A method for space-time synchronization of heterogeneous giant star constellation systems

Abstract

The present invention discloses a method for a spatiotemporal synchronization system for a heterogeneous giant star constellation system. Based on the core concept of "full network distribution, local concentration, and dynamic nodes," the method implements a distributed processing mode for satellite groups in the entire constellation network and centralized processing mode for each local group of satellites, and ensures trouble-free operation of the constellation system through dynamic master node transfer. The method includes dividing all satellites in the constellation system into several groups and designating master and slave satellites; each group of slave satellites establishes a link for mutual observation and transmits distance and time synchronization observations to the master satellite via an intersatellite link; each master satellite calculates the position, velocity, clock error, frequency deviation, and frequency drift of each slave satellite using a centralized method based on the received observation data, and feeds the data back to the slave satellite; when a slave satellite fails to receive a feedback signal from the master satellite within a synchronization period, the master satellite selection mechanism is triggered to implement dynamic master node transfer; all master satellites establish a link for mutual observation and use a distributed method to calculate the optimal estimated value of the state, clock error, and clock drift of each master satellite.

Description

Technical Field

[0001] The present invention relates to giant star constellation technology, and in particular to a space-time synchronization system method for a heterogeneous giant star constellation system. Background Art

[0002] Heterogeneous mega-satellite constellations consist of a large number of satellites of varying types and functions, ranging from thousands to tens of thousands. These satellites may vary in orbital altitude, communication capabilities, sensor types, and computing power. High-precision orbit determination and precise time synchronization are key to ensuring efficient operation of satellite constellations. However, as the number of satellites increases dramatically, achieving spatiotemporal synchronization for a giant constellation consisting of tens of thousands of heterogeneous satellites presents numerous challenges and technical challenges that require further research.

[0003] The prior art CN202110997872.X discloses a system and method suitable for joint orbit determination of networked low-orbit satellites. The implementation idea of ​​this method is: first, obtaining observation data and measurement data; second, obtaining the predicted orbit information of each LEO constellation satellite based on the obtained observation data and measurement data; finally, obtaining the orbit information of the LEO constellation satellite based on the obtained measurement data and predicted orbit information.

[0004] This invention only considers low-orbit satellites and is not applicable to scenarios involving heterogeneous giant star constellations. Furthermore, this method combines distributed autonomous orbit determination onboard the satellite with centralized orbit determination in ground-based data centers, making it unsuitable for scenarios with large satellite populations. As the number of satellites increases, the orbit determination accuracy of the distributed onboard method decreases. Furthermore, it places additional demands on ground equipment, preventing the realization of fully autonomous orbit determination for constellations.

[0005] The prior art CN201811509599.6 discloses a distributed autonomous orbit determination method using multi-star-target satellite angular distance fusion. Its implementation idea is: first, establish a multi-star-target satellite angular distance information preprocessing model; second, calculate the right ascension and declination of the observation satellite-target satellite connection line, and then use the angular distance information combined with the relative distance measurement between the constellation satellites as the observation quantity to establish the measurement equation; finally, combined with the constellation orbit dynamics recursion, a distributed method is used to complete the autonomous orbit determination of the entire satellite constellation.

[0006] This invention uses a distributed method to estimate the navigation parameters of the observation satellite and the target satellite to perform orbit determination for the entire constellation network. It is not suitable for constellation systems with a huge number of satellites. As the number of satellites increases, the orbit determination results obtained by the distributed method have poor accuracy. Summary of the Invention

[0007] In response to the above-mentioned deficiencies in the existing technology, the present invention provides a method for the spatiotemporal synchronization system of a heterogeneous giant star constellation system, centering on the core idea of ​​"full network distribution, local concentration, and dynamic nodes", which solves the problem of poor orbit determination accuracy of the existing technology with the increase in the number of satellites and the on-board fully distributed method.

[0008] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0009] A method for space-time synchronization of a heterogeneous giant star constellation system is provided, comprising the steps of:

[0010] S1. Divide all satellites in the heterogeneous Giant Star constellation into several groups based on the principle of grouping a preset number of adjacent satellites. In each group, one satellite is randomly selected as the master satellite, and the remaining satellites are slave satellites.

[0011] S2. In each group, the slave satellites establish a link between each other and transmit the calculated distance observations and time synchronization observations to the master satellite through the intersatellite link;

[0012] S3. Each master satellite calculates the position, velocity, clock error, frequency offset, and frequency drift of each slave satellite using a centralized data processing method based on the distance observations and time synchronization observations received from all slave satellites, and feeds these back to the slave satellites via the intersatellite link.

[0013] S4. During a synchronization cycle, if a slave satellite fails to receive feedback from the master satellite, the master satellite selection mechanism is triggered, and the slave satellite with the highest priority is selected as the master satellite to achieve dynamic master node transfer;

[0014] S5. All primary stars establish a link observation with each other, and use distributed data processing methods to calculate the optimal estimate of the state of each primary star and the optimal estimate of the clock error and clock drift.

[0015] The beneficial effects of the present invention are as follows: This solution revolves around the core idea of ​​"full network distribution, local concentration, and dynamic nodes", realizes the processing mode of distributed satellite grouping of the entire constellation network and centralized processing of each local satellite group, and ensures the trouble-free operation of the constellation system through dynamic master node transfer.

[0016] This scheme targets heterogeneous giant satellite constellation systems composed of a large number of satellites of different types or functions. By grouping the heterogeneous giant satellite constellation system, the computational complexity of a single primary satellite can be significantly reduced, thus enabling the heterogeneous giant satellite constellation system to achieve spatiotemporal synchronization with lower computational complexity.

[0017] This scheme establishes a chain of observations between the slave stars in each group to obtain distance observations and time synchronization observations. The master star is then used to calculate the data of the slave stars in its group. After that, the data is synchronized between all the master stars. This method first concentrates locally and then distributes the space-time synchronization across the entire network, which can achieve high-precision spatial position synchronization and time information synchronization of heterogeneous giant constellations.

[0018] During the data synchronization process, this solution takes into account the situation of master satellite failure. Through the election mechanism, the master satellite transfer mechanism can be dynamically implemented to ensure that the new centralized master satellite can take over all management functions to ensure the completion of the space-time synchronization task.

[0019] Furthermore, step S2 further includes:

[0020] S21. From the mutual link observation between satellites, we can obtain the two-way inter-satellite ranging observation equation:

[0021]

[0022] in, are the intersatellite observation quantities between satellites AB and BA respectively; t A , t B are the nominal launch times of satellite A and B signals respectively; The intersatellite distances between satellites AB and BA at the time of sending and receiving, respectively; Δ AB , Δ BA are the signal propagation delays between satellites AB and BA respectively; C A 、C B are the clock errors of satellites A and B, are the receiving and transmitting delays of satellite A respectively; are the receiving and transmitting delays of satellite B respectively; O AB , O BA are the observation corrections between satellites AB and BA, including antenna phase center correction, relativistic correction and satellite-ground troposphere correction; ε AB , ε BA are the measurement noise between satellites AB and BA respectively;

[0023] S22. Preprocess the two-way inter-satellite ranging observation equation to obtain the inter-satellite distance observation equation and the inter-satellite time synchronization observation equation:

[0024]

[0025] in, and are distance observation and time synchronization observation respectively; is the instantaneous pseudorange observation between satellites AB and BA at time t0 after preprocessing; is the theoretical value of the inter-satellite distance at time t0; C A 、C B are the instantaneous clock differences of satellites A and B respectively; are the residual errors between satellites AB and BA respectively.

[0026] The beneficial effect of the above technical solution is: by preprocessing the two-way inter-satellite ranging observation equation, the inter-satellite observation equation related to spatial position and the inter-satellite time synchronization observation equation related to clock information can be decoupled, and then the orbit parameters and clock parameters can be solved separately. This technology can reduce the latitude of the unknown number matrix, reduce the amount of calculation, and improve the calculation efficiency.

[0027] Furthermore, the method for calculating the position and velocity of each slave satellite using a centralized data processing method in step S3 includes:

[0028] S31. Based on the prior orbit and dynamic parameters, the satellite dynamic equation and its corresponding variational equation are integrated using the numerical integration method to obtain the reference orbit and state transfer matrix of the preset epoch. The expression of the satellite dynamic equation is:

[0029]

[0030] in, is the satellite’s acceleration vector; is the initial position vector of the satellite; is the initial velocity vector of the satellite; is the set of satellite dynamic model parameters; f(·) is a nonlinear function;

[0031] S32. Linearize the satellite distance observation equation using the reference orbit to obtain the linearized observation equation:

[0032]

[0033] in, is the instantaneous star distance after preprocessing; is the instantaneous satellite distance calculated using the reference orbit; ρ AB is the intersatellite distance between satellites A and B; and are the state vectors of satellites A and B, respectively, which include position and velocity; and are the initial position vector, initial velocity vector and dynamic model parameters of satellite A and satellite B; and They are Correction value of d and They are Correction value; dD A ,dD B are the intersatellite ranging bias corrections for A and B respectively; is the observation noise;

[0034] S33. Based on the linearized observation equation, combine the distance observations between all two slave stars in each group to establish a centralized processing parameter estimation equation:

[0035] Y=HX+ε,Y=(ΔP 12 ΔP 1i … ΔP jn ) T

[0036]

[0037] X=(ΔX1 ΔD1 ΔX2 ΔD2 … ΔX n ΔD n ) T

[0038]

[0039] Where Y is the pseudorange differential observation vector; ΔP 12 , ΔP 1i and ΔP jn are the pseudorange differential observations between satellites 1 and 2, satellites 1 and i, and satellites j and n, respectively; T is the transpose; H is the inter-satellite pseudorange differential observation matrix, which has 2n columns and n(n-1) / 2 rows. Each row in H corresponds to an observation equation, and the 1 and -1 in H are the coefficients of the clock error term; ρ 12 , ρ 1i , ρ jn and ρ ij The inter-satellite pseudoranges are from the 1st satellite to the 2nd satellite, from the 1st satellite to the i-th satellite, from the j-th satellite to the n-th satellite, and from the i-th satellite to the j-th satellite respectively; is the partial derivative; X is the parameter matrix to be estimated; X1, X2, X i and X n are the parameters to be estimated for satellites 1, 2, i, and n, including the position and velocity of the satellites; ΔD1, ΔD2, and ΔD n are the clock corrections of satellites 1, 2 and n respectively; is the position vector of satellite i at the observation time; n is the total number of satellites;

[0040] S34. Combine all the distance observations between each other at different times and use the least squares estimation strategy to obtain the parameter to be estimated X:

[0041] X=(HT PH) ―1 HY

[0042] Among them, P is the weight matrix in the least squares estimation formula.

[0043] The beneficial effects of the above technical solution are: the centralized processing method using the least squares strategy to calculate the position and velocity of each group of satellites can simultaneously process the observation data of all the slave satellites in the group, obtain the optimal solution for the state of the group of satellites, and improve the accuracy of the overall orbit determination; at the same time, the comprehensive use of all observation data can improve the reliability of the estimation of the position and velocity parameters of the group of satellites.

[0044] Furthermore, the method for calculating the clock error, frequency offset, and frequency drift of each slave satellite using a centralized data processing method in step S3 includes:

[0045] A1. Based on the inter-satellite time synchronization observation equation and the satellite reception and transmission delay, the observation equation for estimating the satellite clock error, frequency offset and frequency drift is constructed:

[0046]

[0047] in, is the estimated value of intersatellite time synchronization observation; a A and a B 、b A and b B and c A and c B are the clock error, frequency bias, and frequency drift of satellite A and satellite B respectively; t1 is the observation time after epoch normalization; is the reference epoch;

[0048] A2. Based on the observation equations for estimating satellite clock error, frequency bias, and frequency drift, a combined observation equation is constructed:

[0049]

[0050] in, is the time-synchronized observation vector; are the observed values ​​of clock error, frequency offset and frequency drift information between satellites 1 and 2, satellites 1 and i, satellites j and n, and satellites n-1 and n, respectively; T is the transpose; is the design matrix of the observation equation, Δt=t1―t0 is the time difference, The 1 or -1 in represents the coefficient of the clock error term; is the combined matrix of clock error, frequency offset and frequency drift of all slave satellites in each group; a1,

[0051] a2、a n―1 and a nare the clock errors of satellites 1, 2, n-1 and n respectively; b1, b2, b n―1 and b n are the frequency deviations of satellites 1, 2, n-1 and n respectively; c1, c2, c n―1 and c n The frequency drifts of satellites 1, 2, n-1 and n are respectively; A3, using the clock error constraint method, constructs the observation vector to eliminate Virtual observation equation for medium rank deficiency:

[0052]

[0053] in, are the prior values ​​of the clock error of satellite j; are the accuracy of satellite j; a j 、b j 、c j For The clock parameters of the j-th satellite extracted from ;

[0054] A4. Combined observation vector And the virtual observation equation, we get the combined relationship:

[0055]

[0056] in, is the combined clock information observation; is the combined observation matrix; is the residual error after combination; is a row vector;

[0057] A5. Use the least squares estimation strategy to find the optimal estimate of the combined relationship:

[0058]

[0059] Among them, the optimal estimate Contains the clock error, frequency deviation and frequency drift of each slave satellite.

[0060] The beneficial effects of the above technical solution are as follows: by modeling clocks using polynomials, the three parameters of slave satellite clock error, frequency offset, and frequency drift can be simultaneously calculated. Using a least-squares strategy for centralized processing of clock error, frequency offset, and frequency drift for each group of satellites simultaneously processes observation data from all slave satellites in the group, resulting in an optimal clock solution for the group and improving the accuracy of overall time synchronization. Furthermore, the comprehensive utilization of all observation data improves the reliability of the three parameter estimates for the group of satellites.

[0061] Furthermore, the method of calculating the optimal estimated value of the state of each primary star using the distributed data processing method includes:

[0062] S51, the extended Kalman filter state transfer equation is:

[0063] x k+1 =f(x k ,p,t)+w

[0064] Among them, x k+1 is the true state vector of the primary star at time k+1; x k is the true state vector of the primary star at time k; p is the covariance matrix; t is the time of primary star motion; w is the system process noise; f(·) is the nonlinear function;

[0065] S52, the time update calculation formula of the extended Kalman filter is:

[0066]

[0067] in, is the estimated value of the primary star state predicted at time k+1; is the current state vector of the primary star at time k; Φ is the state transfer matrix, Find the partial derivative of the f(·) function with respect to the true state vector of the primary star at time k, is the k+1 time prediction error covariance matrix, is the error covariance matrix at time k, Φ T is the transpose of the state transfer matrix, Q is the system process noise covariance matrix;

[0068] S53. Extend the Kalman filter observation update calculation formula to obtain the optimal estimated value of the state of each primary star:

[0069]

[0070] in, is the optimal estimated value of the state of the primary star at time k+1, H a is the observation matrix of the extended Kalman filter, H a T For H a Find the transpose; R is the observation noise covariance matrix; is the inter-satellite distance residual Δρ of all the primary stars that can be linked in the current epoch ij The vector formed, is the optimal estimation error covariance matrix at time k+1.

[0071] The beneficial effects of the above technical solution are: using the extended Kalman filter algorithm for distributed processing to solve the optimal state estimate between several master satellites in the entire network, the computing tasks can be distributed to multiple master satellites, reducing the computing burden of each master satellite and improving computing efficiency; at the same time, the distributed algorithm can reduce processing delays, increase the overall computing speed of the system, and meet the needs of real-time orbit determination.

[0072] Furthermore, the inter-satellite residual Δρ ij The methods for obtaining include:

[0073] S531. Construct the intersatellite two-way observation equation between primary stars i and j as follows:

[0074]

[0075] Among them, ρ ij , ρ ij is the pseudo-range observation quantity of primary star i receiving primary star j and primary star j receiving primary star i; c is the speed of light; t i , t j are the clock errors of primary stars i and j respectively; ε ij , ε ij is the observation noise from primary star i to primary star j and from primary star j to primary star i; is the theoretical interstellar distance between the main stars i and j, which is expressed as:

[0076]

[0077] Among them, x i 、x j are the position components of the primary stars i and j in the x-axis direction; y i 、y j are the position components of the primary stars i and j in the y-axis direction respectively; z i 、z j are the position components of the primary stars i and j in the z-axis direction respectively;

[0078] S532. Use the reference orbit linearization to obtain the linear observation equation without satellite clock error:

[0079]

[0080] Where ρ is the linear observation without satellite clock error; is the approximate satellite-to-ground distance calculated using the reference orbit; are the correction values ​​of the positions of primary stars i and j relative to the reference orbit; L m is the star spacing unit vector, which is in the form of ε′ is the observation noise;

[0081] S533. Based on the linear observation equation without satellite clock error, the Kalman filter observation equation is obtained as follows:

[0082]

[0083] Where Δρ ij is the residual error of the inter-satellite distance.

[0084] The beneficial effect of the above technical solution is that by processing the inter-satellite two-way observation equation, a linearized observation equation can be obtained, and an expression form of the inter-satellite distance residual related to the Kalman filter algorithm can be obtained, which is convenient for introducing into the extended Kalman filter to calculate the parameters to be estimated.

[0085] Furthermore, the method for calculating the optimal estimated value of the clock error and clock drift of each primary star using a distributed data processing method includes:

[0086] B1. Construct a linear model containing two states to represent the satellite clock error:

[0087]

[0088] Among them, b k d k is the clock error and clock drift of the primary star at time k; b k+1 d k+1 is the clock error and clock drift of the primary star at time k+1; dt is the sampling interval; ω b 、ω d They are white FM noise and random walk FM noise respectively;

[0089] B2. The time update calculation formula of the extended Kalman filter is:

[0090]

[0091] in, is the satellite clock estimate predicted at time k, including clock error and clock drift; φ k,k―1 The state transition matrix solved for the clock parameters; φ T k,k―1 For φ k,k―1 Find the transpose; is the satellite clock parameter at time k-1; is the clock error covariance matrix predicted at time k; is the error covariance matrix at time k-1; Q k―1 is the covariance matrix of white FM noise and random walk FM noise at time k-1;

[0092] B3. The observation update calculation formula of the extended Kalman filter is:

[0093]

[0094] Among them, K k is the Kalman gain matrix at time k, is the observation matrix, For Find the transpose; R k is the clock error covariance matrix at time k; (·) ―1 To find the inverse operation; is the optimal estimate of the satellite clock error and clock drift at time k; Z k―1 is the actual measurement value of the satellite clock error and clock drift at time k-1; is the optimal estimation error covariance matrix at time k, and I is the identity matrix.

[0095] The beneficial effects of the above technical solution are: using the extended Kalman filter algorithm for distributed processing to solve the optimal estimated values ​​of clock errors and clock drifts among several master satellites in the entire network, the computing tasks can be distributed to multiple master satellites, reducing the computing burden of each master satellite and improving computing efficiency; at the same time, the distributed algorithm can reduce time synchronization delays, quickly respond to clock changes, and meet the needs of implementing time synchronization.

[0096] Furthermore, the master star selection mechanism is triggered to select the slave star with the highest priority as the master star. The method for realizing dynamic master node transfer includes:

[0097] S41, modify each slave star in the group to a candidate state;

[0098] S42. Each candidate slave satellite sends its own campaign information to other surrounding candidate slave satellites to form a campaign information set; the campaign information includes communication capability, computing capability, orbit error, and clock error;

[0099] S43: The secondary star that initiated the election is designated as the first election star, any secondary star that received the election information is designated as the second election star, and the election information of the second election star is added to the election information set;

[0100] S44. Based on the election information of the first and second election stars, the one with the higher priority is selected as the new first election star;

[0101] S45: Determine whether there is a candidate slave star in the group that has not participated in the comparison. If so, proceed to step S46; otherwise, take the first candidate star selected most recently as the master star and proceed to step S47;

[0102] S46: The first candidate star sends its own campaign information to other candidate slave stars in the surrounding area, and any slave star that receives the campaign information is selected as the second candidate star. The campaign information of the first candidate star and the second candidate star is stored in a new campaign information set, and the process returns to step S44.

[0103] S47: The selected master satellite broadcasts the information of its successful selection to all slave satellites in the group, and other slave satellites confirm and synchronize their status.

[0104] The beneficial effects of the above technical solution are: the master star selection mechanism ensures that when the original master star fails, the master star can be dynamically transferred to ensure that the new centralized master star can take over all management functions to ensure the completion of the space-time synchronization task; and this technology can definitely select a new master star through direct comparison of election information, and there will be no election failure.

[0105] Furthermore, step S44 further includes:

[0106] There are four situations when determining the priority of the first and second campaign stars:

[0107] First, the orbit error of the first candidate satellite is smaller than the orbit error of the second candidate satellite, and the clock error of the first candidate satellite is smaller than the clock error of the second candidate satellite;

[0108] Second, the orbit error of the first candidate satellite is smaller than the orbit error of the second candidate satellite, and the clock error of the second candidate satellite is smaller than the clock error of the first candidate satellite;

[0109] Third, the orbit error of the second candidate satellite is smaller than that of the first candidate satellite, and the clock error of the first candidate satellite is smaller than that of the second candidate satellite;

[0110] Fourth, the orbit error of the second candidate satellite is smaller than that of the first candidate satellite, and the clock error of the second candidate satellite is smaller than that of the first candidate satellite;

[0111] When the first, second, and third conditions are met, the first campaign star is considered to have a higher priority; when the fourth condition is met, the second campaign star is considered to have a higher priority.

[0112] Furthermore, the method for calculating the preset number includes:

[0113] Starting from 1, the number of main stars is selected in ascending order, and the complexity of the selected number of main stars is calculated using the complexity model. The expression of the complexity model is:

[0114] O=(3*N / k) 3

[0115] Among them, O is the complexity; N is the total number of satellites in the giant star constellation network; k is the number of main stars;

[0116] When the difference between the complexities corresponding to two adjacent numbers of primary stars selected is greater than a preset threshold, the larger value of the two numbers of primary stars is selected as the preset number.

[0117] The beneficial effect of the above technical solution is: this solution deduces that the computational complexity is only related to the complexity of the matrix inversion operation through the process of solving the clock error. Therefore, when selecting the master satellite, a model is constructed based on the computational complexity and the total number of satellites in the entire network and the number of master satellites. The optimal number of master satellites can be quickly selected through the computational complexity corresponding to two adjacent values, thereby ensuring the efficiency of space-time synchronization. BRIEF DESCRIPTION OF THE DRAWINGS

[0118] Figure 1 The figure is a flow chart of the space-time synchronization system method for the heterogeneous giant star constellation system.

[0119] Figure 2 This is a simulation diagram of the computational complexity of a single star (taking N=10000 as an example). DETAILED DESCRIPTION

[0120] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0121] refer to Figure 1 , Figure 1 A method for time-space synchronization of heterogeneous giant star constellation systems is shown; Figure 1 As shown, the method S includes steps S1 to S5.

[0122] In step S1, all satellites in the heterogeneous giant star constellation system are divided into several groups based on the principle that a preset number of adjacent satellites are grouped together. In each group, one satellite is randomly selected as the master satellite, and the remaining satellites are slave satellites.

[0123] In one embodiment of the present invention, the method for calculating the preset number includes:

[0124] Starting from 1, the number of main stars is selected in ascending order, and the complexity of the selected number of main stars is calculated using the complexity model. The expression of the complexity model is:

[0125] O=(3*N / k z ) 3

[0126] Where O is the complexity; N is the total number of satellites in the Giant Star constellation network; kz The number of main stars;

[0127] When the difference between the complexities corresponding to two adjacent numbers of primary stars selected is greater than a preset threshold, the larger value of the two numbers of primary stars is selected as the preset number.

[0128] like Figure 2 As shown in , this curve depicts the change in the computational complexity of each main satellite as the number of main satellites gradually increases. Here, the vertical axis data is logarithmically processed. Figure 2 The simulation results show that in the centralized processing method, as the number of primary satellites increases, when the total number of satellites in the Giant Star constellation system is 10,000, when k z =100, the computational complexity of a single primary star will be significantly reduced.

[0129] Therefore, in this scheme, the heterogeneous giant satellite constellation system is divided into groups according to the principle of dividing every 100 adjacent satellites into a group, so that time and space synchronization can be achieved with lower computational complexity.

[0130] In step S2, in each group, the slave satellites establish a link to observe each other, and transmit the calculated distance observations and time synchronization observations to the master satellite through the intersatellite link.

[0131] During implementation, the preferred step S2 of this solution further includes:

[0132] S21. From the mutual link observation between satellites, we can obtain the two-way inter-satellite ranging observation equation:

[0133]

[0134] in, are the intersatellite observation quantities between satellites AB and BA respectively; t A , t B are the nominal launch times of satellite A and B signals respectively; The intersatellite distances between satellites AB and BA at the time of sending and receiving, respectively; Δ AB , Δ BA are the signal propagation delays between satellites AB and BA respectively; C A 、C B are the clock errors of satellites A and B, are the receiving and transmitting delays of satellite A respectively; are the receiving and transmitting delays of satellite B respectively; O AB , O BA are the observation corrections between satellites AB and BA, including antenna phase center correction, relativistic correction and satellite-ground troposphere correction; ε AB , ε BAare the measurement noise between satellites AB and BA respectively;

[0135] S22. Preprocess the two-way inter-satellite ranging observation equation to obtain the inter-satellite distance observation equation and the inter-satellite time synchronization observation equation:

[0136]

[0137] in, and are distance observation and time synchronization observation respectively; is the instantaneous pseudorange observation between satellites AB and BA at time t0 after preprocessing; is the theoretical value of the inter-satellite distance at time t0; C A 、C B are the instantaneous clock differences of satellites A and B respectively; are the residual errors between satellites AB and BA respectively.

[0138] In step S3, each master satellite calculates the position, velocity, clock error, frequency offset and frequency drift of each slave satellite based on the distance observations and time synchronization observations received from all slave satellites using a centralized data processing method, and feeds back the calculated values ​​to the slave satellites via the intersatellite link.

[0139] In one embodiment of the present invention, the method of calculating the position and velocity of each slave satellite using a centralized data processing method in step S3 includes:

[0140] S31. Based on the prior orbit and dynamic parameters, the satellite dynamic equation and its corresponding variational equation are integrated using the numerical integration method to obtain the reference orbit and state transfer matrix of the preset epoch. The expression of the satellite dynamic equation is:

[0141]

[0142] in, is the satellite’s acceleration vector; is the initial position vector of the satellite; is the initial velocity vector of the satellite; is the set of satellite dynamic model parameters; f(·) is a nonlinear function;

[0143] S32. Linearize the satellite distance observation equation using the reference orbit to obtain the linearized observation equation:

[0144]

[0145] in, is the instantaneous star distance after preprocessing; is the instantaneous satellite distance calculated using the reference orbit; ρ ABis the intersatellite distance between satellites A and B; and are the state vectors of satellites A and B, respectively, which include position and velocity; and are the initial position vector, initial velocity vector and dynamic model parameters of satellite A and satellite B; and They are Correction value of and They are Correction value; dD A ,dD B are the intersatellite ranging bias corrections for A and B respectively; is the observation noise;

[0146] S33. Based on the linearized observation equation, combine the distance observations between all two slave stars in each group to establish a centralized processing parameter estimation equation:

[0147] Y=HX+ε,Y=(ΔP 12 ΔP 1i … ΔP jn ) T

[0148]

[0149] X=(ΔX1 ΔD1 ΔX2 ΔD2 … ΔX n ΔD n ) T

[0150]

[0151] Where Y is the pseudorange differential observation vector; ΔP 12 , ΔP 1i and ΔP jn are the pseudorange differential observations between satellites 1 and 2, satellites 1 and i, and satellites j and n, respectively; T is the transpose; H is the inter-satellite pseudorange differential observation matrix, which has 2n columns and n(n-1) / 2 rows. Each row in H corresponds to an observation equation, and the 1 and -1 in H are the coefficients of the clock error term; ρ 12 , ρ 1i , ρ jn and ρ ij The inter-satellite pseudoranges are from the 1st satellite to the 2nd satellite, from the 1st satellite to the i-th satellite, from the j-th satellite to the n-th satellite, and from the i-th satellite to the j-th satellite respectively; is the partial derivative; X is the parameter matrix to be estimated; X1, X2, X i and Xn are the parameters to be estimated for satellites 1, 2, i, and n, including the position and velocity of the satellites; ΔD1, ΔD2, and ΔD n are the clock corrections of satellites 1, 2 and n respectively; is the position vector of satellite i at the observation time; n is the total number of satellites;

[0152] The function of the matrix H is to establish a linear relationship between the observed values ​​and the parameters to be estimated. To facilitate understanding of H, let's take three satellites as an example (there will be three sets of observations). The expression of H is:

[0153]

[0154] Among them, the first line is the observation equation between satellites 1 and 2; the second line is the observation equation between satellites 1 and 3, and the third line is the observation equation between satellites 2 and 3.

[0155] Taking 4 satellites as an example (there will be 6 sets of observations), the expression of H is:

[0156]

[0157] Among them, the first line is the observation equation between satellites 1 and 2; the second line is the observation equation between satellites 1 and 3, the third line is the observation equation between satellites 1 and 4; the fourth line is the observation equation between satellites 2 and 3, the fifth line is the observation equation between satellites 2 and 4, and the sixth line is the observation equation between satellites 3 and 4.

[0158] In H, each row has only 4 non-zero elements (2 partial derivatives, 2 ±1), and the rest are 0; the determination of the positive and negative is explained: the determination of ± is related to the pseudo-range differential observation ΔP in Y ij The definition of ΔP is related to: ij It represents the pseudo-range differential observation value from satellite i to satellite j. The clock difference coefficient of satellite i with the number written in front is 1, and the clock difference coefficient of satellite j with the number written in the back is -1.

[0159] Based on the matrix H corresponding to the above 3 satellites and 4 satellites, it can be seen that when there are n satellites, the rows of the matrix H are, from top to bottom, the observation equations of satellite 1 and satellites 2 to n, the observation equations of satellite 2 and satellites 3 to n, the observation equations of satellite 3 and satellites 4 to n, ..., the observation equations of satellite n-1 and satellite n.

[0160] S34. Combine all the distance observations between each other at different times and use the least squares estimation strategy to obtain the parameter to be estimated X:

[0161] X=(H T PH) ―1 HY

[0162] Among them, P is the weight matrix in the least squares estimation formula.

[0163] During implementation, the method for calculating the clock error, frequency offset, and frequency drift of each slave satellite using a centralized data processing method in step S3 of this solution preferably includes:

[0164] A1. Based on the inter-satellite time synchronization observation equation and the satellite reception and transmission delay, the observation equation for estimating the satellite clock error, frequency offset and frequency drift is constructed:

[0165]

[0166] in, is the estimated value of intersatellite time synchronization observation; a A and a B 、b A and b B and c A and c B are the clock error, frequency bias, and frequency drift of satellite A and satellite B, respectively; Δ1 is the observation time after epoch normalization; is the reference epoch;

[0167] A2. Based on the observation equations for estimating satellite clock error, frequency bias, and frequency drift, a combined observation equation is constructed:

[0168]

[0169]

[0170] in, is the time-synchronized observation vector; are the observed values ​​of clock error, frequency offset and frequency drift information between satellites 1 and 2, satellites 1 and i, satellites j and n, and satellites n-1 and n, respectively; T is the transpose; is the design matrix of the observation equation, Δt=t1―t0 is the time difference, The 1 or -1 in the equation represents the coefficient of the clock error term, Δt, Δt 2 are the coefficients of the satellite clock error, frequency bias and frequency drift in the observation equation respectively; is the combined matrix of clock error, frequency offset and frequency drift of all slave satellites in each group; a1, a2, a n―1 and a n are the clock errors of satellites 1, 2, n-1 and n respectively; b1, b2, b n―1 and b n are the frequency deviations of satellites 1, 2, n-1 and n respectively; c1, c2, c n―1 and c n are the frequency drifts of satellites 1, 2, n-1 and n respectively;

[0171] for Matrix, taking 4 satellites as an example, its expression is:

[0172]

[0173] exist In the equation, columns 1 to 3 correspond to the clock error, frequency deviation, and frequency drift coefficients of satellite 1, columns 4 to 6 correspond to the clock error, frequency deviation, and frequency drift coefficients of satellite 2, columns 7 to 9 correspond to the clock error, frequency deviation, and frequency drift coefficients of satellite 3, columns 10 to 12 correspond to the clock error, frequency deviation, and frequency drift coefficients of satellite 4, and so on. When there are n satellites, then The matrix consists of 3n columns, and each column consists of n(n-1) / 2 rows.

[0174] exist In the equation, the first line is the observation equation between satellites 1 and 2, the second line is the observation equation between satellites 1 and 3, and so on. The definition logic of the positive and negative signs in is the same as the definition logic of the matrix H in S33. The positive sign of the first satellite is positive and the negative sign of the second satellite is negative.

[0175] A3. Use the clock error constraint method to construct the observation matrix to eliminate the observation Virtual observation equation for medium rank deficiency:

[0176]

[0177] in, are the prior values ​​of the clock error of satellite j; are the accuracy of satellite j; a j 、b j 、c j For The clock parameters of the j-th satellite extracted from ;

[0178] A4. Combined observation vector And the virtual observation equation, we get the combined relationship:

[0179]

[0180] in, is the combined clock information observation; is the combined observation matrix; is the residual error after combination; is a row vector, in which the three consecutive elements corresponding to the extracted j-th satellite are 1, and the rest are 0.

[0181] It is used to Extract the clock parameters of the jth satellite (aj ; b j ;c j ), The three consecutive 1s in correspond to the positions of the three clock parameters to be extracted (i.e., the positions of the corresponding j-th satellite parameters), and the other positions are all 0.

[0182] A5. Use the least squares estimation strategy to find the optimal estimate of the combined relationship:

[0183]

[0184] Among them, the optimal estimate Contains the clock error, frequency deviation and frequency drift of each slave satellite.

[0185] In step S4, within a synchronization cycle, when a slave star fails to receive a feedback signal from the master star, the master star selection mechanism is triggered, and the slave star with the highest priority is selected as the master star to achieve dynamic master node transfer.

[0186] During implementation, this solution will prioritize triggering the master star selection mechanism and select the slave star with the highest priority as the master star. The methods for achieving dynamic master node transfer include:

[0187] S41, modify each slave star in the group to a candidate state;

[0188] S42. Each candidate slave satellite sends its own campaign information to other surrounding candidate slave satellites to form a campaign information set; the campaign information includes communication capability, computing capability, orbit error, and clock error;

[0189] S43: The secondary star that initiated the election is designated as the first election star, any secondary star that received the election information is designated as the second election star, and the election information of the second election star is added to the election information set;

[0190] S44. Based on the election information of the first and second election stars, the one with the higher priority is selected as the new first election star;

[0191] S45: Determine whether there is a candidate slave star in the group that has not participated in the comparison. If so, proceed to step S46; otherwise, take the first candidate star selected most recently as the master star and proceed to step S47;

[0192] S46: The first candidate star sends its own campaign information to other candidate slave stars in the surrounding area, and any slave star that receives the campaign information is selected as the second candidate star. The campaign information of the first candidate star and the second candidate star is stored in a new campaign information set, and the process returns to step S44.

[0193] S47: The selected master satellite broadcasts the information of its successful selection to all slave satellites in the group, and other slave satellites confirm and synchronize their status.

[0194] In this plan, the priority of the first and second campaign stars is determined in four situations:

[0195] First, the orbit error of the first candidate satellite is smaller than the orbit error of the second candidate satellite, and the clock error of the first candidate satellite is smaller than the clock error of the second candidate satellite;

[0196] Second, the orbit error of the first candidate satellite is smaller than the orbit error of the second candidate satellite, and the clock error of the second candidate satellite is smaller than the clock error of the first candidate satellite;

[0197] Third, the orbit error of the second candidate satellite is smaller than that of the first candidate satellite, and the clock error of the first candidate satellite is smaller than that of the second candidate satellite;

[0198] Fourth, the orbit error of the second candidate satellite is smaller than that of the first candidate satellite, and the clock error of the second candidate satellite is smaller than that of the first candidate satellite;

[0199] When the first, second, and third conditions are met, the first campaign star is considered to have a higher priority; when the fourth condition is met, the second campaign star is considered to have a higher priority.

[0200] In step S5, all the primary stars establish a link observation with each other, and use a distributed data processing method to calculate the optimal estimate of the state of each primary star and the optimal estimate of the clock error and clock drift.

[0201] In one embodiment of the present invention, a method for calculating the optimal estimated value of the state of each primary star using a distributed data processing method includes:

[0202] S51, the extended Kalman filter state transfer equation is:

[0203] x k+1 =f(x k ,p,t)+w

[0204] Among them, x k+1 is the true state vector of the primary star at time k+1; x k is the true state vector of the primary star at time k; p is the covariance matrix; t is the time of primary star motion; w is the system process noise; f(·) is the nonlinear function;

[0205] S52, the time update calculation formula of the extended Kalman filter is:

[0206]

[0207] in, is the estimated value of the primary star state predicted at time k+1; is the current state vector of the primary star at time k; Φ is the state transfer matrix, Find the partial derivative of the f(·) function with respect to the true state vector of the primary star at time k, is the k+1 time prediction error covariance matrix, is the error covariance matrix at time k, Φ T is the transpose of the state transfer matrix, Q is the system process noise covariance matrix;

[0208] S53. Extend the Kalman filter observation update calculation formula to obtain the optimal estimated value of the state of each primary star:

[0209]

[0210] in, is the optimal estimated value of the state of the primary star at time k+1, H a is the observation matrix of the extended Kalman filter, H a T For H a Find the transpose; R is the observation noise covariance matrix; is the inter-satellite distance residual Δρ of all the primary stars that can be linked in the current epoch ij The vector formed, is the optimal estimation error covariance matrix at time k+1.

[0211] When implemented, the residual error Δρ of the priority star spacing of this scheme is ij The methods for obtaining include:

[0212] S531. Construct the intersatellite two-way observation equation between primary stars i and j as follows:

[0213]

[0214] Among them, ρ ij , ρ ji is the pseudo-range observation quantity of primary star i receiving primary star j and primary star j receiving primary star i; c is the speed of light; t i , t j are the clock errors of primary stars i and j respectively; ε ij , ε ij is the observation noise from primary star i to primary star j and from primary star j to primary star i; is the theoretical interstellar distance between the main stars i and j, which is expressed as:

[0215]

[0216] Among them, x i 、x j are the position components of the primary stars i and j in the x-axis direction; y i 、y j are the position components of the primary stars i and j in the y-axis direction respectively; zi 、z j are the position components of the primary stars i and j in the z-axis direction respectively;

[0217] S532. Use the reference orbit linearization to obtain the linear observation equation without satellite clock error:

[0218]

[0219] Where ρ is the linear observation without satellite clock error; is the approximate satellite-to-ground distance calculated using the reference orbit; are the correction values ​​of the positions of primary stars i and j relative to the reference orbit; L m is the star spacing unit vector, which is in the form of ε′ is the observation noise;

[0220] S533. Based on the linear observation equation without satellite clock error, the Kalman filter observation equation is obtained as follows:

[0221]

[0222] Where Δρ ij is the residual error of the inter-satellite distance.

[0223] Methods for calculating the optimal estimated clock error and clock drift of each primary star using distributed data processing methods include:

[0224] B1. Construct a linear model containing two states to represent the satellite clock error:

[0225]

[0226] Among them, b k d k is the clock error and clock drift of the primary star at time k; b k+1 d k+1 is the clock error and clock drift of the primary star at time k+1; dt is the sampling interval; ω b 、ω d They are white FM noise and random walk FM noise respectively;

[0227] B2. The time update calculation formula of the extended Kalman filter is:

[0228]

[0229] in, is the satellite clock estimate predicted at time k, including clock error and clock drift; φ k,k―1 The state transition matrix solved for the clock parameters; φ T k,k―1 For φ k,k―1 Find the transpose; is the satellite clock parameter at time k-1; is the clock error covariance matrix predicted at time k; is the error covariance matrix at time k-1; Q k―1 is the covariance matrix of white FM noise and random walk FM noise at time k-1;

[0230] B3. The observation update calculation formula of the extended Kalman filter is:

[0231]

[0232] Among them, K k is the Kalman gain matrix at time k, is the observation matrix, For Find the transpose; R k is the clock error covariance matrix at time k; (·) -1 To find the inverse operation; is the optimal estimate of the satellite clock error and clock drift at time k; Z k―1 is the actual measurement value of the satellite clock error and clock drift at time k-1; is the optimal estimation error covariance matrix at time k, and I is the identity matrix.

[0233] In summary, this solution can achieve high-precision satellite constellation spatial position synchronization and time information synchronization by grouping heterogeneous giant constellation systems and then combining local concentration with full-network distributed space-time synchronization.

Claims

1. A method for spatiotemporal synchronization of a heterogeneous giant star system, characterized in that: Including steps: S1. Divide all satellites in the heterogeneous Giant Star constellation into several groups based on the principle of grouping a preset number of adjacent satellites. In each group, one satellite is randomly selected as the master satellite, and the remaining satellites are slave satellites. S2. In each group, the slave satellites establish a link between each other and transmit the calculated distance observations and time synchronization observations to the master satellite through the intersatellite link; S3. Each master satellite calculates the position, velocity, clock error, frequency offset, and frequency drift of each slave satellite using a centralized data processing method based on the distance observations and time synchronization observations received from all slave satellites, and feeds this back to the slave satellites via the intersatellite link. S4. During a synchronization cycle, if a slave satellite fails to receive feedback from the master satellite, the master satellite selection mechanism is triggered, and the slave satellite with the highest priority is selected as the master satellite to achieve dynamic master node transfer; S5. All primary stars establish a link observation with each other and use a distributed data processing method to calculate the optimal estimate of the state of each primary star and the optimal estimate of the clock error and clock drift; The method for calculating the position and velocity of each slave satellite using a centralized data processing method in step S3 includes: S31. Based on the prior orbit and dynamic parameters, the satellite dynamic equation and its corresponding variational equation are integrated using a numerical integration method to obtain the reference orbit and state transfer matrix of the preset epoch; S32. Linearize the satellite distance observation equation using the reference orbit to obtain the linearized observation equation: in, is the instantaneous star distance after preprocessing; is the instantaneous satellite distance calculated using the reference orbit; is the intersatellite distance between satellites A and B; and are the state vectors of satellites A and B, respectively, which include position and velocity; and are the initial position vector, initial velocity vector and dynamic model parameters of satellite A and satellite B; 、 and They are Correction value of 、 and They are Correction value of are the intersatellite ranging bias corrections for A and B respectively; is the observation noise; 、 and are the estimated parameter corrections of satellites 1, 2 and n respectively; S33. Based on the linearized observation equation, combine the distance observations between all two slave stars in each group to establish a centralized processing parameter estimation equation: ， Where Y is the pseudorange difference observation vector; 、 and are the pseudorange differential observations between satellites 1 and 2, satellites 1 and i, and satellites j and n, respectively; T is the transpose; H is the inter-satellite pseudorange differential observation matrix, which has 2n columns and n(n-1) / 2 rows. Each row in H corresponds to an observation equation, and the 1 and -1 in H are the coefficients of the clock error term. 、 、 and The inter-satellite pseudoranges are from the 1st satellite to the 2nd satellite, from the 1st satellite to the i-th satellite, from the j-th satellite to the n-th satellite, and from the i-th satellite to the j-th satellite respectively; is the partial derivative; X is the parameter matrix to be estimated; 、 、 and are the parameters to be estimated for satellites 1, 2, i, and n, including the position and velocity of the satellites; 、 and are the clock corrections of satellites 1, 2 and n respectively; is the position vector of satellite i at the observation time; n is the total number of satellites; S34. Combine all the distance observations between each other at different times and use the least squares estimation strategy to obtain the parameter to be estimated X: Among them, P is the weight matrix in the least squares estimation formula.

2. The method for spatiotemporal synchronization of a heterogeneous giant star system according to claim 1, characterized in that: Step S2 further comprises: S21. From the mutual link observation between satellites, we can obtain the two-way inter-satellite ranging observation equation: in, They are the intersatellite observation quantities between satellites AB and BA respectively; are the nominal launch times of satellite A and B signals respectively; The inter-satellite distances between satellites AB and BA are the time intervals of transmission and reception between satellites; are the signal propagation delays between satellites AB and BA respectively; are the clock errors of satellites A and B, are the receiving and transmitting delays of satellite A respectively; are the receiving and transmitting delays of satellite B respectively; are the observation corrections between satellites AB and BA, including antenna phase center correction, relativistic correction and satellite-ground troposphere correction; are the measurement noise between satellites AB and BA respectively; S22. Preprocess the two-way inter-satellite ranging observation equation to obtain the inter-satellite distance observation equation and the inter-satellite time synchronization observation equation: in, and are distance observation and time synchronization observation respectively; After preprocessing, the satellites AB and BA are The instantaneous pseudo-range observation at time; for Theoretical value of the time-star distance; are the instantaneous clock differences of satellites A and B respectively; are the residual errors between satellites AB and BA respectively.

3. The method for spatiotemporal synchronization of a heterogeneous giant star system according to claim 2, characterized in that: The method for calculating the clock error, frequency offset, and frequency drift of each slave satellite using a centralized data processing method in step S3 includes: A1. Based on the inter-satellite time synchronization observation equation and the satellite reception and transmission delay, the observation equation for estimating the satellite clock error, frequency offset and frequency drift is constructed: in, is the estimated value of the intersatellite time synchronization observation; and 、 and and and are the clock error, frequency offset and frequency drift of satellite A and satellite B respectively; is the observation time after epoch normalization; is the reference epoch; A2. Based on the observation equations for estimating satellite clock error, frequency bias, and frequency drift, a combined observation equation is constructed: ， in, is the time-synchronized observation vector; 、 、 、 are the observed values ​​of clock error, frequency offset and frequency drift information between satellites 1 and 2, satellites 1 and i, satellites j and n, and satellites n-1 and n, respectively; T is the transpose; is the design matrix of the observation equation, is the time difference, The 1 or -1 in represents the coefficient of the clock error term; is the combined matrix of clock error, frequency offset and frequency drift of all slave satellites in each group; 、 、 and are the clock errors of satellites 1, 2, n-1 and n respectively; 、 、 and are the frequency deviations of satellites 1, 2, n-1 and n respectively; 、 、 and are the frequency drifts of satellites 1, 2, n-1 and n respectively; A3. Use the clock error constraint method to construct the observation vector to eliminate Virtual observation equation for medium rank deficiency: in, are the prior values ​​of the clock error of satellite j; Both are the accuracy of satellite j; For The clock parameters of the j-th satellite extracted from ; A4. Combined observation vector And the virtual observation equation, we get the combined relationship: in, is the combined clock information observation; is the combined observation matrix; is the residual error after combination; is a row vector; A5. Use the least squares estimation strategy to find the optimal estimate of the combined relationship: Among them, the optimal estimate Contains the clock error, frequency deviation and frequency drift of each slave satellite.

4. The method for spatiotemporal synchronization of a heterogeneous giant star system according to claim 1, characterized in that: The method for calculating the optimal estimated value of the state of each primary star using a distributed data processing method includes: S51, the extended Kalman filter state transfer equation is: in, is the true state vector of the primary star at time k+1; is the true state vector of the primary star at time k; is the covariance matrix; The time of the main star's movement; is the system process noise; is a nonlinear function; S52, the time update calculation formula of the extended Kalman filter is: ， in, is the estimated value of the primary star state predicted at time k+1; is the current state vector of the primary star at time k; is the state transition matrix, for The function calculates the partial derivative of the true state vector of the primary star at time k, is the k+1 time prediction error covariance matrix, is the error covariance matrix at time k, To take the transpose of the state transfer matrix, is the system process noise covariance matrix; S53. Extend the Kalman filter observation update calculation formula to obtain the optimal estimated value of the state of each primary star: in, is the optimal estimated value of the state of the primary star at time k+1, is the observation matrix of the extended Kalman filter, For Find the transpose; is the observation noise covariance matrix; is the inter-satellite distance residual of all the primary stars that can be linked in the current epoch The vector formed, is the optimal estimation error covariance matrix at time k+1.

5. The method for spatiotemporal synchronization of a heterogeneous giant star system according to claim 4, characterized in that: Star distance residual The methods for obtaining include: S531. Construct the intersatellite two-way observation equation between primary stars i and j as follows: in, 、 The pseudo-range observations of primary star i receiving primary star j and primary star j receiving primary star i; c is the speed of light; 、 are the clock errors of primary stars i and j respectively; 、 is the observation noise from primary star i to primary star j and from primary star j to primary star i; 、 is the theoretical interstellar distance between the main stars i and j, which is expressed as: in, 、 are the position components of the primary stars i and j in the x-axis direction respectively; 、 are the position components of the primary stars i and j in the y-axis direction respectively; 、 are the position components of the primary stars i and j in the z-axis direction respectively; S532. Use the reference orbit linearization to obtain the linear observation equation without satellite clock error: in, It is the linear observation without satellite clock error; is the approximate satellite-to-ground distance calculated using the reference orbit; 、 are the correction values ​​of the positions of primary stars i and j relative to the reference orbit; is the star spacing unit vector, which is in the form of ; is the observation noise; S533. Based on the linear observation equation without satellite clock error, the Kalman filter observation equation is obtained as follows: in, is the residual error of the inter-satellite distance.

6. The method for spatiotemporal synchronization of a heterogeneous giant star system according to claim 1, characterized in that: Methods for calculating the optimal estimated clock error and clock drift of each primary star using distributed data processing methods include: B1. Construct a linear model containing two states to represent the satellite clock error: in, 、 is the clock error and clock drift of the primary star at time k; 、 is the clock error and clock drift of the primary star at time k+1; is the sampling interval; 、 They are white FM noise and random walk FM noise respectively; B2. The time update calculation formula of the extended Kalman filter is: in, is the satellite clock estimate predicted at time k, including clock error and clock drift; State transition matrix solved for clock parameters; For Find the transpose; is the satellite clock parameter at time k-1; is the clock error covariance matrix predicted at time k; is the error covariance matrix at time k-1; is the covariance matrix of white FM noise and random walk FM noise at time k-1; B3. The observation update calculation formula of the extended Kalman filter is: ， in, is the Kalman gain matrix at time k, is the observation matrix, For Find the transpose; is the clock error covariance matrix at time k; To find the inverse operation; is the optimal estimate of the satellite's clock error and clock drift at time k; is the actual measurement value of the satellite clock error and clock drift at time k-1; is the optimal estimation error covariance matrix at time k, is the identity matrix.

7. The method for spatiotemporal synchronization of a heterogeneous giant star system according to claim 1, characterized in that: Trigger the master star selection mechanism and select the slave star with the highest priority as the master star. The methods for realizing dynamic master node transfer include: S41, modify each slave star in the group to a candidate state; S42. Each candidate slave satellite sends its own campaign information to other surrounding candidate slave satellites to form a campaign information set; the campaign information includes communication capability, computing capability, orbit error, and clock error; S43: The secondary star that initiated the election is designated as the first election star, any secondary star that received the election information is designated as the second election star, and the election information of the second election star is added to the election information set; S44. Based on the election information of the first and second election stars, the one with the higher priority is selected as the new first election star; S45: Determine whether there is a candidate slave star in the group that has not participated in the comparison. If so, proceed to step S46; otherwise, take the first candidate star selected most recently as the master star and proceed to step S47; S46: The first candidate star sends its own campaign information to other candidate slave stars in the surrounding area, and any slave star that receives the campaign information is selected as the second candidate star. The campaign information of the first candidate star and the second candidate star is stored in a new campaign information set, and the process returns to step S44. S47: The selected master satellite broadcasts the information of its successful selection to all slave satellites in the group, and other slave satellites confirm and synchronize their status.

8. The method for spatiotemporal synchronization of a heterogeneous giant star system according to claim 7, characterized in that: Step S44 further includes: There are four situations when determining the priority of the first and second campaign stars: First, the orbit error of the first candidate satellite is smaller than the orbit error of the second candidate satellite, and the clock error of the first candidate satellite is smaller than the clock error of the second candidate satellite; Second, the orbit error of the first candidate satellite is smaller than the orbit error of the second candidate satellite, and the clock error of the second candidate satellite is smaller than the clock error of the first candidate satellite; Third, the orbit error of the second candidate satellite is smaller than that of the first candidate satellite, and the clock error of the first candidate satellite is smaller than that of the second candidate satellite; Fourth, the orbit error of the second candidate satellite is smaller than that of the first candidate satellite, and the clock error of the second candidate satellite is smaller than that of the first candidate satellite; When the first, second, and third conditions are met, the first campaign star is considered to have a higher priority; when the fourth condition is met, the second campaign star is considered to have a higher priority.

9. The method for spatiotemporal synchronization of a heterogeneous giant star system according to any one of claims 1 to 8, characterized in that: The calculation method of the preset number includes: Starting from 1, the number of main stars is selected in ascending order, and the complexity of the selected number of main stars is calculated using the complexity model. The expression of the complexity model is: Where O is the complexity; N is the total number of satellites in the Giant Star constellation network; The number of main stars; When the difference between the complexities corresponding to two adjacent numbers of primary stars selected is greater than a preset threshold, the larger value of the two numbers of primary stars is selected as the preset number.

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