Method, device, equipment and medium for determining the speed of a station of a navigation satellite system

Abstract

The application provides a method and device for determining the speed of a station of a navigation satellite system, equipment and a medium, which can be applied to the technical field of satellite navigation and positioning. The method comprises the following steps: determining the spatial correlation between each two stations of the navigation satellite system according to the spherical distance between each two stations; constructing a space-time noise covariance matrix between the stations based on the spatial correlation and the coordinate sequence of each station; constructing a to-be-solved equation corresponding to a target station based on the first coefficient and the first unknown parameter included in each of a plurality of preset modelable deterministic components; solving the to-be-solved equation according to the space-time noise covariance matrix to obtain the result of the noise term and the result of the parameter vector, so as to determine the speed of the target station based on the result of the parameter vector.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation and positioning technology, and more specifically to a method, apparatus, equipment, and medium for determining the velocity of a station in a navigation satellite system. Background Technology

[0002] In space geodesy technologies such as Global Navigation Satellite System (GNSS), determining the velocity field of a station is fundamental to studying geophysical processes at different spatiotemporal scales on the Earth's surface. The velocity field is typically calculated from the station's coordinate data at different times. However, the coordinate data from different times contains a significant amount of noise, which greatly affects the accuracy and reliability of determining the station's velocity.

[0003] In implementing this invention, it was found that related techniques typically analyze coordinate data from a single station at different times to determine the temporal correlation between noises in the coordinate data, and then denoise all the coordinate data accordingly. However, since noise usually also exhibits spatial correlation, using the above-mentioned denoising methods still makes it difficult to ensure the accuracy and reliability of the coordinate data. Summary of the Invention

[0004] In view of the above problems, the present invention provides a method, apparatus, equipment and medium for determining the velocity of a station in a navigation satellite system.

[0005] According to a first aspect of the present invention, a method for determining the velocity of a station in a navigation satellite system is provided, comprising: determining the spatial correlation between multiple stations based on the spherical distances between each other in the navigation satellite system, wherein the multiple stations include a target station; constructing a spatiotemporal noise covariance matrix between the multiple stations based on the multiple spatial correlations and the noise types included in each of the multiple stations; constructing an equation to be solved corresponding to the target station based on the first coefficients and first unknown parameters included in each of multiple preset modelable deterministic components, wherein the equation to be solved includes an observation vector, a coefficient matrix, a parameter vector, and a noise term, wherein the coefficient matrix includes first coefficients, the parameter vector includes first unknown parameters, the observation vector is determined according to the coordinate sequence of the target station, and the coordinate sequence of each station includes the position coordinates of the station at multiple time points; and solving the equation to be solved based on the spatiotemporal noise covariance matrix to obtain the result of the noise term and the result of the parameter vector, so as to determine the velocity of the target station based on the result of the parameter vector.

[0006] According to an embodiment of the present invention, determining the spatial correlation between multiple stations based on the spherical distances between each other in a navigation satellite system includes: substituting the spherical distances into a preset exponential model to obtain the spatial correlation between two stations corresponding to the spherical distances; the preset exponential model is determined by: determining multiple sample residual sequences based on the sample coordinate sequences of each of the multiple stations and the sample deterministic components corresponding to each of the multiple sample coordinate sequences; determining the sample correlation coefficient based on the covariance between the multiple sample residual sequences and the variance of each of the multiple sample residual sequences; and fitting the parameters to be determined in the sample exponential model based on the sample correlation coefficient, the spherical distances, and the sample exponential model to obtain the preset exponential model.

[0007] According to an embodiment of the present invention, the sample deterministic components are determined in the following manner: a noise covariance matrix is ​​constructed among multiple stations based on their respective sample coordinate sequences; the equation to be solved is solved according to the noise covariance matrix to obtain the sample results of the noise term and the sample results of the parameter vector; and the sample deterministic components are determined based on the sample results and the coefficient matrix.

[0008] According to an embodiment of the present invention, constructing a spatiotemporal noise covariance matrix among multiple stations based on multiple spatial correlations and the noise types included in each of the multiple stations includes: determining, from the noise types included in each of the multiple stations, the second coefficients and second unknown parameters included in each of the multiple preset modelable noise components; for each pair of stations among the multiple stations, determining the spatial noise correlation between the two stations based on the second coefficients and second unknown parameters of the two stations and the spatial correlation between the two stations; for each station among the multiple stations, determining the temporal noise correlation of the station based on the second coefficients and second unknown parameters of the station; and constructing a spatiotemporal noise covariance matrix based on the multiple spatial noise correlations and the multiple temporal noise correlations.

[0009] According to an embodiment of the present invention, determining the spatial noise correlation between two stations based on their respective second coefficients and second unknown parameters, and the spatial correlation between the two stations, includes: for the same noise component, determining the subspace noise correlation of the noise component based on the spatial correlation, the second coefficient of the noise component, and multiple second unknown parameters of the noise component; and summing the subspace noise correlations of the multiple noise components to obtain the spatial noise correlation.

[0010] According to an embodiment of the present invention, the spatiotemporal noise covariance matrix includes the amplitude to be solved corresponding to the noise type of the noise components present in the station; the equation to be solved is solved based on the spatiotemporal noise covariance matrix to obtain the result of the noise term and the result of the parameter vector, including: constructing a maximum likelihood estimation solution objective for the equation to be solved based on the spatiotemporal noise covariance matrix and its determinant; determining the assumed value of the amplitude to be solved based on the historical value of the noise type, and determining the determinant value and inverse matrix of the spatiotemporal noise covariance matrix; repeating the following operations until the solution objective converges: instantiating the solution objective based on the determinant value and inverse matrix, and determining the gradient direction; adjusting the assumed value based on the gradient direction; and, if the solution objective converges, using the assumed value as the true value of the amplitude to be solved, and determining the result of the noise term and the result of the parameter vector.

[0011] According to an embodiment of the present invention, based on the first coefficients and first unknown parameters of each of a plurality of preset modelable deterministic components, an equation to be solved corresponding to a target station is constructed, including: analyzing the plurality of deterministic components to obtain the first coefficients and first unknown parameters of each of the plurality of deterministic components; determining a coefficient matrix based on the plurality of first coefficients and a parameter vector based on the plurality of first unknown parameters; determining the observation vector of the equation to be solved based on the coordinate sequence of the target station; and constructing the equation to be solved based on the observation vector, the coefficient matrix, the parameter vector, and the noise term.

[0012] A second aspect of the present invention provides a velocity determination device for a navigation satellite system station, comprising: a correlation calculation module for determining the spatial correlation between multiple stations based on the spherical distances between each other in the navigation satellite system, wherein the multiple stations include a target station; a matrix determination module for constructing a spatiotemporal noise covariance matrix between the multiple stations based on the multiple spatial correlations and the noise types included in each of the multiple stations; an equation construction module for constructing an equation to be solved corresponding to the target station based on the first coefficients and first unknown parameters included in each of the multiple preset modelable deterministic components, wherein the equation to be solved includes an observation vector, a coefficient matrix, a parameter vector, and a noise term, wherein the coefficient matrix includes first coefficients, the parameter vector includes first unknown parameters, the observation vector is determined according to the coordinate sequence of the target station, and the coordinate sequence of each station includes the position coordinates of the station at multiple time points; and a result fitting module for solving the equation to be solved based on the spatiotemporal noise covariance matrix to obtain the result of the noise term and the result of the parameter vector, so as to determine the velocity of the target station based on the result of the parameter vector.

[0013] A third aspect of the present invention provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0014] A fourth aspect of the present invention also provides a computer-readable storage medium having a computer program or instructions stored thereon, wherein the computer program or instructions, when executed by a processor, implement the steps of the above-described method.

[0015] A fifth aspect of the present invention also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0016] According to embodiments of the present invention, spatial correlation is constructed by introducing spherical distances between stations, and a spatiotemporal noise covariance matrix is ​​constructed by combining preset noise types, thereby achieving accurate modeling of spatiotemporal correlation noise in the station coordinate sequence of a navigation satellite system. Since the spatiotemporal noise covariance matrix is ​​constructed by combining spatial correlation with temporal correlation, it is possible to comprehensively fit the observation equations by combining both temporal and spatial correlations, significantly reducing the interference of noise on parameter estimation and improving the accuracy and stability of target station velocity calculation. Attached Figure Description

[0017] The above-mentioned contents, as well as other objects, features and advantages of the present invention, will become clearer from the following description of embodiments of the present invention with reference to the accompanying drawings.

[0018] Figure 1 The illustration schematically depicts an application scenario of the velocity determination method, apparatus, equipment, and medium for a navigation satellite system according to an embodiment of the present invention.

[0019] Figure 2 A flowchart illustrating a method for determining the velocity of a station in a navigation satellite system according to an embodiment of the present invention is shown.

[0020] Figure 3 A data flow diagram illustrating a method for determining the velocity of a station in a navigation satellite system according to an embodiment of the present invention is shown.

[0021] Figure 4 A schematic block diagram of a velocity determination device for a navigation satellite system according to an embodiment of the present invention is shown.

[0022] Figure 5 A block diagram of an electronic device suitable for implementing a method for determining the velocity of a station in a navigation satellite system, according to an embodiment of the present invention, is shown schematically. Detailed Implementation

[0023] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the invention. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the invention for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0024] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0025] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0026] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0027] In the technical solution of this invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, invention, and application of related data all comply with relevant laws, regulations, and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse.

[0028] In scenarios involving automated decision-making using personal information, the methods, devices, and systems provided in this invention offer users corresponding entry points for choosing to agree to or reject the automated decision-making results. If the user chooses to reject, the process proceeds to the expert decision-making stage. Here, "automated decision-making" refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests, or economic, health, and credit status through computer programs, and then making a decision. Here, "expert decision-making" refers to the activity of making decisions by personnel who specialize in a particular field, possess specialized experience, knowledge, and skills, and have reached a certain level of professional expertise.

[0029] An embodiment of the present invention provides a method for determining the velocity of a station in a navigation satellite system, comprising: determining the spatial correlation between multiple stations based on the spherical distances between each other in the navigation satellite system, wherein the multiple stations include a target station; constructing a spatiotemporal noise covariance matrix between the multiple stations based on the multiple spatial correlations and the noise types included in each of the multiple stations; constructing an equation to be solved corresponding to the target station based on the first coefficients and first unknown parameters included in each of the multiple preset modelable deterministic components, wherein the equation to be solved includes an observation vector, a coefficient matrix, a parameter vector, and a noise term, the coefficient matrix includes the first coefficients, the parameter vector includes the first unknown parameter, the observation vector is determined according to the coordinate sequence of the target station, and the coordinate sequence of each station includes the position coordinates of the station at multiple time points; solving the equation to be solved based on the spatiotemporal noise covariance matrix to obtain the result of the noise term and the result of the parameter vector, so as to determine the velocity of the target station based on the result of the parameter vector.

[0030] Figure 1 The illustration schematically depicts an application scenario of the velocity determination method, apparatus, equipment, and medium for a navigation satellite system according to an embodiment of the present invention.

[0031] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0032] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as applications that communicate with stations of navigation satellite systems to obtain station coordinate sequences, etc. (for example only).

[0033] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0034] Server 105 could be a server that provides various services, such as processing station coordinate sequences to determine station speed (for example only).

[0035] It should be noted that the station velocity determination method for the navigation satellite system provided in this embodiment of the invention can generally be executed by server 105. Correspondingly, the station velocity determination device for the navigation satellite system provided in this embodiment of the invention can generally be located in server 105. The station velocity determination method for the navigation satellite system provided in this embodiment of the invention can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the station velocity determination device for the navigation satellite system provided in this embodiment of the invention can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0036] It should be understood that Figure 1 The number of first terminal devices, second terminal devices, third terminal devices, networks, and servers shown in the diagram is merely illustrative. Depending on implementation needs, any number of first terminal devices, second terminal devices, third terminal devices, networks, and servers can be included.

[0037] The following will be based on Figure 1 The described scene, through Figures 2-3 The method for determining the velocity of a station in a navigation satellite system according to an embodiment of the present invention will be described in detail.

[0038] Figure 2 A flowchart illustrating a method for determining the velocity of a station in a navigation satellite system according to an embodiment of the present invention is shown.

[0039] like Figure 2As shown, the velocity determination method for the navigation satellite system station in this embodiment includes operations S210 to S240.

[0040] In operation S210, the spatial correlation between multiple stations is determined based on the spherical distances between each other in the navigation satellite system.

[0041] In operation S220, based on multiple spatial correlations and the noise types included by each of the multiple stations, a spatiotemporal noise covariance matrix is ​​constructed among the multiple stations.

[0042] In operation S230, based on the first coefficients and first unknown parameters of each of the multiple preset modelable deterministic components, an equation to be solved corresponding to the target station is constructed.

[0043] In operation S240, the equation to be solved is obtained based on the spatiotemporal noise covariance matrix, and the results of the noise term and the parameter vector are obtained, so as to determine the velocity of the target station based on the results of the parameter vector.

[0044] A navigation satellite system comprises multiple stations, including a target station. The target station's velocity is calculated using the navigation satellite system's station velocity determination method. The spherical distance between two stations represents the shortest distance measured along the Earth's surface. The spatial correlation between two stations indicates their spatial similarity; based on this correlation, the similarity of noise levels caused by space environment interference can be determined.

[0045] Based on the pairwise spatial correlation between multiple monitoring stations, the spatial correlation of noise at multiple stations can be determined. Furthermore, based on the noise types included at each station, the temporal correlation of noise at each station can be determined. Therefore, based on the spatial correlation and temporal correlation of noise at multiple stations, a spatiotemporal noise covariance matrix representing the spatiotemporal correlation of noise at multiple stations can be obtained.

[0046] By observation, the coordinate sequences of multiple stations can be determined, and each station's coordinate sequence includes the station's position coordinates at multiple time points. In other words, the coordinate sequences in the embodiments of this invention are all coordinate time series.

[0047] Modelable deterministic components represent systematic signal components in the coordinate sequence of a measuring station that can be accurately described, fitted, and predicted using specific mathematical functions or physical models. Since modelable deterministic components can be analyzed based on physical laws, multiple modelable deterministic components can be pre-defined according to the physical characteristics of the coordinate sequence. For example, modelable deterministic components may include long-term trend terms, periodic changes, step changes, and post-earthquake deformation movements in the coordinate sequence.

[0048] It should be noted that although the aforementioned deterministic components can be analyzed and modeled using physical laws, their specific values ​​vary with the specific coordinate sequence. Therefore, each deterministic component can be analyzed to obtain its first coefficient and first unknown parameter.

[0049] An equation to be solved can be constructed based on the first coefficient and the first unknown parameter. The equation to be solved can include an observation vector, a coefficient matrix, a parameter vector, and a noise term. The coefficient matrix includes the first coefficient, the parameter vector includes the first unknown parameter, the observation vector can be determined based on the coordinate sequence of the target station, and the noise term can be used to represent the noise present in the coordinate sequence.

[0050] Based on the spatiotemporal noise covariance matrix, the maximum likelihood estimation method can be used to solve the equation to be solved, thereby determining the results of the noise term and the parameter vector. Specifically, the spatiotemporal noise covariance matrix can be applied to the equation to be solved for the target station, and the maximum likelihood estimation method can be used to perform overall adjustment estimation of the unknown parameters in the coordinate sequence of the target station, thereby obtaining the results of the noise term and the parameter vector.

[0051] It is understandable that multiple stations of a navigation satellite system can be used as target stations, and the above method can be used to solve the problem, so as to obtain the noise term results and parameter vector results for each station.

[0052] After obtaining the above results, the specific values ​​of the modelable deterministic components in the coordinate sequence of the target station can be determined based on the results of the parameter vector and the coefficient matrix, thereby determining the velocity of the target station.

[0053] According to embodiments of the present invention, by introducing spherical distances between stations to construct spatial correlation and combining coordinate sequences to construct a spatiotemporal noise covariance matrix, accurate modeling of spatiotemporal correlation noise in the station coordinate sequences of a navigation satellite system is achieved. Since the spatiotemporal noise covariance matrix is ​​constructed by combining spatial correlation with temporal correlation, it is possible to comprehensively fit the observation equations by combining both temporal and spatial correlations, significantly reducing the interference of noise on parameter estimation and improving the accuracy and stability of target station velocity calculation.

[0054] According to an embodiment of the present invention, determining the spatial correlation between multiple stations based on the spherical distances between each other in a navigation satellite system can include: substituting the spherical distances into a preset exponential model to obtain the spatial correlation between the two stations corresponding to the spherical distances. The preset exponential model can be determined as follows: based on the sample coordinate sequences of each of the multiple stations and the sample deterministic components corresponding to each of the multiple sample coordinate sequences, multiple sample residual sequences are determined; based on the covariance between the multiple sample residual sequences and the variance of each of the multiple sample residual sequences, a sample correlation coefficient is determined; based on the sample correlation coefficient, the spherical distances, and the sample exponential model, the parameters to be determined in the sample exponential model are fitted to obtain the preset exponential model.

[0055] Methods other than those described in this invention can be used to denoise the sample coordinate sequences of the station and determine the sample deterministic components of each of the multiple sample residual sequences. The sample residual sequences are obtained by removing the sample deterministic components from the sample coordinate sequences.

[0056] In one example, the sample deterministic components can be determined as follows: construct a noise covariance matrix among multiple stations based on their respective sample coordinate sequences; solve the equation to be solved based on the noise covariance matrix to obtain the sample results of the noise term and the parameter vector; and determine the sample deterministic components based on the sample results and the coefficient matrix.

[0057] Based on the noise covariance matrix, the equation to be solved can be obtained by using maximum likelihood estimation, thus obtaining sample results of the noise term and the parameter vector.

[0058] By iteratively fitting the deterministic components of the samples, effective separation of signal and noise is achieved. In the initial stage, spatial correlation is ignored during fitting to extract the deterministic components of the samples, thus obtaining the sample residual sequence. This provides a clean noise data source for the subsequent construction of the spatial correlation model. This iterative strategy ensures mutual promotion and optimization between deterministic component estimation and noise model construction, improving the overall accuracy of data processing.

[0059] For example, the sample coordinate sequence of a certain station is y(t) i The sample deterministic component determined through the above process is μ(t). i ), then the sample residual sequence ε(t) i ) can be determined by equation (1):

[0060] (1)

[0061] The covariance between multiple sample residual sequences and the variance of each sample residual sequence can be calculated separately. The Pearson correlation coefficient between each pair of multiple sample residual sequences can be used as the sample correlation coefficient r(X,Y), as shown in equation (2):

[0062] (2)

[0063] Where X and Y are two sample residual sequences, r(X,Y) is the sample correlation coefficient between X and Y, Cov(X,Y) is the covariance between X and Y, Var(X) represents the variance of X, Var(Y) represents the variance of Y, and n is the number of sample residuals in sequences X and Y. i Let Y be the residual of the i-th sample in sequence X. i Let be the residual of the i-th sample in sequence Y.

[0064] The sample index model can be an index model of the form (3) that includes parameters to be determined:

[0065] (3)

[0066] Where d represents the spherical distance, C(d) represents the calculation result, i.e. the sample correlation coefficient, and a, dc, and b are parameters to be determined.

[0067] Multiple residual sequence groups, each consisting of two sample residual sequences, can be identified. The sample correlation coefficient of each residual sequence group and the spherical distance between the two stations corresponding to the residual sequence group can be determined respectively. The sample correlation coefficient and spherical distance of multiple residual sequence groups can be substituted into equation (3) to determine the value of the parameter to be determined in equation (3) and obtain the preset index model.

[0068] After obtaining the preset exponential model, substituting the spherical distance into the preset exponential model yields the spatial correlation between the two stations corresponding to the spherical distance. In embodiments of the present invention, the spatial correlation coefficient between the two stations can be used to represent the spatial correlation between the two stations.

[0069] According to embodiments of the present invention, the decay law of spatial correlation is inverted using sample data and an exponential model to obtain a preset exponential model, thereby achieving a quantitative and universal description of spatial correlation. The sample correlation coefficient is calculated using the covariance and variance of the sample residuals, ensuring the objectivity of the model parameter fitting. The exponential model can effectively characterize the physical properties of noise decay with distance, providing a reliable mathematical foundation for the subsequent construction of a high-precision spatiotemporal noise covariance matrix.

[0070] According to an embodiment of the present invention, constructing a spatiotemporal noise covariance matrix among multiple stations based on multiple spatial correlations and the noise types included in each of the multiple stations includes: determining, from the noise types included in each of the multiple stations, the second coefficients and second unknown parameters included in each of the multiple preset modelable noise components; for each pair of stations among the multiple stations, determining the spatial noise correlation between the two stations based on the second coefficients and second unknown parameters of the two stations and the spatial correlation between the two stations; for each station among the multiple stations, determining the temporal noise correlation of the station based on the second coefficients and second unknown parameters of the station; and constructing a spatiotemporal noise covariance matrix based on the multiple spatial noise correlations and the multiple temporal noise correlations.

[0071] Modelable noise components can include white noise and flicker noise, and the noise components can be represented by equation (4):

[0072] (4)

[0073] Among them, Q y σ represents the noise component. w σ represents the amplitude of white noise. f Let I represent the amplitude of the flicker noise, and let Q represent the covariance matrix of the white noise. f Let I represent the covariance matrix of the flicker noise. Where I and Q... f It is a fixed matrix, therefore I and Q f The second coefficient, σ, varies depending on the coordinate sequence. w and σ f This is the second unknown parameter.

[0074] In embodiments of the present invention, spatial noise correlation can be represented by spatial correlation noise covariance, and temporal noise correlation can be represented by temporal correlation noise covariance.

[0075] For each pair of stations, based on the spatial correlation between the two stations, the second coefficient of each station, and the second unknown parameter, a covariance matrix representing the spatiotemporal noise correlation between the two stations can be constructed, as shown in equation (5):

[0076] (5)

[0077] Among them, y i and y j These are the coordinate sequences for the two stations, respectively. For the coordinate sequence y i The amplitude of white noise, For the coordinate sequence y j The amplitude of white noise, For the coordinate sequence y iThe amplitude of the flicker noise, For the coordinate sequence y j The amplitude of the flicker noise, The spatial correlation coefficient of white noise between the two stations. Q is the spatial correlation coefficient of flicker noise between two stations. i Q j The time noise correlations of the two stations are Q and Q, respectively. ij This represents the spatial noise correlation between two stations, (Q ij ) T The transpose matrix represents the spatial noise correlation between two stations.

[0078] According to an embodiment of the present invention, the covariance matrix of spatiotemporal noise correlation constructed in the above manner can express the spatial correlation between flicker noise and white noise in the noise covariance matrix. The diagonal elements of this covariance matrix are the sum of the covariance matrices of power-law noise and white noise, and the off-diagonal elements reflect the spatial correlation of noise, including the spatial correlation of power-law noise and the spatial correlation of white noise. The spatial correlation of the above noise can be specifically reflected by the spatial correlation coefficient of the above noise.

[0079] The spatial correlation coefficients of white noise and flicker noise can be determined by cubic spline interpolation using spatial correlation noise models for white noise and flicker noise, respectively.

[0080] Since the noise components include white noise and flicker noise, for each category of noise component, the subspace noise correlation of the noise component can be determined based on the spatial correlation, the second coefficient of the noise component, and multiple second unknown parameters of the noise component. The spatial noise correlation is obtained by summing the subspace noise correlations of multiple noise components. That is, Q in equation (5) above. ij Among them, subspace noise correlation can be expressed as subspace correlated noise covariance, and spatial noise correlation can be expressed as spatial correlated noise covariance.

[0081] According to embodiments of the present invention, the subspace noise correlation is calculated and summed for different noise components, thereby achieving accurate synthesis of the total spatial noise correlation. By considering the different contributions of different noise sources to the spatial correlation, model distortion caused by mixing noises with different characteristics can be avoided, thus improving the adaptability of the spatiotemporal noise covariance matrix to the actual noise environment.

[0082] Combining equation (5) above, the spatiotemporal correlation noise covariance matrix among multiple stations can be constructed using equation (6). :

[0083] (6)

[0084] According to embodiments of the present invention, noise is decomposed into multiple modelable components, and the spatial correlation between noises from multiple stations is calculated, achieving refined analysis of the noise structure. By introducing a second coefficient and a second unknown parameter to describe the characteristics of different noise components, and combining this with a spatially correlated noise model to obtain the spatial covariance, the constructed spatiotemporal noise covariance matrix can truly reflect the complex noise distribution characteristics, further improving the accuracy of parameter estimation.

[0085] According to an embodiment of the present invention, based on the first coefficients and first unknown parameters of each of a plurality of preset modelable deterministic components, an equation to be solved corresponding to a target station is constructed, including: analyzing the plurality of deterministic components to obtain the first coefficients and first unknown parameters of each of the plurality of deterministic components; determining a coefficient matrix based on the plurality of first coefficients and a parameter vector based on the plurality of first unknown parameters; determining the observation vector of the equation to be solved based on the coordinate sequence of the target station; and constructing the equation to be solved based on the observation vector, the coefficient matrix, the parameter vector, and the noise term.

[0086] The coefficient matrix A can be determined based on the first coefficients of each of the multiple deterministic components, the parameter vector x can be determined based on the first unknown parameters of each of the multiple deterministic components of the target station, and the coordinate sequence of the target station can be determined as the observation vector y of the equation to be solved.

[0087] After determining the above elements, the equation to be solved can be constructed as shown in equation (7):

[0088] (7)

[0089] Where ε represents the noise term, and the noise term satisfies ε ~ N(0, σ 2 ).

[0090] According to an embodiment of the present invention, a coefficient matrix and parameter vector are constructed by analyzing deterministic components, and the equation to be solved is constructed by combining the observation vector, thus realizing a standardized expression of the observation data. This structured equation form facilitates subsequent weighted least squares or maximum likelihood estimation by combining the spatiotemporal noise covariance matrix, providing a rigorous mathematical framework for the entire solution process and ensuring the standardization and traceability of the calculation process.

[0091] According to an embodiment of the present invention, the spatiotemporal noise covariance matrix includes the amplitude to be solved corresponding to the noise type of the noise components present in the station. The overall adjustment of the noise term and parameter vector can be estimated and solved using maximum likelihood estimation, where maximum likelihood estimation is a parameter estimation method based on probabilistic thinking, capable of inferring the parameter values ​​most likely to generate these data from sample data.

[0092] Specifically, the equation to be solved is obtained by solving the spatiotemporal noise covariance matrix, yielding the results for the noise term and the parameter vector. This includes: constructing a maximum likelihood estimation solution objective for the equation based on the spatiotemporal noise covariance matrix and its determinant; determining the assumed value of the amplitude to be solved based on historical values ​​of the noise type, and determining the determinant and inverse matrix of the spatiotemporal noise covariance matrix; repeating the following operations until the solution objective converges: instantiating the solution objective based on the determinant and inverse matrix, and determining the gradient direction; adjusting the assumed value based on the gradient direction; and, if the solution objective converges, using the assumed value as the true value of the amplitude to be solved, and determining the results for the noise term and the parameter vector.

[0093] Based on the spatiotemporal noise covariance matrix Q y The determinant det(Q) of the spatiotemporal noise covariance matrix can be determined. y According to the spatiotemporal noise covariance matrix Q y The determinant of the spatiotemporal noise covariance matrix, det(Q) y Given the equation to be solved, we can construct the objective function as shown in equation (8):

[0094] (8)

[0095] Where n represents the number of observations in the coordinate sequence of the target station.

[0096] To improve numerical stability, maximum likelihood estimation can be achieved by maximizing the log-likelihood function. This is done by adjusting the noise covariance matrix Q. y To maximize the log-likelihood value, solve for the corresponding unknown parameter x and process noise parameter σ.

[0097] Specifically, based on the noise type, historical values ​​for white noise and flicker noise can be determined separately, and based on these historical values, assumed values ​​for the amplitudes to be solved for white noise and flicker noise can be determined. These assumed values ​​can then be used to... and Instantiate the matrix and obtain the determinant and inverse of the spatiotemporal noise covariance matrix. Substitute the determinant and inverse into equation (8) to obtain the actual value of the target, and determine the gradient direction based on the actual value and the expected value of the target.

[0098] The assumptions are adjusted based on the gradient direction until the solution converges. Once convergence is confirmed, the true value of the amplitude to be solved, along with the results for the noise term and the parameter vector, are determined based on the current assumptions.

[0099] According to an embodiment of the present invention, the maximum likelihood estimation combined with the gradient maximum method is used to solve the equation to be solved, thereby achieving a joint optimal estimate of the noise amplitude and unknown parameters. By iteratively adjusting the assumed value of the noise amplitude until the objective function converges, the optimal parameters of the noise model can be adaptively determined, solving the problem of the difficulty in accurately setting noise parameters in traditional methods, and significantly improving the reliability and accuracy of the speed calculation.

[0100] After determining the parameter vector, the velocity of the target station can be obtained from the parameter vector. The deterministic component can be represented by equation (9):

[0101] (9)

[0102] in, Indicates the long-term trend term. Indicates periodic changes. Indicates a step jump. This indicates the deformation and movement after the earthquake.

[0103] Figure 3 A data flow diagram illustrating a method for determining the velocity of a station in a navigation satellite system according to an embodiment of the present invention is shown.

[0104] like Figure 3 As shown, multiple stations can be monitored, and the spherical distance 301 between each pair of stations and the coordinate sequence 302 of each station can be determined respectively. The coordinate sequence 302 includes noise components and deterministic components. By analyzing the coordinate sequence 302 based on the noise components and deterministic components respectively, the second coefficient, the second unknown parameter 304 and the first coefficient, the first unknown parameter 305 can be obtained.

[0105] Using a pre-defined exponential model, the spherical distance 301 is calculated to obtain the spatial correlation 303 between each pair of stations. Based on the spatial correlation 303, the second coefficient, and the second unknown parameter 304, the spatiotemporal noise covariance matrix 306 can be obtained. Based on the spatial correlation 303, the first coefficient, and the first unknown parameter 305, the equation to be solved 307 can be obtained.

[0106] Based on the spatiotemporal noise covariance matrix 306, the maximum likelihood estimation method can be used to solve equation 307, yielding result 308. Using result 308 as the first unknown parameter, combined with the first coefficient, the deterministic components in the coordinate sequence 302 can be obtained, thus determining the calculated result 309 representing the station velocity.

[0107] Based on the above-mentioned method for determining the velocity of stations in a navigation satellite system, this invention also provides a device for determining the velocity of stations in a navigation satellite system. The following will be combined with... Figure 4The device is described in detail.

[0108] Figure 4 A schematic block diagram of a velocity determination device for a navigation satellite system according to an embodiment of the present invention is shown.

[0109] like Figure 4 As shown, the velocity determination device 400 for the navigation satellite system station in this embodiment includes a correlation calculation module 410, a matrix determination module 420, an equation construction module 430, and a result fitting module 440.

[0110] The correlation calculation module 410 is used to determine the spatial correlation between multiple stations, including the target station, based on the spherical distances between each pair of stations in the navigation satellite system. In one embodiment, the correlation calculation module 410 can be used to perform the operation S210 described above, which will not be repeated here.

[0111] The matrix determination module 420 is used to construct a spatiotemporal noise covariance matrix among multiple stations based on multiple spatial correlations and the coordinate sequences of each station. The coordinate sequence of each station includes the position coordinates of the station at multiple time points. In one embodiment, the matrix determination module 420 can be used to perform the operation S220 described above, which will not be repeated here.

[0112] The equation construction module 430 is used to construct an equation to be solved corresponding to the target station based on the first coefficients and first unknown parameters of each of multiple preset modelable deterministic components. The equation to be solved includes an observation vector, a coefficient matrix, a parameter vector, and a noise term. The coefficient matrix includes first coefficients, the parameter vector includes first unknown parameters, and the observation vector is determined according to the coordinate sequence of the target station. In one embodiment, the equation construction module 430 can be used to perform the operation S230 described above, which will not be repeated here.

[0113] The result fitting module 440 is used to solve the equation to be solved based on the spatiotemporal noise covariance matrix, obtaining the result of the noise term and the result of the parameter vector, so as to determine the velocity of the target station based on the result of the parameter vector. In one embodiment, the result fitting module 440 can be used to perform the operation S240 described above, which will not be repeated here.

[0114] According to an embodiment of the present invention, the correlation calculation module 410 includes a model instantiation submodule.

[0115] The model instantiation submodule is used to substitute the spherical distance into a preset exponential model to obtain the spatial correlation between two stations corresponding to the spherical distance.

[0116] According to an embodiment of the present invention, the matrix determination module 420 includes a parameter determination submodule, a first correlation determination submodule, a second correlation determination submodule, and a matrix determination submodule.

[0117] The parameter determination submodule is used to determine the second coefficients and second unknown parameters of each of the multiple preset modelable noise components from the coordinate sequences of multiple stations.

[0118] The first correlation determination submodule is used to determine the spatial noise correlation between two stations for each pair of multiple stations, based on the second coefficient and second unknown parameter of each of the two stations and the spatial correlation between the two stations.

[0119] The second correlation determination submodule is used to determine the temporal noise correlation of each of the multiple stations based on the station's second coefficient and second unknown parameter.

[0120] The matrix determination submodule is used to construct the spatiotemporal noise covariance matrix based on multiple spatial noise correlations and multiple temporal noise correlations.

[0121] According to an embodiment of the present invention, the first correlation determination submodule includes a component determination unit and a correlation determination unit.

[0122] The component determination unit is used to determine the subspace noise correlation of a noise component for the same noise component based on spatial correlation, the second coefficient of the noise component, and multiple second unknown parameters of the noise component.

[0123] The correlation determination unit is used to sum the subspace noise correlations of multiple noise components to obtain the spatial noise correlation.

[0124] According to an embodiment of the present invention, the result fitting module 440 includes a target determination submodule, a value determination submodule, a direction determination submodule, a parameter adjustment submodule, and a result determination submodule.

[0125] The objective determination submodule is used to construct a maximum likelihood estimate of the objective for solving the equation based on the spatiotemporal noise covariance matrix and its determinant.

[0126] The value determination submodule is used to determine the assumed value of the amplitude to be solved based on historical values ​​of the noise type, and to determine the determinant and inverse matrix of the spatiotemporal noise covariance matrix.

[0127] The direction determination submodule is used to instantiate the target solution based on the determinant value and the inverse matrix, and determine the gradient direction.

[0128] The parameter tuning submodule is used to adjust the assumed values ​​based on the gradient direction.

[0129] The result determination submodule is used to take the assumed value as the true value of the amplitude to be solved when the solution converges, and to determine the result of the noise term and the result of the parameter vector.

[0130] According to an embodiment of the present invention, the equation construction module 430 includes a component analysis submodule, a determination term construction submodule, a vector construction submodule, and an equation construction submodule.

[0131] The component analysis submodule is used to analyze multiple deterministic components to obtain the first coefficient and first unknown parameter of each deterministic component.

[0132] The determination term construction submodule is used to determine the coefficient matrix based on multiple first coefficients and to determine the parameter vector based on multiple first unknown parameters.

[0133] The vector construction submodule is used to determine the observation vector of the equation to be solved based on the coordinate sequence of the target station.

[0134] The equation construction submodule is used to construct the equation to be solved based on the observation vector, coefficient matrix, parameter vector, and noise term.

[0135] According to embodiments of the present invention, any plurality of modules among the correlation calculation module 410, matrix determination module 420, equation construction module 430, and result fitting module 440 may be combined into one module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of the present invention, at least one of the correlation calculation module 410, matrix determination module 420, equation construction module 430, and result fitting module 440 may be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any one of the three implementation methods or a suitable combination of any of them. Alternatively, at least one of the correlation calculation module 410, matrix determination module 420, equation construction module 430, and result fitting module 440 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0136] Figure 5 A block diagram of an electronic device suitable for implementing a method for determining the velocity of a station in a navigation satellite system, according to an embodiment of the present invention, is shown schematically.

[0137] like Figure 5 As shown, an electronic device 500 according to an embodiment of the present invention includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage portion 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0138] RAM 503 stores various programs and data required for the operation of electronic device 500. Processor 501, ROM 502, and RAM 503 are interconnected via bus 504. Processor 501 executes various operations of the method flow according to embodiments of the present invention by executing programs in ROM 502 and / or RAM 503. It should be noted that the programs may also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 may also execute various operations of the method flow according to embodiments of the present invention by executing programs stored in said one or more memories.

[0139] According to an embodiment of the present invention, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to a bus 504. The electronic device 500 may also include one or more of the following components connected to the input / output (I / O) interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0140] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0141] According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of the present invention, the computer-readable storage medium may include ROM 502 and / or RAM 503 and / or one or more memories other than ROM 502 and RAM 503 described above.

[0142] Embodiments of the present invention also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of the present invention.

[0143] When the computer program is executed by the processor 501, it performs the functions defined in the system / apparatus of this invention. According to embodiments of the invention, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0144] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 509, and / or installed from a removable medium 511. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0145] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by processor 501, it performs the functions defined in the system of this embodiment of the invention. According to embodiments of the invention, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0146] According to embodiments of the present invention, program code for executing the computer programs provided in the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0147] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0148] Those skilled in the art will understand that the features described in the various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention can be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or combinations fall within the scope of the present invention.

[0149] The embodiments of the present invention have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of the invention, and all such substitutions and modifications should fall within the scope of the invention.

Claims

1. A method for determining the velocity of a station in a navigation satellite system, characterized in that, The method includes: Based on the spherical distances between each pair of multiple stations of the navigation satellite system, the spatial correlation between each pair of the multiple stations is determined, and the multiple stations include the target station. Based on the multiple spatial correlations and the noise types included in each of the multiple stations, a spatiotemporal noise covariance matrix is ​​constructed among the multiple stations; Based on the first coefficients and first unknown parameters of each of the multiple preset modelable deterministic components, an equation to be solved corresponding to the target station is constructed. The equation to be solved includes an observation vector, a coefficient matrix, a parameter vector, and a noise term. The coefficient matrix includes the first coefficients, the parameter vector includes the first unknown parameters, and the observation vector is determined according to the coordinate sequence of the target station. The coordinate sequence of each station includes the position coordinates of the station at multiple time points. The equation to be solved is obtained by solving the spatiotemporal noise covariance matrix to obtain the result of the noise term and the result of the parameter vector, so as to determine the velocity of the target station based on the result of the parameter vector; The step of constructing a spatiotemporal noise covariance matrix among the multiple stations based on the spatial correlations and the noise types included in each of the multiple stations includes: The noise type includes a plurality of preset modelable noise components, and each noise component includes a second coefficient and a second unknown parameter. For each pair of the multiple stations, the spatial noise correlation between the two stations is determined based on their respective second coefficients and second unknown parameters, and the spatial correlation between the two stations. For each of the multiple stations, the temporal noise correlation of the station is determined based on the second coefficient and the second unknown parameter of the station. The spatiotemporal noise covariance matrix is ​​constructed based on multiple spatial noise correlations and multiple temporal noise correlations.

2. The method according to claim 1, characterized in that, The step of determining the spatial correlation between each pair of stations based on the spherical distances between each pair of stations in the navigation satellite system includes: Substituting the spherical distance into a preset exponential model, the spatial correlation between the two stations corresponding to the spherical distance is obtained; The preset index model is determined in the following way: Based on the sample coordinate sequences of each of the multiple stations and the sample deterministic components corresponding to each of the multiple sample coordinate sequences, multiple sample residual sequences are determined; The sample correlation coefficient is determined based on the covariance among the multiple sample residual sequences and the variance of each of the multiple sample residual sequences. Based on the sample correlation coefficient, the spherical distance, and the sample index model, the parameters to be determined in the sample index model are fitted to obtain the preset index model.

3. The method according to claim 2, characterized in that, The sample deterministic components are determined in the following way: Based on the sample coordinate sequences of each of the multiple stations, a noise covariance matrix among the multiple stations is constructed. The equation to be solved is solved based on the noise covariance matrix to obtain the sample results of the noise term and the sample fitting results of the parameter vector; Based on the sample results and the coefficient matrix, the deterministic components of the sample are determined.

4. The method according to claim 1, characterized in that, The determination of the spatial noise correlation between the two stations based on their respective second coefficients and second unknown parameters, and the spatial correlation between the two stations, includes: For the same noise component, the subspace noise correlation of the noise component is determined based on the spatial correlation, the second coefficient of the noise component, and multiple second unknown parameters of the noise component. The spatial noise correlation is obtained by summing the subspace noise correlations of each of the multiple noise components.

5. The method according to claim 1, characterized in that, The spatiotemporal noise covariance matrix includes the amplitude to be solved corresponding to the noise type of the noise components present in the station; The step of solving the equation to be solved based on the spatiotemporal noise covariance matrix to obtain the result of the noise term and the result of the parameter vector includes: Based on the spatiotemporal noise covariance matrix and the determinant of the spatiotemporal noise covariance matrix, a maximum likelihood estimation solution objective is constructed for the equation to be solved. Based on the historical values ​​of the noise type, the assumed value of the amplitude to be solved is determined, and the determinant and inverse matrix of the spatiotemporal noise covariance matrix are determined. Repeat the following steps until the objective function converges: Based on the determinant value and the inverse matrix, the objective function is instantiated, and the gradient direction is determined. The assumed value is adjusted based on the gradient direction; If the solution objective converges, the assumed value is taken as the true value of the amplitude to be solved, and the results of the noise term and the parameter vector are determined.

6. The method according to claim 1, characterized in that, The process involves constructing a solution equation corresponding to the target station based on the first coefficients and first unknown parameters of each of multiple preset modelable deterministic components, including: The deterministic components are analyzed to obtain the first coefficient and the first unknown parameter included in each of the deterministic components; The coefficient matrix is ​​determined based on a plurality of first coefficients, and the parameter vector is determined based on a plurality of first unknown parameters; Based on the coordinate sequence of the target station, the observation vector of the equation to be solved is determined; Based on the observation vector, the coefficient matrix, the parameter vector, and the noise term, the equation to be solved is constructed.

7. A velocity determination device for a station in a navigation satellite system, characterized in that, The device includes: The correlation calculation module is used to determine the spatial correlation between each pair of multiple stations of the navigation satellite system based on the spherical distance between each pair of stations, wherein the multiple stations include a target station. The matrix determination module is used to construct a spatiotemporal noise covariance matrix among the multiple stations based on the multiple spatial correlations and the noise types included in each of the multiple stations; The equation construction module is used to construct an equation to be solved corresponding to the target station based on the first coefficients and first unknown parameters of each of the multiple preset modelable deterministic components. The equation to be solved includes an observation vector, a coefficient matrix, a parameter vector, and a noise term. The coefficient matrix includes the first coefficients, the parameter vector includes the first unknown parameters, and the observation vector is determined according to the coordinate sequence of the target station. The coordinate sequence of each station includes the position coordinates of the station at multiple time points. The result fitting module is used to solve the equation to be solved based on the spatiotemporal noise covariance matrix to obtain the result of the noise term and the result of the parameter vector, so as to determine the velocity of the target station based on the result of the parameter vector; The matrix determination module includes: The parameter determination submodule is used to determine the multiple preset modelable noise components included in the noise type, and the second coefficient and second unknown parameter included in each of the noise components. The first correlation determination submodule is used to determine the spatial noise correlation between two stations for each pair of the plurality of stations, based on the second coefficient and second unknown parameter of each of the two stations and the spatial correlation between the two stations. The second correlation determination submodule is used to determine the temporal noise correlation of each of the multiple stations based on the second coefficient and the second unknown parameter of the station. The matrix determination submodule is used to construct the spatiotemporal noise covariance matrix based on multiple spatial noise correlations and multiple temporal noise correlations.

8. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 6.

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