Measurement station system error correction method, system, equipment and medium
By using a combined orbit determination method with observation data from SLR and UXB, the system errors of the Deep Space Network stations are calibrated and compensated, solving the problems of hardware delay and time delay in the Deep Space Network stations and realizing high-precision spacecraft orbit observation.
Patent Information
- Application Number
- CN202510792958.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-13
- Publication Date
- 2025-10-31
AI Technical Summary
The Deep Space Network's tracking stations suffer from hardware delays and transponder delays that are difficult to account for, resulting in insufficient accuracy in spacecraft orbit observations and failing to meet the requirements for high-precision observations.
The combined orbit determination method is adopted, which uses observation data from the laser rangefinder station (SLR) and the deep space network tracking station (UXB). By constructing a set of normal equations and combining the weights of each observation data, multiple iterative calculations are performed to finally calibrate and compensate for the station system errors.
It has improved the accuracy of spacecraft orbit observation, with orbit determination accuracy better than 10m and velocity accuracy better than 1mm/s, significantly improving the observation accuracy of deep space exploration missions.
Smart Images
Figure CN120871184A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of aerospace telemetry and control, and specifically relates to a method, system, computer equipment and storage medium for correcting station system errors. Background Technology
[0002] In the aerospace field, the continuous advancement of deep space exploration missions places extremely high demands on the accuracy of spacecraft orbit observations, and the performance of deep space network stations, as key observation infrastructure, is of paramount importance. With their outstanding advantages of long operating range and excellent signal continuity, deep space network stations have become the core force for tracking, observing, and communicating with distant spacecraft, providing a solid guarantee for the successful implementation of various deep space exploration missions.
[0003] However, deep space network tracking stations face numerous challenges in actual operation, with systemic bias being particularly prominent. These biases primarily originate from station hardware delays, transponder delays, and atmospheric propagation delays. While atmospheric propagation delay errors can be largely offset using ground-based atmospheric delay calibration equipment, station hardware delays and transponder delays are difficult to effectively offset by the stations themselves. These two types of delays become the main sources of systematic errors affecting observation accuracy, severely interfering with the accuracy of precise spacecraft orbit determination, making accurate spacecraft orbit determination an extremely challenging task. To ensure the precise execution of deep space exploration missions, accurate on-orbit calibration of these errors is urgently needed.
[0004] In previous studies, researchers have proposed various solutions to related problems. Addressing the issue of small geometric variations in geostationary GEO satellites, making it impossible to self-subtract systematic errors in transponder ranging, a parallel station comparison method was proposed using advanced laser ranging technology. This successfully improved the systematic deviation calibration accuracy to the sub-meter level, mitigating the impact of errors on observation accuracy to some extent. A systematic deviation calibration method based on standard orbit fitting residuals was also proposed. The unique feature of this method is that it can not only calibrate observational deviations but also simultaneously correct timescale deviations, effectively compensating for the shortcomings of laser ranging calibration methods in observation, and providing new ideas and approaches for reducing systematic errors and improving observation accuracy. Although these existing solutions have achieved certain results in their respective application scenarios, the problems of large systematic errors and insufficient accuracy in spacecraft orbit observations still exist, failing to fully meet the growing demand for high-precision observations. Summary of the Invention
[0005] To address the problem of large system errors, this invention provides a method for correcting system errors at a monitoring station, a system computer device, and a storage medium.
[0006] To achieve the above objectives, the present invention provides a method for correcting station system errors, comprising: The observation matrix A1 of the distance from the satellite to the SLR is obtained through the laser ranging station SLR, and the observation matrix A2 of the distance from the satellite to the UXB and the satellite velocity is obtained through the Deep Space Network tracking station UXB. The weights of each observation in matrix A1 and matrix A2 are then determined.
[0007] The SLR observation equation is established based on matrix A1; the parameters to be estimated generated in the UXB measurement are used as unknown parameters to establish the UXB observation equation with matrix A2; the orbital motion equation of the satellite is established, and the SLR observation equation, UXB observation equation, and orbital motion equation of the satellite are superimposed according to weights to construct a set of normal equations.
[0008] Using the SLR observation equations and the satellite's orbital motion equations, the theoretical prediction matrix V1 for the distance from the satellite to the SLR is calculated. Similarly, using the UXB observation equations and the satellite's orbital motion equations, the theoretical prediction matrices V2 for the distance from the satellite to the UXB and the satellite's velocity are calculated. The residual matrix of the SLR is calculated from the difference between matrix A1 and matrix V1, and the residual matrix of the UXB is calculated from the difference between matrix A2 and matrix V2. Based on the residual matrices of the SLR and UXB, the correction values for the estimated parameters are solved using the least squares method. These correction values are then substituted into the SLR and UXB observation equations for further calculation. Through multiple iterations, the final correction values for the estimated parameters are obtained, and these final correction values are used to calibrate and compensate for the UXB measurement process.
[0009] Preferably, after obtaining the observation matrix A1 of the distance between the satellite and the SLR through the laser ranging station SLR, and the observation matrix A2 of the distance between the satellite and the UXB and the satellite velocity through the Deep Space Network tracking station UXB, the method further includes preprocessing A1 and A2, specifically removing and correcting outliers in A1 and A2, and processing all data of the same type in the two matrices into a unified unit of measurement.
[0010] Preferably, the weights of each observation in matrix A1 and matrix A2 are determined by a weight ratio of 1:500 between A2 and A1, wherein the weight ratio of the distance observation from the satellite to UXB obtained by the Deep Space Network tracking station UXB to the satellite velocity observation obtained by the Deep Space Network tracking station UXB in A2 is 1:10000.
[0011] Preferably, before constructing the normal equation set, the method further includes converting the orbital motion equations into linear variational equations using variational methods, calculating the partial derivatives of the satellite orbital parameters from the variational equations, and using the partial derivatives to perform linear gradient information fusion of matrices A1 and A2 with the orbital motion equations.
[0012] Preferably, the residual matrix based on SLR and the residual matrix based on UXB are used to solve the estimated parameter correction amount of the normal equation system using the least squares method, and the estimated parameter correction amount is substituted into the SLR observation equation and the UXB observation equation for further calculation; the final estimated parameter correction amount is obtained through multiple iterations, including: The least squares method is used to solve the system of normal equations to obtain the correction amount of the parameters to be estimated; Substitute the estimated parameter corrections into the SLR observation equations and UXB observation equations, and reconstruct the normal equation set with the orbital motion equations. Calculate the new residual matrices of SLR and UXB. If either the residual matrix of SLR or UXB is greater than a set threshold, repeat the above steps. If both the residual matrices of SLR and UXB are less than the set threshold, stop the iteration and obtain the final parameter correction amount.
[0013] The present invention also provides a station system error correction system, comprising: The data acquisition module is used to acquire the observation matrix A1 of the distance between the satellite and the SLR through the laser ranging station SLR, and the observation matrix A2 of the distance between the satellite and the UXB and the satellite velocity through the Deep Space Network tracking station UXB, and to determine the weight of each observation in matrix A1 and matrix A2.
[0014] The normal equations construction module is used to establish the SLR observation equations based on matrix A1; to establish the UXB observation equations by taking the parameters to be estimated generated in the UXB measurement as unknown parameters and matrix A2; to establish the satellite's orbital motion equations; and to construct the normal equations by superimposing the SLR observation equations, UXB observation equations, and satellite's orbital motion equations according to their weights.
[0015] The normal equation solving module is used to calculate the theoretical prediction matrix V1 of the distance from the satellite to the SLR using the SLR observation equations and the satellite's orbital motion equations, and the theoretical prediction matrix V2 of the distance from the satellite to the UXB and the satellite's velocity using the UXB observation equations and the satellite's orbital motion equations. The residual matrix of the SLR is calculated from the difference between matrix A1 and matrix V1, and the residual matrix of the UXB is calculated from the difference between matrix A2 and matrix V2. Based on the residual matrices of the SLR and UXB, the correction values of the estimated parameters are solved using the least squares method, and these correction values are then substituted into the SLR and UXB observation equations for further calculation. The final correction values of the estimated parameters are obtained through multiple iterations.
[0016] The verification module is used to calibrate and compensate the UXB measurement process using the final estimated parameter correction.
[0017] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement any of the steps in the station system error correction method.
[0018] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, can execute any of the steps in the station system error correction method.
[0019] The station system error correction method provided by this invention has the following beneficial effects: This invention employs a combined orbit determination method, combining the laser ranging station (SLR), the deep space network tracking station, and the orbital motion equations to construct a normal equation. Based on the different weights of each type of observation data, observation weights are designed. Partial derivatives are calculated based on the orbital motion equations and variational equations to provide the normal equation coefficients for each measurement method, which are then fused and superimposed. Through orbit iterative improvement and orbit determination residual editing, the orbital parameters of each satellite are estimated. Addressing the systematic errors in the deep space network stations that are difficult to subtract themselves, the invention utilizes the high accuracy and lack of systematic bias of the SLR observations as an external means. In the fusion solution model, a sparse representation model of the measurement systematic errors is established to calibrate and compensate for the deep space network's systematic biases, thereby improving orbit determination performance. Attached Figure Description
[0020] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0021] Figure 1 This is a flowchart of a station system error correction method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the deep space network system error calibration according to an embodiment of the present invention; Figure 3 This is the orbit determination process based on combined multi-ground observation data in an embodiment of the present invention; Figure 4 This is a tracking time period system for deep space network stations according to an embodiment of the present invention; Figure 5 This is a satellite SLR observation of an embodiment of the present invention; Figure 6 The 3D orbit determination position of the high-orbit satellite in this embodiment of the invention; Figure 7 The 3D velocity accuracy of high-orbit satellites in this embodiment of the invention. Detailed Implementation
[0022] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.
[0023] This invention provides a method for correcting station system errors, specifically as follows: Figure 1 As shown, it includes: S1. Obtain the observation matrix A1 of the distance from the satellite to the SLR through the laser ranging station SLR, and obtain the observation matrix A2 of the distance from the satellite to the UXB and the satellite velocity through the deep space network tracking station UXB, and determine the weight of each observation in matrix A1 and matrix A2.
[0024] Outlier removal and correction were performed on the obtained observation matrix A1 and observation matrix A2, and all data of the same type were converted to the same unit of measurement.
[0025] S2. Establish the SLR observation equation based on matrix A1; use the parameters to be estimated generated in the UXB measurement as unknown parameters and establish the UXB observation equation with matrix A2; establish the satellite's orbital motion equation, and superimpose the SLR observation equation, UXB observation equation, and satellite's orbital motion equation according to their weights to construct a set of normal equations.
[0026] In an inertial coordinate system, the orbital motion equations of a satellite can be expressed as: (1) In the formula, and Let these represent the satellite's position, velocity, and acceleration vectors at time t, respectively. The parameter vectors in the perturbation model that need to be estimated in orbit determination are shown in equation (2). The second-order ordinary differential equation of equation (1) is shown in equation (2).
[0027] (2) Solving for orbital dynamic parameters This is the goal of precise orbit determination. However, in practice, the precise initial state of a satellite cannot be known in advance; only its reference state can be obtained. Furthermore, the satellite's dynamic model is not necessarily completely accurate or perfect, so the orbit obtained using numerical integration methods will have errors, and these errors accumulate as the integration time increases. Therefore, in exploration missions, it is necessary to utilize a large amount of observational data to correct the satellite's initial state. For example... Figure 2As shown, the Deep Space Network (DSN) tracking station UXB has a long tracking distance and is sensitive to positional changes along the line of sight, but it suffers from systematic bias. The Laser Ranging Station (SLR) has high ranging accuracy, but it is easily limited by weather conditions and has limited measurement data. This invention employs a combined orbit determination method, which calibrates and compensates for the systematic errors of the DSN station by fusing SLR and DSN orbit determination calculations, thereby improving orbit determination performance. Specifically, when constructing the normal equations, a constant parameter to be estimated is added for the DSN station for different station types (DSN station or SLR station), and this parameter is estimated simultaneously with the solar radiation pressure parameter and the initial orbital position and velocity parameter. Simultaneously, pseudo-equation constraints are added for stations that are not estimated, enhancing the stability of the solution. This achieves complementary advantages of different methods and improves orbit determination performance. The detailed implementation process is as follows:
[0028] Suppose we have two sets of observation equations A1 and A2: (3) (4) in, The satellite's state vector; For observation data The corresponding truth value; For the measured random noise, the random error models D corresponding to the random noise are as follows: (5) (6) In the formula, , The standard deviation of random noise, , Represents the weights of different data.
[0029] Let the correction value of the parameter vector to be estimated be... The normal equations obtained after least squares batch processing are as follows: (7) (8) in, The satellite's state vector; For observation data The corresponding truth value; For the random noise of the measurement, for The transpose of the matrix is used to calculate the observed values.
[0030] S3. Using the SLR observation equations and the satellite's orbital motion equations, calculate the theoretical prediction matrix V1 for the distance from the satellite to the SLR. Using the UXB observation equations and the satellite's orbital motion equations, calculate the theoretical prediction matrices V2 for the distance from the satellite to the UXB and the satellite's velocity. Calculate the SLR residual matrix from the difference between matrix A1 and matrix V1, and calculate the UXB residual matrix from the difference between matrix A2 and matrix V2. Based on the SLR and UXB residual matrices, use the least squares method to solve the estimated parameter corrections in the normal equation system, and substitute these corrections into the SLR and UXB observation equations for further calculation. Obtain the final estimated parameter corrections through multiple iterations.
[0031] When constructing the normal equations, it is necessary to obtain the current orbital position vector. right The partial derivatives can be solved using the orbital variational equations. The orbital motion equations are transformed from nonlinear to linear. Combining the partial derivatives, the normal equations fuse matrix A1 and matrix A2 with the orbital motion equations using linear gradient information, and then the least squares method is used to solve for the correction of the parameters to be estimated. Specifically:
[0032] For both sides of equation (2) about Taking partial derivatives yields the variational equation: (9) Its initial conditions are: (10) make , , , Then equation (1) can be written as a second-order linear ordinary differential equation: (11) in, , and It can be obtained during the calculation of perturbation acceleration. The solution of equation (11) is the state transition matrix. , representing the initial dynamic parameter vector Changes caused t The percentage change in satellite state at any given time. Construct the integral vector:
[0033] (12) Then the equations of motion (2) and the variational equations (9) can form the following system of second-order ordinary differential equations: (13) Equation (13) can be solved by the Adams-Cowell numerical integration method.
[0034] The corresponding covariance matrices in equation (8) are as follows: (14) (15) Let the parameters to be estimated and their covariance matrices in different observation equations be pseudo-observations, then we have: (16) The common estimated parameters are , It is the identity matrix. Given the observation matrices from different methods, the corresponding stochastic models are: (17) The normal equations obtained after superposition are: (18) After simplification, we get: (19) In the formula Here is the state transition matrix. The weight matrices are as follows: (20) ; (twenty one) The least squares method is used to solve the normal equations to obtain the correction amount of the parameter to be estimated; the correction amount of the parameter to be estimated is substituted into the SLR observation equation and UXB observation equation, and the normal equations are reconstructed with the orbital dynamics equation; Calculate the new residual matrix. If the residual matrix is greater than the set threshold, repeat the above steps. If the residual matrix is less than the set threshold, stop the iteration and obtain the final correction amount of the parameter to be estimated.
[0035] Precise orbit determination is achieved by combining the orbital motion equations, SLR observation equations, and UXB observation equations, based on the superposition of their normal equations. Figure 3 This paper presents a process for overlaying normal equations to achieve precise orbit determination using data from multiple ground-based methods. For different tracking measurement data, observation equations are established. First, each type of observation data is preprocessed to remove outlier data and arc segments with limited observation data, and various errors are corrected. Then, observation weights are designed based on the different weights of each type of observation data. Partial derivatives are calculated based on the orbital motion equations and variational equations to provide the normal equation coefficients for each measurement method, and these coefficients are then fused and overlaid. Finally, orbit iteration is improved and orbit determination residuals are edited to estimate the orbital parameters for each satellite.
[0036] S4. Use the final estimated parameter correction amount to calibrate and compensate the UXB measurement process.
[0037] The final correction amount of the parameter to be estimated is obtained through multiple iterations. The correction amount of the parameter to be estimated is used to calibrate and compensate the UXB measurement process of the Deep Space Network tracking station. Finally, the correctness and feasibility of the method are verified by the Monte Carlo method.
[0038] The following is the verification process of the above research method. Taking the superposition of the SLR and Deep Space Network method equations as an example, the SLR method equations include information such as the satellite's initial position and velocity, nine solar pressure coefficients, and station coordinates; while the Deep Space Network method equations include information such as the satellite's initial position and velocity, and nine solar pressure coefficients. Two sets of method equations are provided, with the common estimated parameters being... Let the first set of normal equations be:
[0039] (twenty two) The second set of normal equations is: (twenty three) By superimposing the two sets of normal equations, the new combined normal equations are obtained as follows: (twenty four) in The state transition matrix has common parameters. The state transition matrix has no common parameters. Given a unit state transition matrix, the correction values of the parameters to be estimated are obtained through multiple iterations. This refers to the coordinates of the tracking station. Next, simulation calculations will be used to verify the feasibility of this method in improving the accuracy of fusion orbit determination. The selected satellite parameters are shown in Table 1, and the dynamic model is shown in Table 2.
[0040] Table 1. Basic parameters of the satellite's nominal orbit (J2000 coordinate system) Table 2 Satellite Orbital Mechanics Model Regarding the selection of observation stations, the Deep Space Network chose three stations: Jiamusi, Kas, and Argentina. The distribution of the observation data acquired by these stations is as follows: Figure 4 .
[0041] Figure 5The SLR observation timeline is presented. For SLR stations, this chapter selects three domestic stations—Zhuhai, Wuhan, and Kashgar—for simulation calculations. The fusion orbit determination simulation parameter settings and orbit determination model selection are shown in Table 3. The dynamic model used for orbit determination is consistent with the model used to generate the Tianqin nominal orbit. Regarding the weighting ratio of observation data, the weights are set based on the product of the random error and sampling rate of each type of observation data. The weighting ratio between the Deep Space Network and the SLR method equations is 1:500. Thirty simulation experiments were conducted using the Monte Carlo method, and the average RMS values of the orbit determination position and velocity errors were calculated and statistically analyzed to evaluate the satellite's orbit determination accuracy.
[0042] Table 3. Parameter settings and orbit determination model selection for integrated orbit determination simulation Figure 6 This displays the 3D orbital position of a high-orbit satellite under the estimated errors of the Deep Space Network system. Figure 7 The data shows the 3D velocity accuracy of a high-orbit satellite under the estimation of Deep Space Network system errors. It can be seen that after estimating the Deep Space Network system errors, the satellite's orbital position accuracy within one orbital period (4 days) is better than 10m, a significant improvement compared to the 100m level without estimating system errors. The orbital velocity accuracy is better than 1mm / s, also a significant improvement compared to the 10mm / s level without estimating system errors. When the orbital arc length reaches 7 days, the orbital accuracy is on the order of 1m and 0.1mm / s. It is evident that most of the system errors are absorbed in the parameter estimation of the fused orbital determination, making the orbital accuracy more reliable.
[0043] Table 4. Accuracy of Deep Space Network Station System Difference Estimation under 7-Day Orbit Determination Arc Length Table 4 shows the difference between the estimated systematic error of the Deep Space Network stations under the 7-day orbit determination arc length and the systematic error added to each station during the simulation of the observation data. It can be seen that the estimation results of each station for a single satellite are quite similar, all within 2 cm. The estimation accuracy of station C is lower than that of the two Chinese stations A and B. This is because its location is far from the three Chinese SLR stations and its distance from the SLR observation range is large.
[0044] Table 5. Satellite orbit determination accuracy under 7-day orbit determination arc length (SLR + Deep Space Network) Table 5 shows the average orbit determination accuracy of the satellite under a 7-day orbit determination arc length. The results show that after estimating the systematic error, the average 3D RMS position accuracy of the satellite is 1.16m, an improvement of 95.3% compared to the result without estimating the systematic error, with the R-direction position accuracy better than 10cm; the 3D RMS velocity accuracy is 0.07mm / s, an improvement of 95.3% compared to the result without estimating the systematic error, with the T-direction velocity accuracy better than 0.05mm / s. Preliminary analysis indicates that high-precision SLR data can achieve calibration and compensation for systematic errors in deep space network orbit determination, thereby improving orbit determination performance.
[0045] To address the systematic errors in deep space network stations that are difficult to subtract themselves, an external method with high SLR observation accuracy and no systematic bias is used to establish a sparse representation model of measurement systematic errors in the fusion solution model, thereby achieving calibration and compensation of deep space network systematic bias.
[0046] Based on the same inventive concept, the present invention also provides a station system error correction system, comprising: The data acquisition module is used to acquire the observation matrix A1 of the distance between the satellite and the SLR through the laser ranging station SLR, and the observation matrix A2 of the distance between the satellite and the UXB and the satellite velocity through the Deep Space Network tracking station UXB, and to determine the weight of each observation in matrix A1 and matrix A2.
[0047] The normal equations construction module is used to establish the SLR observation equations based on matrix A1; to establish the UXB observation equations by taking the parameters to be estimated generated in the UXB measurement as unknown parameters and matrix A2; to establish the satellite's orbital motion equations; and to construct the normal equations by superimposing the SLR observation equations, UXB observation equations, and satellite's orbital motion equations according to their weights.
[0048] The normal equation solving module is used to calculate the theoretical prediction matrix V1 of the distance from the satellite to the SLR using the SLR observation equations and the satellite's orbital motion equations, and the theoretical prediction matrix V2 of the distance from the satellite to the UXB and the satellite's velocity using the UXB observation equations and the satellite's orbital motion equations. The residual matrix of the SLR is calculated from the difference between matrix A1 and matrix V1, and the residual matrix of the UXB is calculated from the difference between matrix A2 and matrix V2. Based on the residual matrices of the SLR and UXB, the correction values of the estimated parameters are solved using the least squares method, and these correction values are then substituted into the SLR and UXB observation equations for further calculation. The final correction values of the estimated parameters are obtained through multiple iterations.
[0049] The verification module is used to calibrate and compensate the UXB measurement process using the final estimated parameter correction.
[0050] This invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the aforementioned station system error correction method.
[0051] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described method for correcting station system errors.
[0052] Specific limitations regarding the calculation device for the station system error correction method can be found in the limitations of the station system error correction method described above, and will not be repeated here. Each module in the aforementioned station system error correction system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.
[0053] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the patent of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.
Claims
1. A method for correcting station system errors, characterized in that, The method includes: The observation matrix A1 of the distance from the satellite to the SLR is obtained through the laser ranging station SLR, and the observation matrix A2 of the distance from the satellite to the UXB and the satellite velocity is obtained through the Deep Space Network tracking station UXB. The weights of each observation in matrix A1 and matrix A2 are then determined. The SLR observation equation is established based on matrix A1; the parameters to be estimated generated in the UXB measurement are used as unknown parameters to establish the UXB observation equation with matrix A2; the orbital motion equation of the satellite is established, and the SLR observation equation, UXB observation equation and the orbital motion equation of the satellite are superimposed with weights to construct a set of normal equations. Using the SLR observation equations and the satellite's orbital motion equations, the theoretical prediction matrix V1 for the distance from the satellite to the SLR is calculated. Similarly, using the UXB observation equations and the satellite's orbital motion equations, the theoretical prediction matrices V2 for the distance from the satellite to the UXB and the satellite's velocity are calculated. The residual matrix of the SLR is calculated from the difference between matrix A1 and matrix V1, and the residual matrix of the UXB is calculated from the difference between matrix A2 and matrix V2. Based on the residual matrices of the SLR and UXB, the correction values for the estimated parameters are solved using the least squares method. These correction values are then substituted into the SLR and UXB observation equations for further calculation. Through multiple iterations, the final correction values for the estimated parameters are obtained, and these final correction values are used to calibrate and compensate for the UXB measurement process.
2. The station system error correction method according to claim 1, characterized in that, After obtaining the observation matrix A1 of the distance from the satellite to the SLR through the laser ranging station SLR, and the observation matrix A2 of the distance from the satellite to the UXB and the satellite velocity through the Deep Space Network tracking station UXB, the process also includes preprocessing of A1 and A2. Specifically, outlier removal and correction are performed on A1 and A2, and all data of the same type in the two matrices are processed into a unified unit of measurement.
3. The station system error correction method according to claim 1, characterized in that, The weights of each observation in matrix A1 and matrix A2 are determined by a weight ratio of 1:500 between A2 and A1. In A2, the weight ratio of the distance observation from the satellite to UXB obtained by the Deep Space Network tracking station UXB to the satellite velocity observation from the satellite to UXB obtained by the Deep Space Network tracking station UXB is 1:10000.
4. The station system error correction method according to claim 1, characterized in that, It also includes converting the orbital motion equations into linear variational equations using variational methods before constructing the normal equation set, calculating the partial derivatives of the satellite orbital parameters from the variational equations, and using the partial derivatives to perform linear gradient information fusion of matrices A1 and A2 with the orbital motion equations.
5. The station system error correction method according to claim 1, characterized in that, The residual matrices based on SLR and UXB are used to solve the correction amount of the estimated parameters in the normal equation system using the least squares method, and the correction amount of the estimated parameters is then substituted into the SLR observation equation and UXB observation equation for further calculation. The final correction amount for the parameter to be estimated is obtained through multiple iterations, including: The least squares method is used to solve the system of normal equations to obtain the correction amount of the parameters to be estimated; Substitute the estimated parameter corrections into the SLR observation equations and UXB observation equations, and reconstruct the normal equation set with the orbital motion equations. Calculate the new residual matrices of SLR and UXB. If either the residual matrix of SLR or UXB is greater than a set threshold, repeat the above steps. If both the residual matrices of SLR and UXB are less than the set threshold, stop the iteration and obtain the final parameter correction amount.
6. A station system error correction system, characterized in that, include: The data acquisition module is used to acquire the observation matrix A1 of the distance between the satellite and the SLR through the laser ranging station SLR, and the observation matrix A2 of the distance between the satellite and the UXB and the satellite velocity through the Deep Space Network tracking station UXB, and to determine the weight of each observation in matrix A1 and matrix A2. The normal equation system construction module is used to establish the SLR observation equations based on matrix A1; The parameters to be estimated generated in the UXB measurement are used as unknown parameters to establish the UXB observation equation with matrix A2; the orbital motion equation of the satellite is established, and the SLR observation equation, UXB observation equation and satellite orbital motion equation are superimposed with weights to construct a set of normal equations; The normal equation solving module is used to calculate the theoretical prediction matrix V1 of the distance from the satellite to the SLR using the SLR observation equation and the satellite's orbital motion equation, and to calculate the theoretical prediction matrix V2 of the distance from the satellite to the UXB and the satellite's velocity using the UXB observation equation and the satellite's orbital motion equation. The residual matrix of SLR is calculated from the difference between matrix A1 and matrix V1, and the residual matrix of UXB is calculated from the difference between matrix A2 and matrix V2. Based on the residual matrices of SLR and UXB, the correction amount of the estimated parameter is solved using the least squares method. The correction amount of the estimated parameter is then substituted into the SLR observation equation and the UXB observation equation for further calculation. The final correction amount of the estimated parameter is obtained through multiple iterations. The verification module is used to calibrate and compensate the UXB measurement process using the final estimated parameter correction.
7. A computer device, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method steps of any one of claims 1 to 5.
8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method steps as described in any one of claims 1 to 5.