (corresponding to six orbit elements one by one), attitude and sailboard information (the measurement process can be described as a measurement equation and comprises noise in the measurement process) obtained in an external measurement mode, a spacecraft dynamics equation state equation established in the fourth step is combined, data filtering is carried out, a spacecraft state (position speed and attitude) tracking result is obtained, and parameters of the solar sailboard angle are identified.
A PCE uncertainty analysis method is adopted, and the specific method is shown in a reference document, namely application of the PCE method in orbit prediction error analysis in a space laboratory, namely astronavigation, European Qi, Cheng, Li Cuilan and Li \21232andquantitative analysis is carried out on error factors of a multi-source data identification result.
And accelerating the perturbation calculation of the multi-task pneumatic fusion orbit coupling attitude from the first step to the sixth step by using a GPU parallel calculation method, and constructing a multi-source data on-orbit service long-term forecasting and evaluation parallel acceleration calculation module.
The orbit change of the on-orbit motion of the spacecraft under the action of various perturbation factors comprises three types of long-term, long-term and short-term, wherein the impact of cross-basin aerodynamic force is fully considered, and a ballistic coefficient represented by aerodynamic resistance is introduced
Figure 442578DEST_PATH_IMAGE014
Considering the dissipation effect of aerodynamic resistance on orbital energy and the influence on the semi-major axis and eccentricity of an orbit, six-element description of the orbit element defined based on Brouwer commonly used in the international space world is modified as follows:
Figure 166952DEST_PATH_IMAGE001
Figure 151088DEST_PATH_IMAGE002
Figure 746149DEST_PATH_IMAGE003
Figure 524749DEST_PATH_IMAGE004
Figure 329894DEST_PATH_IMAGE005
Figure 242355DEST_PATH_IMAGE006
Figure 550977DEST_PATH_IMAGE007
（1）
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE075
the method is characterized in that the method respectively corresponds to instantaneous orbit parameters and trajectory coefficients, and seven parameters respectively correspond to instantaneous orbit semi-major axis, orbit eccentricity, orbit inclination, perigee argument, elevation point longitude, true perigee angle and trajectory coefficients.
Figure 110265DEST_PATH_IMAGE076
The seven parameters respectively correspond to the orbit semi-major axis, the orbit eccentricity, the orbit inclination angle, the perigee argument, the longitude of the ascending intersection point, the true perigee angle and the trajectory coefficient of the long-period change state.
Figure 137127DEST_PATH_IMAGE077
The seven parameters respectively correspond to the orbit semi-major axis, the orbit eccentricity, the orbit inclination angle, the perigee argument, the longitude of the ascending intersection point, the true perigee angle and the ballistic coefficient of the short-period perturbation state.
The main track number influenced by the atmospheric resistance in the track attenuation process is a semi-major axis and eccentricity, a long period coefficient of the track number under atmospheric perturbation is solved according to the magnitude of three-component perturbation force of aerodynamic force, and the periodic change rule of the track number is analyzed.
In perturbation solutions of the number of orbits, a simplified model is usually adopted for the influence of air, in the near-earth orbit, aerodynamic forces are the main non-earth gravitational forces acting on the target spacecraft. Aerodynamic force can be defined in terms of atmospheric density, velocity of motion and aerodynamic coefficient, and surface-to-mass ratio. Aerodynamic drag and lift acceleration may be defined as the direction perpendicular to the direction of the target spacecraft motion velocity vector.
Figure 728645DEST_PATH_IMAGE078
（2）
Figure 16407DEST_PATH_IMAGE079
（3）
Wherein, the first and the second end of the pipe are connected with each other,
Figure 402389DEST_PATH_IMAGE080
the perturbation force generated by the aerodynamic resistance of the target spacecraft,
Figure 791913DEST_PATH_IMAGE081
Is the space atmospheric density,
Figure 921543DEST_PATH_IMAGE082
The moving speed of the target spacecraft,
Figure 939178DEST_PATH_IMAGE083
Is the characteristic area of the target spacecraft,
Figure 886274DEST_PATH_IMAGE084
Is the aerodynamic drag coefficient of the target spacecraft,
Figure 887728DEST_PATH_IMAGE085
For the target spacecraft mass,
Figure 555470DEST_PATH_IMAGE086
The perturbation force generated by the aerodynamic lift force,
Figure 568556DEST_PATH_IMAGE087
Is the aerodynamic lift coefficient of the target spacecraft.
Aerodynamic drag and lift are described in the form of perturbation forces in conjunction with the perturbation equations of the keplerian orbit:
Figure 561920DEST_PATH_IMAGE088
（4）
Figure 519512DEST_PATH_IMAGE089
（5）
Figure 381158DEST_PATH_IMAGE090
（6）
wherein the content of the first and second substances,
Figure 638964DEST_PATH_IMAGE091
is an acceleration vector of a geocentric inertial coordinate system of the target spacecraft,
Figure 537650DEST_PATH_IMAGE092
Is a gravitational coefficient,
Figure 123483DEST_PATH_IMAGE093
Is a position vector of a geocentric inertial coordinate system of the target spacecraft,
Figure 726502DEST_PATH_IMAGE094
Is a velocity vector of a geocentric inertial coordinate system of the target spacecraft,
Figure 573236DEST_PATH_IMAGE095
The vector sum of the aerodynamic resistance and the aerodynamic lift of the target spacecraft,
Figure 908402DEST_PATH_IMAGE096
As angular momentum vector of target spacecraft
Figure 981531DEST_PATH_IMAGE097
The unit direction vector of (1).
In the process of track perturbation motion, the semi-major axis of the track
Figure 794767DEST_PATH_IMAGE098
Vector of angular momentum
Figure 27165DEST_PATH_IMAGE097
Sum Hamiltonian integral operator
Figure 392287DEST_PATH_IMAGE099
Rather, their rate of change can be defined as:
Figure 342926DEST_PATH_IMAGE100
（7）
Figure 694273DEST_PATH_IMAGE101
（8）
Figure 390964DEST_PATH_IMAGE102
（9）
wherein the content of the first and second substances,
Figure 802354DEST_PATH_IMAGE103
being the semi-major axis of the track
Figure 974709DEST_PATH_IMAGE098
The rate of change of (a) is,
Figure 519960DEST_PATH_IMAGE104
is a vector of angular momentum
Figure 195792DEST_PATH_IMAGE097
The rate of change of (a) is,
Figure 43662DEST_PATH_IMAGE105
is a Hamiltonian integralOperator
Figure 313101DEST_PATH_IMAGE106
The rate of change of (c).
Figure 271830DEST_PATH_IMAGE107
To facilitate the analytical calculation of the mean orbit root model, the drag coefficient and lift coefficient may also be described in the form of ballistic coefficients, as follows
Figure 333327DEST_PATH_IMAGE108
（10）
Figure 476732DEST_PATH_IMAGE109
（11）
Wherein the content of the first and second substances,
Figure 92521DEST_PATH_IMAGE110
the ballistic coefficient corresponding to the aerodynamic drag coefficient,
Figure 854941DEST_PATH_IMAGE111
The ballistic coefficient is corresponding to the aerodynamic lift coefficient.
By decomposing the vector equation, Walter et al deduce that aerodynamic drag primarily affects the orbit semimajor axis and eccentricity. By a similar approach, Cook found that aerodynamic lift had only a relatively significant long-term perturbation effect on eccentricity and did not contribute to track rounding. According to the work of two people, the influence of aerodynamic lift force and drag force on the long-period effect of the orbit to act on the elliptical orbit and the near-circular orbit respectively can be expressed as follows
In an elliptical orbit, the long period effect of the aerodynamic drag is:
Figure 646310DEST_PATH_IMAGE112
（12）
Figure 835983DEST_PATH_IMAGE113
（13）
wherein the content of the first and second substances,
Figure 204648DEST_PATH_IMAGE114
is a long period inner track semi-major axis
Figure 629813DEST_PATH_IMAGE098
The rate of change of,
Figure 134743DEST_PATH_IMAGE115
Is the atmospheric density of the height of the near place,
Figure 495318DEST_PATH_IMAGE116
Is the height of the near place,
Figure 226644DEST_PATH_IMAGE117
The radius of the track at the height of the near place,
Figure 658763DEST_PATH_IMAGE118
Is the eccentricity of the track in a long period
Figure 18200DEST_PATH_IMAGE099
The rate of change of (c).
The long period effect of aerodynamic lift is:
Figure 425042DEST_PATH_IMAGE119
（14）
for a near-circular orbit, the long-period perturbation effect of the aerodynamic lift force is neglected, and the long-period effect of the aerodynamic resistance is as follows:
Figure 768298DEST_PATH_IMAGE120
（15）
Figure 410632DEST_PATH_IMAGE121
（16）
wherein the content of the first and second substances,
Figure 14789DEST_PATH_IMAGE122
for atmospheric density along with orbit semimajor axis
Figure 717166DEST_PATH_IMAGE098
A function of the change.
Besides, the attitude dynamics modeling involves the disturbance analysis of three components of the aerodynamic moment. The specific analysis mode is similar to the method for analyzing the number of the aerodynamic influence tracks, and the change rule of the attitude angle and the angular speed is analyzed according to the magnitude of the external moment. The difficult problem is that uncertainty of spacecraft attitude rolling motion needs to be considered, and the attitude determination parameters of the measured data are subjected to state estimation by combining an adaptive Kalman filtering method, so that the attitude prediction precision is improved.
Then coupling modeling needs to be carried out on the aerodynamic force influence orbit perturbation and aerodynamic moment influence attitude dynamics of the first two parts, a nonlinear continuous state space model of aerodynamic fusion orbit coupling attitude perturbation is constructed, the attitude motion of the in-orbit spacecraft is described by using dual quaternion, and a spacecraft dynamics equation can be obtained by rigid motion characteristics and dual quaternion properties:
Figure 547718DEST_PATH_IMAGE123
（17）
Figure 615249DEST_PATH_IMAGE124
is the sum of dual mass operator and dual inertia operator, and is defined as dual inertia operator,
Figure 480437DEST_PATH_IMAGE125
Is the change rate of the rotation of the rigid body,
Figure 88136DEST_PATH_IMAGE126
Is the rotation of a rigid body.
In the formula
Figure 265039DEST_PATH_IMAGE127
By forces on the target spacecraft for dual force vectors acting on rigid bodies
Figure 514755DEST_PATH_IMAGE128
Sum moment
Figure 234449DEST_PATH_IMAGE129
The above formula describes that the rigid body moves along with the mass center translation and the mass center rotation at the same time, and the rigid body and the mass center translation and the mass center rotation are coupled with each other.
When the position speed (corresponding to the number of six orbits one by one), the attitude and the solar panel information of the target spacecraft are obtained in an external measurement mode (the measurement process can be described as a measurement equation and includes noise in the measurement process), data filtering can be carried out by combining the state equation to obtain a tracking result of the state (the position speed and the attitude) of the target spacecraft, and parameter identification is carried out on the deflection angle of the solar panel relative to the main body. The identified problem is typically given in the form of a state equation, an output equation, and a measurement equation:
Figure 888415DEST_PATH_IMAGE130
Figure 427981DEST_PATH_IMAGE060
Figure 215809DEST_PATH_IMAGE131
（18）
in the formula (I), the compound is shown in the specification,
Figure 649064DEST_PATH_IMAGE132
corresponding to the position, speed, attitude angle and attitude angular speed of the target spacecraft,
Figure 864145DEST_PATH_IMAGE133
corresponding to the position, the speed, the attitude angle and the change rate of the attitude angular speed of the target spacecraft,
Figure 156586DEST_PATH_IMAGE134
the deflection angle (or inertia parameter of other positions) of the solar panel relative to the main body;
Figure 92312DEST_PATH_IMAGE135
is an input function of the system;
Figure 786598DEST_PATH_IMAGE136
observing vectors of the position, the speed, the attitude angle, the attitude angular speed and the deflection angle of the solar sailboard relative to the main body of the target spacecraft;
Figure 172580DEST_PATH_IMAGE137
the discrete time series obtained for the external measurement contain measurement errors. By measuring the result
Figure 545793DEST_PATH_IMAGE137
Come to right
Figure 409844DEST_PATH_IMAGE132
And
Figure 568424DEST_PATH_IMAGE134
and tracking and estimating, and processing by using an adaptive Kalman filtering algorithm.
Figure 594149DEST_PATH_IMAGE128
In order to add a matrix to the process noise,
Figure 189078DEST_PATH_IMAGE138
in order to measure the additive matrix of the noise,
Figure 122399DEST_PATH_IMAGE139
in order to calculate the initial moment of time,
Figure 135485DEST_PATH_IMAGE140
in order to calculate the time of day,
Figure 863270DEST_PATH_IMAGE141
is a state equation for the state variable,
Figure 352020DEST_PATH_IMAGE142
is an observation equation of the state variable.
And establishing a state space model of the pneumatic fusion orbit coupling attitude based on a recursive least square fitting algorithm of the adaptive Kalman filtering. Parameters such as speed and attitude of a spacecraft metal truss structure and key components during in-orbit service and deflection angle information of a solar sailboard relative to a main body are researched through a simulation means to perform online rapid identification, and an online identification digital simulation system is established based on a neural network, as shown in figure 2.
The online identification digital simulation system comprises:
the initialization module is used for initializing the parameters and comprises the following components: the model parameter setting module is used for inputting model parameters required by the calculation and simulation of the dynamic model, including the quality, the density and the like of the physical characteristic parameters of the spacecraft; the simulation parameter setting module is used for inputting initial condition parameters required by the calculation and simulation of the dynamic model, wherein the initial condition parameters comprise position, speed, posture and solar panel deflection angle; the identification parameter setting module is used for inputting an algorithm and data used for the dynamic model calculation simulation parameter identification;
the online identification module is used for identifying the state parameter disturbance quantity of the spacecraft in the flight process online and identifying the speed and the attitude of the spacecraft in the in-orbit flight process and the deflection angle disturbance quantity of the solar panel according to the adaptive Kalman filtering algorithm and the state equation;
the time-varying parameter simulation module is used for carrying out simulation calculation and simulation on time-varying parameters used by the state space model, and comprises external multi-source data of state variables and the like;
the input excitation generating module is used for generating external input excitation received by the spacecraft in an orbit flight process, and mainly comprises perturbation force and moment received by a natural attenuation process, including aerodynamic resistance, lift force, earth gravity, aerodynamic disturbance moment and the like;
the dynamic model simulation module is used for carrying out simulation calculation on the pneumatic fusion track coupling attitude dynamic model;
the data visualization module is used for visually outputting and demonstrating simulation calculation result data;
meanwhile, aiming at the problem of uncertainty of multi-source data acquired based on intelligent sensing and machine learning, an intelligent control optimization method is required to be used for filtering and parameter identification of a motion simulation result of the multi-source data of a pneumatic fusion orbit coupling attitude perturbation model in an in-orbit service environment, and intelligent detection is carried out by combining an intelligent high-efficiency evaluation technology spacecraft operation safety life. The coupling relationship and the technical scheme flow field of each module of the whole subtask system are shown in FIG. 3. The specific technical scheme comprises the following flows:
firstly, establishing a dynamic system for simulation calculation, wherein the dynamic system comprises a coupling dynamic model of a coupling attitude of a pneumatic fusion orbit; meanwhile, multi-source data obtained by the on-orbit flight of the spacecraft are used as input parameters, and a cross-basin pneumatic parameter fast engineering algorithm under a space environment is combined to perform simulation calculation on a dynamic model; in the calculation process, the online rapid identification of a neural network is utilized to generate and predict state parameters, and the predicted data is filtered based on an adaptive Kalman filtering model; finally, outputting on-orbit prediction parameters generated by calculation of a dynamic system superposed with dissipation effect and intelligent control, carrying out uncertainty analysis on multi-source data and prediction results by combining uncertainty analysis methods such as Monte Carlo and the like, and giving quantitative evaluation results of intelligent high-efficiency evaluation, thereby establishing a whole set of distributed pneumatic fusion track coupling attitude perturbation simulation evaluation system (AOAPSES) based on a digital twin technology
Aiming at the uncertainty of multi-source data under the condition of big data and error factors caused by nonlinear dynamics characteristics of a pneumatic fusion orbit coupling attitude perturbation model, a Monte Carlo simulation method is combined to carry out uncertainty analysis on in-orbit prediction and intelligent evaluation algorithm of service behaviors. In order to overcome the speed bottleneck of the long-term prediction and uncertainty analysis process of the pneumatic fusion track coupling attitude perturbation model, a GPU parallel computing method is used for accelerating the multitask track perturbation calculation, and a parallel acceleration computing module for the long-term prediction and evaluation of the multi-source data in-orbit service is constructed. For the time step number estimation of the parallel computing task in the on-orbit long-term forecasting process, an integrated parallel acceleration algorithm is proposed by adopting a mode of one-time calling and data interaction of a CPU and a GPU, and a specific acceleration flow is shown in FIG. 4. The specific process is as follows:
firstly, reading a multi-source data file obtained by an initialized spacecraft in-orbit observation: the method comprises the steps of reading in random number files of multi-source data generated by adopting a Monte Carlo method, calling an in-orbit forecasting model for initialization calculation, and storing the random number files into a memory. The process is that multi-source data initialization operation of the spacecraft is carried out in the CPU arithmetic unit in sequence;
and then the program enters a 1 st data transmission stage, namely data transmission from the CPU to the GPU is carried out, GPU memory spaces with corresponding sizes are opened up according to the number of multi-source data, and the GPU memory spaces corresponding to each data are respectively stored: the method comprises the following steps of starting time, ending time, time step length, satellite ephemeris, attitude parameters, pneumatic parameters, solar panel deflection angles and the like, wherein the data are used as structure body variables to be packaged and transmitted to a GPU memory space, and waiting for GPU calling. After the 1 st data transmission, the GPU brings the corresponding structure body variables into kernel functions in a plurality of independent threads to perform parallel computation, and the computation of a plurality of time steps in the parallel computation process is not performed sequentially but performed synchronously. And after the GPU parallel computation is finished, obtaining satellite ephemeris, attitude parameters, pneumatic parameters and solar panel deflection angle data of the spacecraft, carrying out data transmission for the 2 nd time (from the GPU to the CPU), transmitting the multi-source data back to a CPU memory, and outputting a file to finish the computation, thereby finishing the parallel acceleration.
Compared with a modular GPU acceleration method, the method has the advantages that the speed is obviously improved during medium and low scale calculation, and the method is suitable for acceleration of the calculation task amount of the project.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A method for analyzing perturbation of coupled attitude of a distributed pneumatic fusion orbit is characterized by comprising the following steps:
step 1: establishing a spacecraft on-orbit flight orbit root perturbation analysis model;
step 2: describing the pose motion of the spacecraft by using a quaternion method, and establishing a perturbation analysis equation of a pneumatic fusion orbit coupling attitude based on an attitude dynamics equation and the on-orbit flight orbit radical perturbation analysis model;
and step 3: obtaining multi-source data of an on-orbit flight process of a spacecraft, wherein the multi-source data comprises: the method comprises the steps that position information of a spacecraft, speed information of the spacecraft, attitude information of the spacecraft and sailboard information of the spacecraft are obtained, perturbation analysis equations of coupling attitudes of the pneumatic fusion orbit are utilized to process multi-source data to obtain a tracking result of a spacecraft state, and parameters of solar sailboard angles of the spacecraft are identified to obtain an identification result;
and 4, step 4: and carrying out quantitative analysis on error factors of the identification result.
2. The distributed pneumatic fusion orbit coupling attitude perturbation analysis method according to claim 1, further comprising:
calculating to obtain a lift coefficient and a resistance coefficient of the spacecraft by using a cross-basin aerodynamic algorithm based on spacecraft height information, spacecraft size information, spacecraft shape information and deflection angle information of a solar sailboard of the spacecraft relative to a spacecraft main body;
and analyzing the influence of the aerodynamic lift and the aerodynamic resistance on the variable rate of the semi-major axis and the variable rate of the eccentricity ratio by using the spacecraft in-orbit flight orbit root perturbation analysis model based on the lift coefficient and the resistance coefficient of the spacecraft.
3. The distributed pneumatic fusion orbit coupling attitude perturbation analysis method according to claim 1, further comprising:
obtaining first external measurement data of a spacecraft, and filtering the first external measurement data by adopting a self-adaptive Kalman filtering method to obtain second external measurement data, wherein the first external measurement data comprises: the method comprises the following steps of (1) position information of a spacecraft, speed information of the spacecraft, attitude information of the spacecraft and deflection angle information of a solar sailboard of the spacecraft relative to a main body of the spacecraft;
and analyzing the influence of the spacecraft aerodynamic moment on the spacecraft attitude motion parameters by utilizing an attitude dynamics equation and a cross-basin aerodynamic algorithm based on the second external measurement data.
4. The method for analyzing perturbation of coupling posture of distributed pneumatic fusion orbit as claimed in claim 1, wherein said method adopts PCE uncertainty analysis method to perform quantitative analysis on error factors of said identification result.
5. The distributed pneumatic fusion orbit coupling attitude perturbation analysis method according to any one of claims 1-4, wherein the method utilizes GPU parallel computing to accelerate the analysis process in the method.
6. The method for analyzing perturbation of coupling attitude of distributed aerodynamic fusion orbit according to claim 1, wherein the perturbation analysis model of the number of orbits of the spacecraft in orbit specifically comprises:
Figure DEST_PATH_IMAGE001
Figure 522518DEST_PATH_IMAGE002
Figure DEST_PATH_IMAGE003
Figure 236396DEST_PATH_IMAGE004
Figure DEST_PATH_IMAGE005
Figure 360341DEST_PATH_IMAGE006
Figure DEST_PATH_IMAGE007
wherein the content of the first and second substances,
Figure 489971DEST_PATH_IMAGE008
is a transient semi-major axis of the orbit,
Figure DEST_PATH_IMAGE009
in order to determine the eccentricity of the track,
Figure 897819DEST_PATH_IMAGE010
in order to obtain the inclination angle of the track,
Figure DEST_PATH_IMAGE011
the radial angle of the near place is the radial angle of the near place,
Figure 267752DEST_PATH_IMAGE012
for the ascending point longitude to be the point of intersection longitude,
Figure DEST_PATH_IMAGE013
in order to be a true proximal angle,
Figure 3626DEST_PATH_IMAGE014
as the coefficient of the trajectory is given,
Figure DEST_PATH_IMAGE015
is a semi-major axis of the track in a long period of variation,
Figure 61581DEST_PATH_IMAGE016
the track eccentricity which is a state of long period change,
Figure DEST_PATH_IMAGE017
the track inclination angle for the long period changing state,
Figure 277930DEST_PATH_IMAGE018
is the perigee argument of the long period changing state,
Figure DEST_PATH_IMAGE019
the ascending node longitude for the long-period changing state,
Figure 5715DEST_PATH_IMAGE020
is the true paraxial angle of the long-period changing state,
Figure DEST_PATH_IMAGE021
is the ballistic coefficient of the long-period changing state,
Figure 556782DEST_PATH_IMAGE022
the semi-major axis of the track for short-period perturbation terms,
Figure DEST_PATH_IMAGE023
the track eccentricity for the short period perturbation term,
Figure 106843DEST_PATH_IMAGE024
the inclination of the track for the short period perturbation term,
Figure DEST_PATH_IMAGE025
is the perigee argument of the short-period perturbation term,
Figure 223703DEST_PATH_IMAGE026
for the ascending point longitude of the short period perturbation term,
Figure DEST_PATH_IMAGE027
is the true epipolar angle of the short-period perturbation term,
Figure 387968DEST_PATH_IMAGE028
is the ballistic coefficient of the short-period perturbation term,
Figure DEST_PATH_IMAGE029
the symbols are changed for a short period.
7. The perturbation analysis method for the coupling attitude of the distributed pneumatic fusion track according to claim 2, wherein the track semimajor axis variable rate and the eccentricity variable rate are calculated in a mode that:
Figure 177064DEST_PATH_IMAGE030
Figure DEST_PATH_IMAGE031
Figure 45663DEST_PATH_IMAGE032
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE033
being instantaneous orbit semi-major axis
Figure 157975DEST_PATH_IMAGE008
The rate of change of (a) is,
Figure 758721DEST_PATH_IMAGE034
is a vector of angular momentum
Figure DEST_PATH_IMAGE035
The rate of change of (a) is,
Figure 35113DEST_PATH_IMAGE036
integrating operator for Hamiltonian
Figure DEST_PATH_IMAGE037
The rate of change of (a) is,
Figure 707402DEST_PATH_IMAGE038
in order to obtain the coefficient of the gravity,
Figure DEST_PATH_IMAGE039
is the vector sum of the aerodynamic drag and the aerodynamic lift of the spacecraft,
Figure 939801DEST_PATH_IMAGE040
is a position vector of a spacecraft geocentric inertial coordinate system,
Figure DEST_PATH_IMAGE041
a velocity vector of a spacecraft geocentric inertial coordinate system;
in an elliptical orbit, the long period effect of the aerodynamic drag is:
Figure 258918DEST_PATH_IMAGE042
Figure DEST_PATH_IMAGE043
wherein the content of the first and second substances,
Figure 803032DEST_PATH_IMAGE044
is a ballistic coefficient corresponding to the aerodynamic drag coefficient,
Figure DEST_PATH_IMAGE045
for the instantaneous orbit semimajor axis within a long period
Figure 419958DEST_PATH_IMAGE008
The rate of change of (a) is,
Figure 851070DEST_PATH_IMAGE046
is the density of the atmosphere at the height of the near site,
Figure DEST_PATH_IMAGE047
is the height of the near-to-location,
Figure 262460DEST_PATH_IMAGE048
is the radius of the orbit at the height of the near point,
Figure DEST_PATH_IMAGE049
is the eccentricity of the track within a long period
Figure 762711DEST_PATH_IMAGE009
The rate of change of (a);
the long period effect of aerodynamic lift is:
Figure 527536DEST_PATH_IMAGE050
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE051
the ballistic coefficient is corresponding to the aerodynamic lift coefficient;
for a near-circular orbit, the long period effect of aerodynamic drag is:
Figure 734527DEST_PATH_IMAGE052
Figure DEST_PATH_IMAGE053
。
8. the method for analyzing the perturbation of the coupling attitude of the distributed pneumatic fusion track according to claim 1, wherein the perturbation analysis equation of the coupling attitude of the pneumatic fusion track is as follows:
Figure 644714DEST_PATH_IMAGE054
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE055
as the dual force vector acting on the rigid body,
Figure 168013DEST_PATH_IMAGE056
is the sum of a dual mass operator and a dual inertia operator,
Figure DEST_PATH_IMAGE057
is the change rate of the rotation of the rigid body,
Figure 861162DEST_PATH_IMAGE058
is the rotation of a rigid body.
9. The distributed pneumatic fusion orbit coupling attitude perturbation analysis method according to claim 1, wherein the recognition result is obtained by calculation in the following way:
Figure DEST_PATH_IMAGE059
Figure 719397DEST_PATH_IMAGE060
Figure DEST_PATH_IMAGE061
wherein the content of the first and second substances,
Figure 347955DEST_PATH_IMAGE062
corresponding to the position, the speed, the attitude angle and the attitude angular speed of the spacecraft,
Figure DEST_PATH_IMAGE063
corresponding to the position, the speed, the attitude angle and the change rate of the attitude angular speed of the spacecraft,
Figure 229324DEST_PATH_IMAGE064
the deflection angle of the solar panel relative to the main body,
Figure DEST_PATH_IMAGE065
is an input function;
Figure 116377DEST_PATH_IMAGE066
the observation vectors of the position, the speed, the attitude angle, the attitude angular speed and the deflection angle of the solar sailboard relative to the main body of the spacecraft are obtained;
Figure DEST_PATH_IMAGE067
for the discrete time series obtained by the external measurement,
Figure 845430DEST_PATH_IMAGE068
in order to add a matrix to the process noise,
Figure DEST_PATH_IMAGE069
in order to measure the noise addition matrix,
Figure 503944DEST_PATH_IMAGE070
in order to calculate the initial moment of time,
Figure DEST_PATH_IMAGE071
in order to calculate the time of day,
Figure 997243DEST_PATH_IMAGE072
is a state equation for the state variable,
Figure DEST_PATH_IMAGE073
is an observation equation for the state variable,
Figure 297774DEST_PATH_IMAGE074
as a discrete time series.
10. The method for analyzing the perturbation of the coupling attitude of the distributed pneumatic fusion orbit according to the claim 1, wherein the method utilizes a digital simulation system to perform parameter identification on the angle of the solar sailboard of the spacecraft to obtain an identification result.
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CN116384600B (en) * 2023-06-06 2023-08-22 中国空气动力研究与发展中心超高速空气动力研究所 Spacecraft LEO elliptical orbit attenuation process parameter forecasting method based on energy analysis
CN116894301A (en) * 2023-09-11 2023-10-17 中国空气动力研究与发展中心超高速空气动力研究所 Spacecraft windward area digital acquisition method based on face element and grid projection
CN116894301B (en) * 2023-09-11 2023-11-21 中国空气动力研究与发展中心超高速空气动力研究所 Spacecraft windward area digital acquisition method based on face element and grid projection

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