Method, device and equipment for predicting orbit state of low-orbit satellite

Abstract

The invention discloses a low-orbit satellite orbit state prediction method, device and equipment. The method comprises the following steps: defining a pseudo resistance coefficient according to an atmospheric resistance perturbation formula; decomposition operation based on pseudo resistance coefficient correlation analysis is carried out on the space environment parameters according to a VMD algorithm, space environment characteristic parameters are obtained, and the space environment parameters comprise a solar radiation flow index and a geomagnetic index; performing pseudo resistance coefficient prediction training on the initial SVM model by adopting the space environment characteristic parameters and a preset equivalent resistance coefficient to obtain an optimized SVM model; predicting a pseudo resistance coefficient at a future moment by optimizing the SVM model to obtain a pseudo resistance predicted value; and performing prediction calculation on the orbit state of the low-orbit satellite according to the pseudo resistance prediction value to obtain a state prediction result. The technical problem that in the prior art, it is difficult to ensure the influence degree of space environment changes, and consequently low-orbit satellite orbit state forecasting accuracy is poor can be solved.

Description

Technical Field

[0001] The present application relates to the field of satellite technology, and in particular to a method, device and equipment for predicting the orbital state of a low-orbit satellite. Background Art

[0002] With the rapid development of space technology, more and more satellites are being deployed into orbit. Accurate and efficient orbit prediction has become particularly critical, which not only supports rendezvous analysis and collision avoidance, but is also crucial for the safe operation of space targets. The current orbit prediction method for space targets is based on the framework of orbit determination and prediction. The orbit determination process uses observation data to obtain accurate historical orbit data and estimate related parameters. However, in the orbit prediction stage, observation data is not available, and the same physical model as the orbit determination process and the estimated atmospheric drag coefficient must be used to achieve orbit prediction. For low-orbit satellites, although conservative forces (including Earth gravity and third-body perturbations) can be modeled with good accuracy, non-conservative perturbations (i.e., atmospheric drag) may have higher uncertainty, which is manifested in the complexity and inaccuracy of atmospheric density modeling and space environment prediction.

[0003] In traditional methods, the accuracy of orbit prediction depends on the accuracy of space environment prediction within the prediction period, and accurate prediction of space environment is still a difficult problem; although the impact is small in short-term orbit prediction or when the space environment is stable. However, in the case of medium- and long-term orbit prediction or significant changes in the space environment, it is difficult to accurately estimate the atmospheric density, and the atmospheric drag coefficient value calculated in the orbit determination stage cannot adapt to the error absorption in the prediction stage, resulting in a rapid decrease in the accuracy of the prediction and a rapid increase in the error. Summary of the invention

[0004] The present application provides a method, device and equipment for predicting the orbital state of a low-orbit satellite, which is used to solve the technical problem that the existing technology is difficult to ensure the impact of space environment changes, resulting in poor accuracy in predicting the orbital state of a low-orbit satellite.

[0005] In view of this, the first aspect of the present application provides a method for predicting the orbital state of a low-orbit satellite, comprising:

[0006] The pseudo drag coefficient is defined according to the atmospheric drag perturbation formula;

[0007] Decomposing the space environment parameters based on the pseudo drag coefficient correlation analysis according to the VMD algorithm to obtain space environment characteristic parameters, wherein the space environment parameters include a solar radiation flux index and a geomagnetic index;

[0008] The spatial environment characteristic parameters and the preset equivalent drag coefficient are used to perform pseudo drag coefficient prediction training on the initial SVM model to obtain an optimized SVM model;

[0009] Predicting the pseudo resistance coefficient at a future moment by using the optimized SVM model to obtain a pseudo resistance prediction value;

[0010] The orbit state of the low-orbit satellite is predicted and calculated according to the pseudo-resistance prediction value to obtain a state prediction result.

[0011] Preferably, the decomposition operation based on the pseudo drag coefficient correlation analysis is performed on the space environment parameters according to the VMD algorithm to obtain the space environment characteristic parameters, including:

[0012] The solar radiation flux index and the geomagnetic index are normalized and then merged to obtain the spatial environment processing parameters;

[0013] The VMD algorithm is used to perform a decomposition operation on the space environment processing parameters based on the pseudo drag coefficient correlation analysis to obtain space environment characteristic parameters.

[0014] Preferably, the adopting of the spatial environment characteristic parameters and the preset equivalent drag coefficient to perform pseudo drag coefficient prediction training on the initial SVM model to obtain an optimized SVM model comprises:

[0015] The spatial environment characteristic parameters and the preset equivalent resistance coefficient are spliced ​​into an input feature vector, and the pseudo resistance historical value is used as a target variable to generate a training set;

[0016] The preset optimization algorithm and the training set are used to perform coefficient prediction training on the initial SVM model to obtain an optimized SVM model.

[0017] Preferably, the method of using the spatial environment characteristic parameters and the preset equivalent drag coefficient to perform pseudo drag coefficient prediction training on the initial SVM model to obtain an optimized SVM model further includes:

[0018] The orbit state of low-orbit satellites is predicted by numerical integration to obtain the predicted ephemeris;

[0019] Calculating a traditional prediction error based on the orbital dynamics model and the predicted ephemeris;

[0020] The preset equivalent drag coefficient is obtained by performing iterative optimization analysis based on the traditional prediction error through the golden section search algorithm.

[0021] Preferably, the predicting and calculating the orbital state of the low-orbit satellite according to the pseudo-resistance prediction value to obtain the state prediction result includes:

[0022] Calculating an atmospheric resistance perturbation value according to the pseudo resistance prediction value;

[0023] The atmospheric drag perturbation value is substituted into the orbital dynamics model formula to perform prediction calculation on the orbital state of the low-orbit satellite to obtain a state prediction result.

[0024] The second aspect of the present application provides a low-orbit satellite orbit state prediction device, comprising:

[0025] A coefficient definition unit is used to define a pseudo drag coefficient according to the atmospheric drag perturbation formula;

[0026] A correlation decomposition unit, used for performing a decomposition operation on the space environment parameters based on the pseudo drag coefficient correlation analysis according to the VMD algorithm to obtain space environment characteristic parameters, wherein the space environment parameters include a solar radiation flux index and a geomagnetic index;

[0027] A model training unit, used to perform pseudo drag coefficient prediction training on the initial SVM model using the spatial environment characteristic parameters and the preset equivalent drag coefficient to obtain an optimized SVM model;

[0028] A coefficient prediction unit, used to predict the pseudo resistance coefficient at a future moment by using the optimized SVM model to obtain a pseudo resistance prediction value;

[0029] The orbit prediction unit is used to predict and calculate the orbit state of the low-orbit satellite according to the pseudo-drag prediction value to obtain a state prediction result.

[0030] Preferably, the association decomposition unit is specifically used to:

[0031] The solar radiation flux index and the geomagnetic index are normalized and then merged to obtain the spatial environment processing parameters;

[0032] The VMD algorithm is used to perform a decomposition operation on the space environment processing parameters based on the pseudo drag coefficient correlation analysis to obtain space environment characteristic parameters.

[0033] Preferably, the model training unit is specifically used for:

[0034] The spatial environment characteristic parameters and the preset equivalent resistance coefficient are spliced ​​into an input feature vector, and the pseudo resistance historical value is used as a target variable to generate a training set;

[0035] The preset optimization algorithm and the training set are used to perform coefficient prediction training on the initial SVM model to obtain an optimized SVM model.

[0036] Preferably, it also includes:

[0037] The traditional prediction unit is used to predict the orbit state of the low-orbit satellite by numerical integration to obtain the predicted ephemeris;

[0038] An error calculation unit, used for calculating a traditional prediction error based on an orbital dynamics model and the predicted ephemeris;

[0039] The optimization calculation unit is used to perform iterative optimization analysis according to the traditional prediction error through a golden section search algorithm to obtain a preset equivalent resistance coefficient.

[0040] A third aspect of the present application provides a low-orbit satellite orbit state prediction device, the device comprising a processor and a memory;

[0041] The memory is used to store program code and transmit the program code to the processor;

[0042] The processor is used to execute the low-orbit satellite orbit state prediction method described in the first aspect according to the instructions in the program code.

[0043] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0044] In the present application, a method for predicting the orbital state of a low-orbit satellite is provided, including: defining a pseudo-drag coefficient according to an atmospheric drag perturbation formula; performing a decomposition operation on space environment parameters based on pseudo-drag coefficient correlation analysis according to a VMD algorithm to obtain space environment characteristic parameters, the space environment parameters including a solar radiation flux index and a geomagnetic index; using the space environment characteristic parameters and a preset equivalent drag coefficient to perform pseudo-drag coefficient prediction training on an initial SVM model to obtain an optimized SVM model; predicting the pseudo-drag coefficient at a future moment by optimizing the SVM model to obtain a pseudo-drag prediction value; predicting and calculating the orbital state of the low-orbit satellite according to the pseudo-drag prediction value to obtain a state prediction result.

[0045] The low-orbit satellite orbit state prediction method provided by this application defines the concept of pseudo-drag coefficient, associates the space environment parameters with the pseudo-drag coefficient through the VMD algorithm, and transforms the low-orbit satellite orbit state prediction problem into a pseudo-drag coefficient prediction problem; and on this basis, a pseudo-drag coefficient prediction model is constructed, which can ensure that an accurate pseudo-drag coefficient is obtained while considering the changes in the space environment, thereby ensuring the accuracy and reliability of the low-orbit satellite orbit state prediction results based on this; this process specifically analyzes the quantitative impact of space environment changes on the pseudo-drag coefficient, which is more in line with the actual situation and can meet the current low-orbit satellite orbit prediction technology requirements. Therefore, this application can solve the technical problem that the existing technology is difficult to ensure the degree of influence of space environment changes, resulting in poor accuracy of low-orbit satellite orbit state prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of a flow chart of a method for predicting the orbital state of a low-orbit satellite provided in an embodiment of the present application;

[0047] Figure 2 A schematic diagram of the structure of a low-orbit satellite orbit state prediction device provided in an embodiment of the present application;

[0048] Figure 3 A schematic diagram of normalized correlation analysis between space environment parameters and pseudo drag coefficients provided in an embodiment of the present application;

[0049] Figure 4 A schematic diagram of a flow chart of solving spatial environment characteristic parameters based on a VMD algorithm provided in an embodiment of the present application;

[0050] Figure 5 A graph showing the correlation between the spatial environment characteristic parameters and the pseudo drag coefficient provided in the embodiment of the present application;

[0051] Figure 6 A schematic diagram showing a comparison of radial errors of low-orbit satellite orbit prediction based on three different methods provided in an embodiment of the present application;

[0052] Figure 7 A schematic diagram showing a comparison of tracking errors of low-orbit satellite orbit prediction based on three different methods provided in an embodiment of the present application;

[0053] Figure 8 A schematic diagram showing a comparison of normal errors of low-orbit satellite orbit prediction based on three different methods provided in an embodiment of the present application;

[0054] Fig. 9 A schematic diagram showing a comparison of radial prediction errors between the scheme provided by the present application and the traditional prediction scheme provided as an application example of the present application;

[0055] Fig.10 A schematic diagram comparing the track prediction errors of the scheme provided by the present application and the traditional prediction scheme provided for the application example of the present application;

[0056] Fig.11 A schematic diagram comparing the normal forecast error of the scheme provided by the present application and the traditional forecast scheme is provided as an application example of the present application. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] For easier understanding, see Figure 1 , an embodiment of a method for predicting the orbital state of a low-orbit satellite provided by the present application includes:

[0059] Step 101: Define the pseudo drag coefficient according to the atmospheric drag perturbation formula.

[0060] The atmospheric drag perturbation formula is expressed as:

[0061]

[0062] in, represents the atmospheric drag perturbation, which refers to the acceleration caused by atmospheric drag. is the mass of the current satellite, is the windward area, is the surface-to-mass ratio, is the atmospheric drag coefficient, is the atmospheric density, which is related to the orbital altitude, Indicates v e The modulus value, v e represents the velocity vector of the spacecraft relative to the atmosphere. All parameters on the right side of the equation have uncertainties, and most studies focus on the atmospheric density. The atmospheric drag coefficient has a complex dependence on the geomagnetic index, solar radiation flux index and other space environmental parameters. In traditional precise orbit determination and prediction, it is estimated together with the state of the spacecraft to absorb the errors of the atmospheric density model, satellite surface mass ratio, and satellite attitude change. right The partial derivative of is expressed as:

[0063]

[0064] According to the above two formulas, changes in space environment parameters will affect the atmospheric density. The calculation of atmospheric drag coefficient Moreover, in the case of medium- and long-term orbit forecasts or significant changes in the space environment, the atmospheric density It is difficult to estimate accurately, which affects the atmospheric drag coefficient The calculation results in decreased orbit prediction accuracy and increased errors.

[0065] Therefore, this embodiment defines the pseudo drag coefficient Since the change of space environment will eventually reflect the orbital state of the satellite, this embodiment uses the real orbit data to introduce the change of space environment into the atmospheric drag coefficient. At this time, it will be used to calculate the atmospheric density. If the space environment parameters are set to fixed values, then the atmospheric drag coefficient does not need to be solved for orbit determination, and it also loses its original physical meaning and is defined as a pseudo drag coefficient. .

[0066] Step 102: Decomposition operation based on pseudo-drag coefficient correlation analysis is performed on the space environment parameters according to the VMD algorithm to obtain space environment characteristic parameters, where the space environment parameters include solar radiation flux index and geomagnetic index.

[0067] Further, step 102 includes:

[0068] The solar radiation flux index and the geomagnetic index are normalized and then merged to obtain the spatial environment processing parameters;

[0069] The VMD algorithm is used to decompose the space environment processing parameters based on the correlation analysis of the pseudo drag coefficient to obtain the space environment characteristic parameters.

[0070] After analysis, it is found that the space environment parameters are the key parameters that affect the atmospheric drag coefficient. Therefore, this embodiment uses the VMD algorithm to associate the changes in the space environment with the pseudo drag coefficient. In order to confirm that there is a strong correlation between the two, the solar radiation flux index and the geomagnetic index in the same time period can be normalized and analyzed with the pseudo drag coefficient. Please refer to Figure 3 , pseudo resistance coefficient and solar radiation flux index The overall trend is relatively close, and the low-frequency characteristics are dominant. The Pearson correlation coefficient between the two is 0.622; the geomagnetic index It has a certain influence on the pseudo drag coefficient locally, which is manifested as the influence of some high-frequency characteristics. The Pearson correlation coefficient between the two is 0.178.

[0071] The VMD algorithm is the variational mode decomposition algorithm, which is an adaptive signal decomposition method that can effectively process non-stationary nonlinear signals. The basic decomposition mechanism is to define each intrinsic mode function IMF component as a non-smooth component with amplitude modulation-frequency modulation characteristics; then perform Hilbert transform, extract the one-sided spectrum, and obtain the IMF spectrum; modulate the exponential term of the center frequency of the mixed spectrum response to the baseband of the response; then use the Gaussian smoothing algorithm to construct the objective function on this basis, and solve it under the constraints to obtain the optimal solution, that is, the target decomposition result.

[0072] From the working principle of the VMD algorithm, it can be seen that this method has obvious advantages in extracting the frequency characteristics of the signal, so this embodiment uses the VMD algorithm to extract the solar radiation flux index. and geomagnetic index The fusion and decomposition process is performed to generate the spatial environment characteristic parameters. Specifically, the solar radiation flux index and geomagnetic index Normalization is performed to normalize the time domain amplitude effects of the two to the same scale; then the normalized results are merged to obtain the spatial environment processing parameters; finally, the VMD algorithm is used to decompose the spatial environment processing parameters, filter out the high-frequency components, and obtain the spatial environment characteristic parameters. For the specific process, please refer to Figure 4 By normalizing and comparing the obtained spatial environment characteristic parameters with the pseudo drag coefficient, we can obtain Figure 5 From the results shown, it can be found that the spatial environment characteristic parameters are strongly correlated with the pseudo drag coefficient, and the Pearson coefficient between the two is calculated to be 0.897. Therefore, this embodiment has associated the changes in the spatial environment with the pseudo drag coefficient, and the correlation is strongly correlated. Therefore, the pseudo drag coefficient can be predicted based on the spatial environment parameters in the future; the decomposition operation based on the correlation analysis of the pseudo drag coefficient in this embodiment refers to establishing an association relationship between the spatial environment parameters and the pseudo drag coefficient to ensure the scientificity and rationality of the subsequent prediction operation.

[0073] Step 103: Use the spatial environment characteristic parameters and the preset equivalent drag coefficient to perform pseudo drag coefficient prediction training on the initial SVM model to obtain an optimized SVM model.

[0074] Further, step 103 includes:

[0075] The spatial environment characteristic parameters and the preset equivalent resistance coefficient are spliced ​​into the input feature vector, and the pseudo resistance historical value is used as the target variable to generate a training set;

[0076] The preset optimization algorithm and training set are used to train the initial SVM model for pseudo drag coefficient prediction, and the optimized SVM model is obtained.

[0077] Since there is a strong correlation between the spatial environment characteristic parameters and the pseudo drag coefficient, the present embodiment can construct a prediction model to predict the pseudo drag coefficient based on the spatial environment characteristic parameters; the spatial environment characteristic parameters can be used as the input feature vector of the model, and the pseudo drag coefficient is the output target variable.

[0078] In order to demonstrate the advantages of the pseudo-drag coefficient determination method of this embodiment, the preset equivalent drag coefficient calculated based on the traditional method is used as a reference. If it is better than this level, the accuracy is improved. After the initial SVM model is constructed in this embodiment, a training set is constructed based on the spatial environment characteristic parameters and the preset equivalent drag coefficient. The input feature vector can be obtained by splicing the two. The pseudo-drag history value is used as the corresponding label, that is, the target variable, to obtain the model training set, which can be specifically expressed as ,in, represents the preset equivalent drag coefficient; the input feature vector is X, the output target vector is Y, and the learning goal of the model is to achieve 's mapping.

[0079] The historical values ​​of pseudo drag in the training set can be calculated based on the concept definition. In this embodiment, the changes in the space environment are introduced into the atmospheric drag coefficient using real orbit data. At this time, the space environment parameters for calculating the atmospheric density are fixed, and the pseudo drag coefficient can be calculated. The pseudo drag coefficient The solution requires a high-precision orbital dynamics model and real satellite orbit data; this embodiment uses the orbital dynamics model in the geocentric inertial ECI coordinate system:

[0080]

[0081] in, and Respectively represent the position and velocity of the space target in the inertial system, , Represents the Euclidean norm of a vector; is the Earth's gravitational constant; represents the perturbation acceleration vector, which is caused by the non-spherical perturbation , three-body gravitational perturbation , atmospheric drag perturbation , Solar pressure perturbation and other unmodeled perturbations Except for the atmospheric drag perturbation, the other perturbation models are already sufficiently accurate or have little impact on low-orbit satellites, so they will not be discussed in detail.

[0082] Set the spatial environment parameters for calculating atmospheric density to fixed values, given the initial epoch and the initial orbital state of the low-orbit satellite , , the orbital state at any future epoch Can be considered as a univariate function of the pseudo drag coefficient , the specific formula is as follows:

[0083]

[0084] The solution can be obtained by numerical integration calculation of the orbital dynamics model mentioned above.

[0085] Satellite measurement data is used as the real satellite orbit data, which is called precise ephemeris. Based on the precise ephemeris and the principle of minimizing the prediction error e(t), the objective function of minimizing the prediction error can be constructed:

[0086]

[0087] Among them, x true(t) is the precise ephemeris data in the inertial system, and e(t) is the error value in the orbital coordinate system along the track direction, normal direction, and radial direction.

[0088] Pseudo drag coefficient The optimization calculation process can be regarded as a problem of finding the minimum value of a single variable function within a certain interval. The golden section search is a method of finding the extreme value of a function by searching within a known interval through the golden ratio and continuously reducing the maximum value of the function within the known interval. Therefore, this embodiment selects the golden section search algorithm to perform iterative optimization calculation according to the objective function of the forecast error to obtain the pseudo resistance coefficient when the forecast error is the smallest. It can be understood that the pseudo resistance history values ​​required for constructing the training set can be calculated using the above method; the initial SVM model can be optimized and trained using the training set and the preset optimization method to obtain an optimized SVM model.

[0089] Furthermore, step 103, before that, also includes:

[0090] The orbit state of low-orbit satellites is predicted by numerical integration to obtain the predicted ephemeris;

[0091] Calculate the traditional prediction error based on the orbital dynamics model and the predicted ephemeris;

[0092] The preset equivalent drag coefficient is obtained by iterative optimization analysis based on the traditional prediction error through the golden section search algorithm.

[0093] According to the calculation of pseudo drag history values ​​above, it can be seen that the calculation process of the preset equivalent drag coefficient is basically the same as that of the pseudo drag history value. The only difference is that the prediction ephemeris obtained by the traditional method is used in the calculation of the preset equivalent drag coefficient, while the calculation process of the pseudo drag history value uses the precise ephemeris. The prediction result obtained by the traditional method, that is, the prediction ephemeris, is used as the benchmark to calculate the preset equivalent drag coefficient, which can represent a new prediction accuracy.

[0094] Given that the preset equivalent resistance coefficient can be solved , then we can compare the preset equivalent resistance coefficients and pseudo drag coefficient For orbit prediction accuracy, see Figure 6 , Figure 7 and Figure 8Taking a low-orbit satellite as an example, the orbit altitude is about 400km. Under the same conditions, the pseudo-drag coefficient prediction, preset equivalent drag coefficient prediction and pure traditional method prediction are carried out respectively, and the orbit prediction error in the RTN coordinate system can be obtained respectively. By comparison, it can be found that the prediction based on the pseudo-drag coefficient has obvious improvement in the total position error compared with the traditional prediction, which is mainly manifested in the along-track direction. The prediction based on the preset equivalent drag coefficient is more consistent with the traditional prediction.

[0095] The prediction error comparison in this embodiment is a unified normalized scaling, with the pseudo drag coefficient The maximum prediction error of is unit quantity, and the scaled error expression is:

[0096]

[0097] in, is the error after scaling, is the selected pseudo drag coefficient The forecast error, is other forms of forecast error, represent radial, track and normal directions respectively.

[0098] After comparative analysis, it can be found that the orbit prediction error of low-orbit satellites is mainly manifested in the direction of the track, and the magnitude of the error in the track direction is much greater than that in the radial and normal directions; the pseudo-drag coefficient It can effectively absorb the orbit prediction error, which mainly affects the track error. The radial error is not as large as the track error but can still be improved, and the normal error has basically no effect; preset equivalent drag coefficient The prediction of can approximately represent the prediction results of the traditional method, and also explains the pseudo resistance coefficient proposed in this embodiment. The scientificity and rationality of the concept.

[0099] Step 104: Predict the pseudo resistance coefficient at a future time by optimizing the SVM model to obtain a pseudo resistance prediction value.

[0100] The optimized SVM model obtained after training can predict the pseudo drag coefficient at the future time according to the current spatial environment characteristic parameters and the current preset equivalent drag coefficient, which is called the pseudo drag prediction value. The current parameter can refer to the latest parameter information at the current time, or the most recently calculated one, which can be determined according to the actual situation.

[0101] Step 105: Predict and calculate the orbital state of the low-orbit satellite based on the pseudo-drag prediction value to obtain a state prediction result.

[0102] Further, step 105 includes:

[0103] Calculate the atmospheric drag perturbation value based on the pseudo drag prediction value;

[0104] The atmospheric drag perturbation value is substituted into the orbital dynamics model formula to predict the orbital state of the low-orbit satellite and obtain the state prediction result.

[0105] The atmospheric drag perturbation value can be calculated according to the atmospheric drag perturbation formula. By substituting the obtained atmospheric drag perturbation value into the formula of the orbital dynamics model, the specific position and speed of the satellite in the orbit are calculated, and the state prediction result of the satellite orbit can be obtained.

[0106] For ease of understanding, this application also provides an experimental calculation case, selecting a low-orbit satellite with an orbit altitude of 400km and an orbit forecast for 10 days. The precise ephemeris data of the first 70 days was used to establish the pseudo-drag history value for the training phase of machine learning. In the forecast phase, the starting epoch starts from the 70th day, and the pseudo-drag coefficient required for the starting epoch is predicted, thereby realizing intelligent orbit forecasting. The normalized orbit forecast results are as follows: Figure 9-11 As shown. Figure 9-11 It can be seen that the tracking error is the main error source for low-orbit satellites, which comes from the uncertainty of atmospheric drag. The present application scheme has achieved good performance in actual orbit prediction applications.

[0107] The low-orbit satellite orbit state prediction method provided in the embodiment of the present application defines the concept of pseudo-drag coefficient, associates the space environment parameters with the pseudo-drag coefficient through the VMD algorithm, and transforms the low-orbit satellite orbit state prediction problem into the pseudo-drag coefficient prediction problem; and on this basis, a pseudo-drag coefficient prediction model is constructed, which can ensure that an accurate pseudo-drag coefficient is obtained while considering the changes in the space environment, thereby ensuring the accuracy and reliability of the low-orbit satellite orbit state prediction results based on this; this process specifically analyzes the quantitative impact of space environment changes on the pseudo-drag coefficient, which is more in line with the actual situation and can meet the current low-orbit satellite orbit prediction technology requirements. Therefore, the embodiment of the present application can solve the technical problem that the existing technology is difficult to ensure the degree of influence of space environment changes, resulting in poor accuracy of low-orbit satellite orbit state prediction.

[0108] For easier understanding, see Figure 2 The present application provides an embodiment of a low-orbit satellite orbit state prediction device, comprising:

[0109] A coefficient definition unit 201, used to define a pseudo drag coefficient according to an atmospheric drag perturbation formula;

[0110] The correlation decomposition unit 202 is used to perform a decomposition operation on the space environment parameters based on the pseudo resistance coefficient correlation analysis according to the VMD algorithm to obtain the space environment characteristic parameters, where the space environment parameters include the solar radiation flux index and the geomagnetic index;

[0111] A model training unit 203 is used to perform pseudo drag coefficient prediction training on the initial SVM model using spatial environment characteristic parameters and a preset equivalent drag coefficient to obtain an optimized SVM model;

[0112] A coefficient prediction unit 204 is used to predict the pseudo resistance coefficient at a future time by optimizing the SVM model to obtain a pseudo resistance prediction value;

[0113] The orbit prediction unit 205 is used to predict the orbit state of the low-orbit satellite according to the pseudo-drag prediction value to obtain a state prediction result.

[0114] Furthermore, the association decomposition unit 202 is specifically configured to:

[0115] The solar radiation flux index and the geomagnetic index are normalized and then merged to obtain the spatial environment processing parameters;

[0116] The VMD algorithm is used to decompose the space environment processing parameters based on the correlation analysis of the pseudo drag coefficient to obtain the space environment characteristic parameters.

[0117] Furthermore, the model training unit 203 is specifically used for:

[0118] The spatial environment characteristic parameters and the preset equivalent resistance coefficient are spliced ​​into the input feature vector, and the pseudo resistance historical value is used as the target variable to generate a training set;

[0119] The preset optimization algorithm and training set are used to train the initial SVM model for pseudo drag coefficient prediction, and the optimized SVM model is obtained.

[0120] Furthermore, it also includes:

[0121] The traditional prediction unit 206 is used to predict the orbit state of the low-orbit satellite by numerical integration to obtain the predicted ephemeris;

[0122] An error calculation unit 207, used to calculate a traditional prediction error based on an orbital dynamics model and a prediction ephemeris;

[0123] The optimization calculation unit 208 is used to perform iterative optimization analysis based on the traditional prediction error through the golden section search algorithm to obtain a preset equivalent resistance coefficient.

[0124] The present application also provides a low-orbit satellite orbit state prediction device, the device comprising a processor and a memory;

[0125] The memory is used to store the program code and transmit the program code to the processor;

[0126] The processor is used to execute the low-orbit satellite orbit state prediction method in the above method embodiment according to the instructions in the program code.

[0127] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0128] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0130] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0131] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting the orbital state of a low-orbit satellite, characterized in that: include: The pseudo drag coefficient is defined according to the atmospheric drag perturbation formula; Decomposing the space environment parameters based on the pseudo drag coefficient correlation analysis according to the VMD algorithm to obtain space environment characteristic parameters, wherein the space environment parameters include a solar radiation flux index and a geomagnetic index; The spatial environment characteristic parameters and the preset equivalent drag coefficient are used to perform pseudo drag coefficient prediction training on the initial SVM model to obtain an optimized SVM model; Predicting the pseudo resistance coefficient at a future moment by using the optimized SVM model to obtain a pseudo resistance prediction value; The orbit state of the low-orbit satellite is predicted and calculated according to the pseudo-resistance prediction value to obtain a state prediction result.

2. The method for predicting the orbital state of a low-orbit satellite according to claim 1, characterized in that: The decomposition operation based on the pseudo drag coefficient correlation analysis is performed on the space environment parameters according to the VMD algorithm to obtain the space environment characteristic parameters, including: The solar radiation flux index and the geomagnetic index are normalized and then merged to obtain the spatial environment processing parameters; The VMD algorithm is used to perform a decomposition operation on the space environment processing parameters based on the pseudo drag coefficient correlation analysis to obtain space environment characteristic parameters.

3. The method for predicting the orbit state of a low-orbit satellite according to claim 1, characterized in that: The method of using the spatial environment characteristic parameters and the preset equivalent drag coefficient to perform pseudo drag coefficient prediction training on the initial SVM model to obtain an optimized SVM model includes: The spatial environment characteristic parameters and the preset equivalent resistance coefficient are spliced ​​into an input feature vector, and the pseudo resistance historical value is used as a target variable to generate a training set; The preset optimization algorithm and the training set are used to perform pseudo drag coefficient prediction training on the initial SVM model to obtain an optimized SVM model.

4. The method for predicting the orbital state of a low-orbit satellite according to claim 1, characterized in that: The pseudo drag coefficient prediction training of the initial SVM model is performed by using the spatial environment characteristic parameters and the preset equivalent drag coefficient to obtain an optimized SVM model, and the method further includes: The orbit state of low-orbit satellites is predicted by numerical integration to obtain the predicted ephemeris; Calculating a traditional prediction error based on the orbital dynamics model and the predicted ephemeris; The preset equivalent drag coefficient is obtained by performing iterative optimization analysis based on the traditional prediction error through the golden section search algorithm.

5. The method for predicting the orbital state of a low-orbit satellite according to claim 1, characterized in that: The step of predicting and calculating the orbital state of the low-orbit satellite according to the pseudo-resistance prediction value to obtain a state prediction result includes: Calculating an atmospheric resistance perturbation value according to the pseudo resistance prediction value; The atmospheric drag perturbation value is substituted into the orbital dynamics model formula to perform prediction calculation on the orbital state of the low-orbit satellite to obtain a state prediction result.

6. A low-orbit satellite orbit state prediction device, characterized in that: include: A coefficient definition unit is used to define a pseudo drag coefficient according to the atmospheric drag perturbation formula; A correlation decomposition unit, used for performing a decomposition operation on the space environment parameters based on the pseudo drag coefficient correlation analysis according to the VMD algorithm to obtain space environment characteristic parameters, wherein the space environment parameters include a solar radiation flux index and a geomagnetic index; A model training unit, used to perform pseudo drag coefficient prediction training on the initial SVM model using the spatial environment characteristic parameters and the preset equivalent drag coefficient to obtain an optimized SVM model; A coefficient prediction unit, used to predict the pseudo resistance coefficient at a future moment by using the optimized SVM model to obtain a pseudo resistance prediction value; The orbit prediction unit is used to predict and calculate the orbit state of the low-orbit satellite according to the pseudo-drag prediction value to obtain a state prediction result.

7. The low-orbit satellite orbit state prediction device according to claim 6, characterized in that: The association decomposition unit is specifically used for: The solar radiation flux index and the geomagnetic index are normalized and then merged to obtain the spatial environment processing parameters; The VMD algorithm is used to perform a decomposition operation on the space environment processing parameters based on the pseudo drag coefficient correlation analysis to obtain space environment characteristic parameters.

8. The low-orbit satellite orbit state prediction device according to claim 6, characterized in that: The model training unit is specifically used for: The spatial environment characteristic parameters and the preset equivalent resistance coefficient are spliced ​​into an input feature vector, and the pseudo resistance historical value is used as a target variable to generate a training set; The preset optimization algorithm and the training set are used to perform pseudo drag coefficient prediction training on the initial SVM model to obtain an optimized SVM model.

9. The low-orbit satellite orbit state prediction device according to claim 6, characterized in that: Also includes: The traditional prediction unit is used to predict the orbit state of the low-orbit satellite by numerical integration to obtain the predicted ephemeris; An error calculation unit, used for calculating a traditional prediction error based on an orbital dynamics model and the predicted ephemeris; The optimization calculation unit is used to perform iterative optimization analysis according to the traditional prediction error through a golden section search algorithm to obtain a preset equivalent resistance coefficient.

10. A low-orbit satellite orbit state prediction device, characterized in that: The device comprises a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the low-orbit satellite orbit state prediction method described in any one of claims 1-5 according to the instructions in the program code.

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