Multi-source data fusion dynamics reference frame conversion method and related device

Abstract

The invention discloses a multi-source data fusion dynamic reference frame conversion method and a related device, and relates to the technical field of celestial body survey and space navigation, and the method comprises the steps: obtaining multi-source observation data, including pulsar timing data and VLBI absolute position measurement data; according to the multi-source observation data, a joint observation model and a high-order rotation model are established, the joint observation model is a mathematical model comprising a calendar error correction term and a coupling coefficient, and the high-order rotation model is a model which is established based on a quaternion parameterized rotation matrix and introduces a secondary correction term for nonlinear correction; on the basis of the combined observation model, a self-adaptive weighted least square algorithm is adopted, and rotation parameters are obtained through estimation; and based on the rotation parameter and the secondary correction term, performing error tracing and iterative optimization by adopting a Monte Carlo simulation method to obtain a dynamic reference framework conversion result. According to the invention, the conversion precision of the dynamic reference frame can be improved, and high-precision space reference conversion is realized.

Description

Technical Field

[0001] The present application relates to the field of astrometry and space navigation technology, and in particular to a dynamic reference frame conversion method and related devices for multi-source data fusion. Background Art

[0002] Current mainstream solar system planetary ephemerides (such as DE405 and DE436) describe the motion of celestial bodies in the Solar System Barycenter Celestial Reference System (BCRS) through numerical integration. However, they suffer from error coupling during the dynamical reference frame conversion process. This is because errors in the planetary ephemerides (such as model simplifications and observational data noise) are directly coupled to the calculated position of the Solar System Barycenter (SSB), leading to systematic deviations in pulsar timing analysis. Consequently, existing dynamical reference frame conversion methods suffer from low accuracy and are unable to achieve high-precision space datum conversion. Summary of the Invention

[0003] The purpose of this application is to provide a dynamic reference frame conversion method and related devices based on multi-source data fusion, which can improve the accuracy of dynamic reference frame conversion and achieve high-precision spatial reference conversion.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for converting a dynamic reference frame using multi-source data fusion, the method specifically comprising the following steps:

[0006] Acquire multi-source observation data; the multi-source observation data includes pulsar timing data and VLBI absolute position measurement data.

[0007] A joint observation model is established based on the multi-source observation data; the joint observation model refers to a mathematical model including an ephemeris error correction term and a coupling coefficient.

[0008] A high-order rotation model is constructed based on the multi-source observation data; the high-order rotation model refers to a model established based on a quaternion parameterized rotation matrix and introducing a quadratic correction term for nonlinear correction.

[0009] Based on the joint observation model, an adaptive weighted least squares algorithm is used to estimate the rotation parameters.

[0010] Based on the rotation parameters and the quadratic correction term, a Monte Carlo simulation method is used to perform error tracing and iterative optimization to obtain a dynamic reference frame conversion result.

[0011] Optionally, after the step of acquiring multi-source observation data, the multi-source data fusion dynamic reference frame conversion method further comprises the following steps:

[0012] Performing data cleaning on the multi-source observation data to obtain cleaned multi-source observation data.

[0013] The multi-source observation data after data cleaning is standardized to obtain standardized multi-source observation data; the standardized multi-source observation data is used as the multi-source observation data to establish the joint observation model.

[0014] Optionally, the expression of the joint observation model is:

[0015]

[0016] in, represents the observed difference vector after fusion, Λ is the rotation difference matrix, Δ eph is the ephemeris error correction term, α is the coupling coefficient, e i is the comprehensive noise term.

[0017] Optionally, after the step of establishing a joint observation model based on the multi-source observation data, the multi-source data fusion dynamic reference frame conversion method further includes the following steps:

[0018] Initialize the ephemeris error correction term and the coupling coefficient; wherein the coupling coefficient is dynamically calibrated using a Bayesian optimization method.

[0019] Optionally, constructing a high-order rotation model based on the multi-source observation data specifically includes the following steps:

[0020] According to the multi-source observation data, a quaternion parameterized rotation matrix is ​​established, which is expressed as:

[0021]

[0022] Where R represents the quaternion parameterized rotation matrix, Θ represents the three-dimensional rotation vector, θ x ,θ y ,θ y They are the components of the x-rotation axis, y-rotation axis, and z-rotation axis respectively.

[0023] Based on the quaternion parameterized rotation matrix, a quadratic correction term is introduced to perform nonlinear correction to compensate for the nonlinear effect in the ephemeris conversion, which is expressed as:

[0024]

[0025] in, is the direction vector before rotation, is the direction vector after rotation and correction, Λ (2) is the secondary correction term, is the tensor product symbol, which represents the vector outer product operation.

[0026] Optionally, based on the joint observation model, an adaptive weighted least squares algorithm is used to estimate the rotation parameters, which specifically includes the following steps:

[0027] Based on the joint observation model, the ephemeris error correction term and the uncertainty parameter value of each pulsar position are determined.

[0028] According to the ephemeris error correction term and the uncertainty parameter value of each pulsar position, a dynamic weight matrix is ​​established, which is expressed as:

[0029]

[0030] Among them, W i is the dynamic weight of the i-th observation, σ i represents the uncertainty parameter value of the i-th pulsar position, Δ eph is the ephemeris error correction term, ||Δ eph || 2 is the Euclidean norm of the ephemeris error correction term.

[0031] According to the dynamic weight matrix, the adaptive weighted least squares algorithm is used to estimate the rotation parameters, which are expressed as:

[0032]

[0033] in, is the rotation parameter, W represents the dynamic weight matrix, M represents the global design matrix, D represents the global observation difference vector, and T represents the transpose.

[0034] Optionally, based on the rotation parameters and the quadratic correction term, a Monte Carlo simulation method is used to perform error tracing and iterative optimization to obtain a dynamic reference frame conversion result, specifically comprising the following steps:

[0035] Based on the rotation parameters and the quadratic correction term, the Monte Carlo simulation method is used to trace the error source, separate the ephemeris error and the observation noise, and generate an error distribution map.

[0036] The rotation parameters and the quadratic correction term are iteratively optimized according to the error distribution graph to obtain iteratively optimized rotation parameters and quadratic correction term.

[0037] The conversion accuracy is verified based on the iteratively optimized rotation parameters and quadratic correction terms, and a sub-milliarcsecond level dynamic reference frame conversion result is obtained.

[0038] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the dynamic reference frame conversion method for multi-source data fusion.

[0039] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the dynamic reference frame conversion method for multi-source data fusion.

[0040] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the dynamic reference frame conversion method for multi-source data fusion.

[0041] According to the specific embodiments provided in this application, this application has the following technical effects:

[0042] The present application provides a method and related device for dynamic reference frame conversion based on multi-source data fusion. By using multi-source observation data including fused pulsar timing data and VLBI absolute position measurement data, the coordination of multi-source observation data is achieved, breaking through the limitations of single-source data, thereby improving the accuracy of dynamic reference frame conversion and solving the problem of single-source data dependence in existing dynamic reference frame conversion methods. By constructing a high-order rotation model, combining the quaternion parameterized rotation matrix with the quadratic correction term, and introducing the quadratic correction term on the basis of the quaternion parameterized rotation matrix for nonlinear correction, high-order nonlinear compensation can be achieved, improving the accuracy of dynamic reference frame conversion, solving the linear approximation limitations and accuracy attenuation problems in large-version calendar conversion. By using the Monte Carlo simulation method to trace the error and iteratively optimize the rotation parameters and the quadratic correction term, the independent correction of the calendar error and the rotation parameter is achieved, which can solve the error coupling problem in the existing dynamic reference frame conversion method. In summary, through the above-mentioned various technical means, the accuracy of dynamic reference frame conversion can be effectively improved and high-precision space reference conversion can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 This is a diagram of the application environment of a dynamic reference frame conversion method for multi-source data fusion in one embodiment of the present application.

[0045] Figure 2 A flowchart of a dynamic reference frame conversion method for multi-source data fusion provided in one embodiment of the present application.

[0046] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0048] At present, the existing dynamical reference frame conversion methods have the following limitations: (1) Error coupling problem: Planetary ephemeris errors (such as model simplification and observational data noise) are directly coupled to the calculation of the solar system center of mass position, resulting in systematic deviations in pulsar timing analysis, and the existing dynamical reference frame conversion methods do not effectively separate the ephemeris errors from the rotation parameters of the reference frame. (2) Single-source data dependence: The existing dynamical reference frame conversion methods only rely on pulsar timing data and do not integrate independent observation techniques such as VLBI (very long baseline interferometry), thereby limiting the robustness of the dynamical reference frame conversion model. (3) Linear approximation limitations: The traditional three-axis rotation angle model uses a first-order linear approximation, ignoring high-order rotation terms and nonlinear effects, and the accuracy decreases significantly when the span of the ephemeris version is large. (4) Low parameter estimation efficiency: The least squares method is easily interfered by observation noise in the multi-pulsar scenario, and the adaptive weight distribution mechanism is not introduced, resulting in insufficient parameter estimation stability.

[0049] Based on this, the present application aims to provide a dynamic reference frame conversion method based on multi-source data fusion, which realizes high-precision space reference conversion by combining pulsar timing data, VLBI position observations and ephemeris error correction through multi-source data fusion.

[0050] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0051] The dynamic reference frame conversion method of multi-source data fusion provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send multi-source observation data to the server 104. After the server 104 receives the multi-source observation data, the server 104 obtains the multi-source observation data, including pulsar timing data and VLBI absolute position measurement data; based on the multi-source observation data, a joint observation model and a high-order rotation model are established. The joint observation model refers to a mathematical model including an ephemeris error correction term and a coupling coefficient. The high-order rotation model refers to a model established based on a quaternion parameterized rotation matrix and introducing a quadratic correction term for nonlinear correction; based on the joint observation model, an adaptive weighted least squares algorithm is used to estimate the rotation parameters; based on the rotation parameters and the quadratic correction term, a Monte Carlo simulation method is used to perform error tracing and iterative optimization to obtain the dynamic reference frame conversion result. The server 104 can feedback the obtained dynamic reference frame conversion result to the terminal 102. In addition, in some embodiments, the dynamic reference frame conversion method of multi-source data fusion can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly perform dynamic reference frame conversion processing on the multi-source observation data, or the server 104 can obtain the multi-source observation data from the data storage system and perform dynamic reference frame conversion processing on the multi-source observation data.

[0052] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or a cloud server.

[0053] In an exemplary embodiment, Figure 2 As shown, a method for converting a dynamic reference frame of multi-source data fusion is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 Taking the server 104 in the example as an example, the following steps are specifically included:

[0054] Step S1: Acquire multi-source observation data, wherein the multi-source observation data includes pulsar timing data and VLBI absolute position measurement data.

[0055] Step S2: Establish a joint observation model based on the multi-source observation data, wherein the joint observation model refers to a mathematical model including an ephemeris error correction term and a coupling coefficient.

[0056] Step S3: constructing a high-order rotation model based on the multi-source observation data, wherein the high-order rotation model refers to a model established based on a quaternion parameterized rotation matrix and introducing a quadratic correction term for nonlinear correction.

[0057] Step S4: Based on the joint observation model, an adaptive weighted least squares algorithm is used to estimate the rotation parameters.

[0058] Step S5: Based on the rotation parameters and the quadratic correction term, a Monte Carlo simulation method is used to perform error tracing and iterative optimization to obtain a dynamic reference frame conversion result. The dynamic reference frame conversion result is a converted dynamic reference frame obtained based on multi-source observation data.

[0059] By implementing the above-mentioned steps S1 to S5, and using multi-source observation data including fused pulsar timing data and VLBI absolute position measurement data, the coordination of multi-source observation data is achieved, breaking through the limitations of single-source data, thereby improving the accuracy of the dynamic reference frame conversion and solving the problem of single-source data dependence in the existing dynamic reference frame conversion method. By constructing a high-order rotation model, combining the quaternion parameterized rotation matrix with the quadratic correction term, and introducing the quadratic correction term on the basis of the quaternion parameterized rotation matrix for nonlinear correction, high-order nonlinear compensation can be achieved, and the accuracy of the dynamic reference frame conversion can be improved, which can solve the linear approximation limitations and accuracy attenuation problems in the conversion of large-version calendars. By using the Monte Carlo simulation method to trace the error source and iteratively optimize the rotation parameters and the quadratic correction term, the independent correction of the calendar error and the rotation parameter can be achieved, which can solve the error coupling problem in the existing dynamic reference frame conversion method. In summary, through the above-mentioned various technical means, the accuracy of the dynamic reference frame conversion can be effectively improved and high-precision space reference conversion can be achieved.

[0060] In this embodiment, after the step of acquiring multi-source observation data in step S1, the multi-source data fusion dynamic reference frame conversion method further includes a data preprocessing step, specifically including the following steps:

[0061] (1) Performing data cleaning on the multi-source observation data to obtain cleaned multi-source observation data.

[0062] (2) Standardizing the cleaned multi-source observation data to obtain standardized multi-source observation data. The standardized multi-source observation data is used as the multi-source observation data for establishing the joint observation model in step S2.

[0063] In this embodiment, after the step of establishing a joint observation model based on the multi-source observation data in step S2, the multi-source data fusion dynamic reference frame conversion method further includes the following steps:

[0064] Initialize the ephemeris error correction term and the coupling coefficient, wherein the coupling coefficient is dynamically calibrated using a Bayesian optimization method.

[0065] In this embodiment, step S3 constructs a high-order rotation model based on the multi-source observation data, specifically including the following steps:

[0066] Step S31: Establish a quaternion parameterized rotation matrix based on the multi-source observation data.

[0067] Step S32: Based on the quaternion parameterized rotation matrix, a quadratic correction term is introduced to perform nonlinear correction to compensate for the nonlinear effect in the ephemeris conversion.

[0068] In this embodiment, step S4 estimates the rotation parameters based on the joint observation model using an adaptive weighted least squares algorithm, which specifically includes the following steps:

[0069] Step S41: Determine the ephemeris error correction term and the uncertainty parameter value of each pulsar position according to the joint observation model.

[0070] Step S42: establishing a dynamic weight matrix according to the ephemeris error correction term and the uncertainty parameter value of each pulsar position.

[0071] Step S43: According to the dynamic weight matrix, an adaptive weighted least squares algorithm is used to estimate the rotation parameters.

[0072] In this embodiment, step S5 uses the Monte Carlo simulation method to perform error tracing and iterative optimization based on the rotation parameters and the quadratic correction term to obtain a dynamic reference frame conversion result, which specifically includes the following steps:

[0073] Step S51: Based on the rotation parameters and the quadratic correction term, a Monte Carlo simulation method is used to trace the error source, separate the ephemeris error and the observation noise, and generate an error distribution map.

[0074] Step S52: iteratively optimize the rotation parameters and the quadratic correction term according to the error distribution graph to obtain iteratively optimized rotation parameters and quadratic correction term.

[0075] Step S53 : verifying the conversion accuracy based on the iteratively optimized rotation parameters and the quadratic correction term to obtain a sub-milliarcsecond level dynamic reference frame conversion result.

[0076] In order to make the technical solution of this embodiment clearer, the specific implementation process of this embodiment is described in detail below in the form of examples.

[0077] This example proposes a dynamic reference frame conversion method based on multi-source data fusion, aiming to achieve high-precision spatial reference conversion by combining pulsar timing data, VLBI absolute position measurement data, and ephemeris error correction. Specifically, the method includes the following steps:

[0078] (1) Obtain multi-source observation data and perform preprocessing.

[0079] 1) Obtain multi-source observation data.

[0080] In this embodiment, the multi-source observation data includes pulsar timing data and VLBI absolute position measurement data. The pulsar timing data is based on the SSB reference frame, sourced from, for example, the public dataset of the International Pulsar Timing Array (IPTA). The VLBI absolute position measurement data is based on the International Celestial Reference Frame (ICRF).

[0081] 2) Preprocess multi-source observation data.

[0082] In this embodiment, preprocessing includes data cleaning and standardization. Data cleaning is used to remove outliers from multi-source observation data, such as pulsars with abnormal TOA (Time of Arrival) timing residuals. Standardization is used to unify time bases, facilitating the conversion of time systems such as TT (geodynamic time) and TDB (solar system barycentric dynamic time) into a unified time coordinate.

[0083] 3) Based on multi-source observation data, extract the standardized direction vector of each pulsar and the uncertainty parameter value of its position measurement.

[0084] (2) Construct a joint observation model.

[0085] 1) Based on multi-source observation data, pulsar timing data and VLBI absolute position measurement data are integrated to construct a joint observation model. The joint observation model includes the ephemeris error correction term Δ eph The mathematical model of the coupling coefficient α is essentially a joint observation equation, which is expressed as follows:

[0086]

[0087] in, represents the observed difference vector after fusion, integrating timing data and VLBI data. Λ is the rotation difference matrix, Δ eph is the ephemeris error correction term, α is the coupling coefficient, and is dynamically calibrated through Bayesian optimization. i is the comprehensive noise term, including the uncertainty parameter values ​​of timing and VLBI observations.

[0088] 2) Initialize the ephemeris error correction term Δ eph and the coupling coefficient α, which is preliminarily calibrated by Bayesian optimization.

[0089] 3) Establish the error covariance matrix Σ to quantify the joint impact of timing noise and VLBI measurement error.

[0090] (3) Construct a high-order rotation model and nonlinear correction.

[0091] 1) The quaternion parameterized rotation matrix R is used to replace the traditional Euler angle linear approximation model to avoid singularities and support large angle rotations. The quaternion parameterized rotation matrix R is expressed as:

[0092]

[0093] Where R represents the quaternion parameterized rotation matrix, Θ represents the three-dimensional rotation vector, θ x ,θ y ,θ y They are the components of the x-rotation axis, y-rotation axis, and z-rotation axis respectively.

[0094] 2) Introducing the quadratic correction term Λ (2) , to compensate for the nonlinear effects (such as curvature error) in the ephemeris conversion, expressed as:

[0095]

[0096] in, is the direction vector before rotation, is the direction vector after rotation and correction, R is the quaternion parameterized rotation matrix, Λ (2) is the secondary correction term, is the tensor product symbol, which represents the vector outer product operation.

[0097] 3) Dynamically adjust the secondary correction term Λ according to the difference in calendar versions (such as DE405 and DE436) (2) .

[0098] (4) Adaptive weighted least squares algorithm is used to estimate the rotation parameters.

[0099] 1) Design a dynamic weight matrix to dynamically assign weights based on the pulsar position uncertainty parameter value and the Euclidean norm of the ephemeris error correction term.

[0100] In this embodiment, the pulsar position uncertainty parameter value σ i and the ephemeris error correction term Δ eph , design the dynamic weight matrix W, expressed as:

[0101]

[0102] Among them, W i is the dynamic weight of the i-th observation, used for the estimation of the adaptive weighted least squares algorithm; σ i represents the uncertainty parameter value of the i-th pulsar position, which comes from pulsar timing data or VLBI absolute position measurement data; Δ eph is the ephemeris error correction term, ||Δ eph || 2 is the Euclidean norm of the calendar error correction term, which represents the impact strength of calendar version differences.

[0103] 2) Construct the global design matrix M and observation difference vector D.

[0104] In this embodiment, based on the global design matrix M and the observation difference vector D, an adaptive weighted least squares algorithm is used to estimate the rotation parameters Expressed as:

[0105]

[0106] in, is the rotation parameter, W is the diagonal weight matrix, W i are diagonal elements, M represents the global design matrix, which in this embodiment refers to the global design matrix after the fusion of pulsar timing data and VLBI absolute position measurement data, D represents the global observation difference vector, which in this embodiment refers to the global observation difference vector after the fusion of pulsar timing data and VLBI absolute position measurement data, and T represents transpose.

[0107] (5) Error tracing and iterative optimization.

[0108] 1) Monte Carlo simulation is used to separate ephemeris errors from observation noise and generate error distribution diagrams as error analysis results.

[0109] 2) Based on the error analysis results (i.e. error distribution diagram), iteratively correct the rotation parameters and the quadratic correction term Λ (2) , optimizing the reference frame alignment accuracy through iterative feedback.

[0110] 3) Verify the conversion accuracy. By comparing the converted pulsar positions with independent observational data (such as the Gaia catalog), we ensure sub-milliarcsecond consistency and ultimately obtain a sub-milliarcsecond dynamical reference frame conversion result.

[0111] This embodiment fuses pulsar timing data and VLBI absolute position measurement data to form multi-source observation data, achieving multi-source observation data collaboration and overcoming the limitations of single-source data. This can improve the accuracy of the dynamic reference frame conversion and significantly enhance the anti-interference ability of the conversion model, resolving the single-source data dependency problem of existing dynamic reference frame conversion methods. By combining a quaternion parameterized rotation matrix with a quadratic correction term, high-order nonlinear compensation is achieved, addressing the linear approximation limitations and precision degradation problems associated with large-version ephemeris conversion. Dynamic weight allocation is achieved through an adaptive weighted least squares algorithm and a dynamic weight matrix, reducing noise sensitivity and improving parameter estimation robustness, addressing the low parameter estimation efficiency problem of existing dynamic reference frame conversion methods. Through Bayesian optimization combined with Monte Carlo simulation for error decoupling, independent correction of ephemeris errors and rotation parameters is achieved, addressing the error coupling problem of existing dynamic reference frame conversion methods. Overall, high-precision maintenance of the dynamic reference frame and precise conversion between different reference systems are achieved. This not only improves the accuracy of pulsar timing analysis but also provides a more accurate dynamic benchmark for fields such as astronomical ephemeris compilation and deep space exploration. It will significantly promote technological progress in astronomical measurement, space navigation and other related fields, and has important scientific value and application prospects.

[0112] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store multi-source observation data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a dynamic reference frame conversion method for multi-source data fusion is realized.

[0113] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0114] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0115] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0116] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0117] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0118] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A dynamic reference frame conversion method based on multi-source data fusion, characterized in that: The multi-source data fusion dynamic reference frame conversion method includes: Acquiring multi-source observation data; the multi-source observation data includes pulsar timing data and VLBI absolute position measurement data; Establishing a joint observation model based on the multi-source observation data; the joint observation model is a mathematical model including an ephemeris error correction term and a coupling coefficient; Constructing a high-order rotation model based on the multi-source observation data; the high-order rotation model refers to a model established based on a quaternion parameterized rotation matrix and introducing a quadratic correction term for nonlinear correction; Based on the joint observation model, an adaptive weighted least squares algorithm is used to estimate the rotation parameters; Based on the rotation parameters and the quadratic correction term, a Monte Carlo simulation method is used to perform error tracing and iterative optimization to obtain a dynamic reference frame conversion result.

2. The method for dynamic reference frame conversion based on multi-source data fusion according to claim 1, characterized in that: After the step of acquiring multi-source observation data, the multi-source data fusion dynamic reference frame conversion method further includes: Performing data cleaning on the multi-source observation data to obtain cleaned multi-source observation data; The multi-source observation data after data cleaning is standardized to obtain standardized multi-source observation data; the standardized multi-source observation data is used as the multi-source observation data to establish the joint observation model.

3. The method for dynamic reference frame conversion based on multi-source data fusion according to claim 1, characterized in that: The expression of the joint observation model is: in, represents the observed difference vector after fusion, Λ is the rotation difference matrix, Δ eph is the ephemeris error correction term, α is the coupling coefficient, e i is the comprehensive noise term.

4. The method for dynamic reference frame conversion based on multi-source data fusion according to claim 1, characterized in that: After the step of establishing a joint observation model based on the multi-source observation data, the multi-source data fusion dynamic reference frame conversion method further includes: Initialize the ephemeris error correction term and the coupling coefficient; wherein the coupling coefficient is dynamically calibrated using a Bayesian optimization method.

5. The method for dynamic reference frame conversion based on multi-source data fusion according to claim 1, characterized in that: Based on the multi-source observation data, a high-order rotation model is constructed, specifically including: According to the multi-source observation data, a quaternion parameterized rotation matrix is ​​established, which is expressed as: Where R represents the quaternion parameterized rotation matrix, Θ represents the three-dimensional rotation vector, θ x ,θ y ,θ y are the components of the x-rotation axis, y-rotation axis, and z-rotation axis respectively; Based on the quaternion parameterized rotation matrix, a quadratic correction term is introduced to perform nonlinear correction to compensate for the nonlinear effect in the ephemeris conversion, which is expressed as: in, is the direction vector before rotation, is the direction vector after rotation and correction, Λ (2) is the quadratic correction term, is the tensor product symbol, which represents the vector outer product operation.

6. The method for dynamic reference frame conversion based on multi-source data fusion according to claim 1, characterized in that: Based on the joint observation model, an adaptive weighted least squares algorithm is used to estimate the rotation parameters, specifically including: Determining ephemeris error correction terms and uncertainty parameter values ​​of each pulsar position based on the joint observation model; According to the ephemeris error correction term and the uncertainty parameter value of each pulsar position, a dynamic weight matrix is ​​established, which is expressed as: Among them, W i is the dynamic weight of the i-th observation, σ i represents the uncertainty parameter value of the i-th pulsar position, Δ eph is the ephemeris error correction term, ||Δ eph || 2 is the Euclidean norm of the ephemeris error correction term; According to the dynamic weight matrix, the adaptive weighted least squares algorithm is used to estimate the rotation parameters, which are expressed as: in, is the rotation parameter, W represents the dynamic weight matrix, M represents the global design matrix, D represents the global observation difference vector, and T represents the transpose.

7. The method for dynamic reference frame conversion based on multi-source data fusion according to claim 1, characterized in that: Based on the rotation parameters and the quadratic correction term, the Monte Carlo simulation method is used to perform error tracing and iterative optimization to obtain the dynamic reference frame conversion result, which specifically includes: Based on the rotation parameters and the quadratic correction term, a Monte Carlo simulation method is used to trace the error source, separate the ephemeris error and the observation noise, and generate an error distribution map; Iteratively optimizing the rotation parameters and the quadratic correction term according to the error distribution graph to obtain iteratively optimized rotation parameters and quadratic correction term; The conversion accuracy is verified based on the iteratively optimized rotation parameters and quadratic correction terms, and a sub-milliarcsecond level dynamic reference frame conversion result is obtained.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the dynamic reference frame conversion method for multi-source data fusion according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the dynamic reference frame conversion method for multi-source data fusion according to any one of claims 1 to 7 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the dynamic reference frame conversion method for multi-source data fusion according to any one of claims 1 to 7 is implemented.

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