CN115276756B - A low-orbit satellite constellation optimization design method to ensure service quality - Google Patents

Abstract

The invention discloses a low orbit satellite constellation optimization design method for guaranteeing service quality, and belongs to the technical field of wireless communication. The method comprehensively considers the reliability, the effectiveness and the completeness of the satellite constellation and gives definition of the service quality of the satellite constellation. Introducing error rate, signal-to-noise ratio and survivability to represent the reliability of satellite constellation; the incoming coverage represents the validity of the satellite constellation; the completeness of the user in the satellite constellation is represented by the user matching degree. On the basis, a service quality threshold value and a calculated service quality value are set, the ratio of the sum of the system capacity of a target area and the constellation construction cost is set as an objective function, the objective function value is iteratively optimized by utilizing the global searching capability of a genetic algorithm to obtain an initial constellation solution, and the optimal constellation parameter is output by utilizing the local searching capability of a tabu searching algorithm to secondarily optimize the initial solution. The invention is oriented to regional users, and the high-efficiency and economical low-orbit satellite constellation is optimally designed according to the user demands and the service quality guarantee.

Description

An optimization design method for low-orbit satellite constellation to ensure service quality

Technical field

The invention belongs to the technical field of wireless communication. Specifically, it relates to a low-orbit satellite constellation optimization design method that guarantees service quality.

Background technique

Today's terrestrial networks have certain limitations. For example, base stations are mostly distributed in densely populated areas. Devices in sparsely populated mountainous areas, deserts, and oceans cannot access the Internet. At the same time, terrestrial base stations are susceptible to irresistible factors such as natural disasters and wars. of destruction. Satellite communications can well complement the shortcomings of terrestrial networks with its many advantages such as small ground restrictions, flexible networking, wide coverage, and high service quality.

Satellite constellation design is the basis for satellite communication design and networking. Satellite constellation design distributes multiple satellites with the same or similar types and functions in similar or complementary orbits, and collaborates to complete certain communication tasks under the management control of a centralized or distributed network. Since a single satellite cannot achieve real-time coverage of the designed target area, multiple satellites need to be used to form a satellite constellation to expand the coverage of satellite communications. A satellite constellation composed of multiple satellites forms a satellite network by constructing inter-satellite links and ground station feeder links, thereby achieving global interconnection and interoperability. Satellite constellation design is the prerequisite for satellite network deployment and operation, which determines the operation and application level of satellite networks. Traditional constellation scheme design methods mainly include two categories: geometric analysis method and simulation-based comparative analysis method. These two types of methods only optimize satellite coverage performance. However, if the constellation design ignores the user needs and service quality of the designed target area, it will lead to redundancy of constellation resources, resulting in an increase in constellation costs and a lack of constellation service capabilities.

CN107329146B, an optimized design method for navigation satellite low-orbit monitoring constellations, fully considers the existing technical foundation and future technology development trends, analyzes the design requirements and constraints of navigation satellite low-orbit monitoring constellations, and selects the Walker-δ constellation and solar Synchronously return to the orbit, and at the same time construct evaluation criteria including monitoring station coverage factors, performance factors and constellation orbit parameters. The optimally designed navigation satellite low orbit monitoring constellation has better monitoring performance; according to the navigation satellite low orbit proposed by the present invention The monitoring constellation optimization design method can effectively realize the optimal design of the navigation satellite low-orbit monitoring constellation. The technical solution is scientific, optimized and highly achievable; the designed constellation can achieve a larger monitoring station coverage factor with a smaller total number of satellites. and performance factors.

This invention establishes a multi-objective optimization model for the middle and low latitudes of the northern and southern hemispheres. The optimization objectives include the minimum coverage factor and the monitoring station coverage performance factor. This optimization goal only considers the coverage characteristics of the satellite to the earth. Optimization goals that are too single can easily lead to a waste of satellite resources. At the same time, the invention uses traditional simulation and mathematical analysis to optimize the design of constellation parameters to determine the final constellation parameters, and does not use the intelligent optimization algorithm that is currently mainstream in research. As a result, it is easy to cause problems such as excessive workload, limited experience of researchers, and the final solved constellation is not the optimal constellation. The present invention fully considers multiple service quality indicators, which is not limited to the coverage performance of the constellation, but also considers the constellation invulnerability, communication bit error rate, signal-to-noise ratio, and user capacity matching degree. By making the above indicators serve as constraints for the optimization model, the scope of the search space is limited. At the same time, two intelligent optimization algorithms, genetic algorithm and tabu search algorithm, are combined to solve the model, which greatly reduces the workload and the output constellation meets the service quality requirements.

Contents of the invention

The present invention aims to solve the above problems of the prior art. A low-orbit satellite constellation optimization design method that ensures service quality is proposed. The technical solution of the present invention is as follows:

A low-orbit satellite constellation optimization design method that ensures service quality includes the following steps:

S1. Use the equal longitude and latitude method in the grid point method to divide the target area that the low-orbit satellite constellation needs to cover into N _g areas of equal area;

S2. Determine the return period and orbital altitude of the constellation;

S3. Use the global search capability of the genetic algorithm, set the population size of the algorithm to 200 and the number of iterations to 20 generations to obtain a set of initial solutions to the Walker constellation;

S4. Set the thresholds for the service quality parameters of the low-orbit satellite constellation, including reliability, effectiveness, and constellation completion. Reliability includes: bit error rate, signal-to-noise ratio, and constellation invulnerability; effectiveness includes: The coverage of the target area by the constellation; the completion of the constellation includes the matching degree of the constellation to the user;

S5. Calculate the corresponding service quality value by combining the constellation STK simulation data and the corresponding formula;

S6. Determine whether the calculated service quality value meets the set threshold. If so, calculate the objective function value according to the constellation. If not, use the tabu search algorithm to update the optimized constellation parameter solution vector, and continue to return to step S4;

S7. Determine whether the maximum number of iterations is met. If so, enter the optimal optimization target value and corresponding constellation parameters. If not, return to step S4.

Further, in S1, the target area is divided into N _g areas of equal area using the grid point method and the medium longitude method, specifically including:

1) Select the latitude and longitude coordinates of the lower left corner of all grid points based on the target area.

2) Select the basic unit of the grid point and divide the target area. The basic units are horizontal and vertical span.

Further, step S2 determines the return period and orbital height of the constellation, specifically including:

The return period T _SAT of the satellite is determined based on the earth's rotation period T _e and the required number of return turns n of the low-orbit satellite. The formula is shown in Equation (1),

T _e represents the Earth's rotation period. Using the obtained satellite period, the altitude h of the low-orbit satellite can be calculated through equation (2);

Where _Re represents the radius of the Earth, G represents the gravitational constant, and _me represents the mass of the Earth.

Further, step S3 uses a genetic algorithm to initialize a better initial solution set. The genetic algorithm has crossover and mutation operations, which makes the next generation phenotype generated by a larger population size diverse; apply this to constellation design , a set of better initial constellation solutions can be generated; the solution vector formed by the parameters of the Walker constellation includes [N _SAT N _P iP _SAT A _SAT ], which respectively represent the number of single-orbit satellites, the number of constellation orbital planes, orbital inclination angle, satellite The transmitting power of the antenna and the equivalent area of the satellite antenna.

Further, setting the service quality threshold in step S4 is to set the thresholds for signal-to-noise ratio, bit error rate, invulnerability, coverage rate, and user matching degree. The specific symbols are expressed as

Further, the specific calculation method of each service quality indicator in step 5 is as follows:

(1) When the threshold BER of the bit error rate of the low-orbit satellite network is given, BER ₀ , the signal-to-noise ratio of the system is calculated by equation (3);

erfc(·) respectively represents the complementary error function, and E _b /N ₀ represents the signal-to-noise ratio of the system. E _b represents the average bit energy, and N ₀ represents the noise power spectral density.

(2) Invulnerability is quantified by citing the natural connectivity of complex networks, and periodic dynamic natural connectivity is used to quantify and optimize the invulnerability of the constellation, as shown in equation (4);

Among them, A _T (G), T _SAT , N _T , _Ti , and P(·) respectively represent the connection probability matrix, the natural connectivity of periodic dynamics, the satellite return period, the number of time slices divided into satellite periods, the length of each time slice, and the corresponding matrix. natural connectivity. The elements in A _T (G) represent the probability that two nodes remain connected during the dynamic topology period in the satellite network,/> is the i-th characteristic root of A _T (G). Within a regression period, the connection probability of any two nodes of the constellation can be obtained by obtaining the STK inter-satellite link establishment data.

(3) During the constellation regression period, the coverage CV of the target area by the constellation is the weighted statistics of the coverage of all grid points in the target area by the satellite constellation. Its specific calculation formula is shown in Equation (5).

Where N _g is the number of ground grid points, L is the number of divided time slots. If the constellation covers grid point i at time t, then y _it = 1, otherwise y _it = 0. The above calculation is obtained by passing in the constellation parameters and It is calculated from the coverage data of the constellation on the ground grid points in STK.

Further, (4) user matching degree S in step S5 is defined as the capacity requirement of satellite resources for users in different grid point areas on the ground in time slots 0, 1, 2, ..., L-1. Matching degree, its value is between 0 and 1. The greater the user matching degree, the better the satellite constellation matches the user needs in different areas on the ground. The calculation steps are as follows;

Put the signal-to-noise ratio into equation (6) to calculate the downlink rate R of a single satellite;

P _SAT represents the transmit power of the satellite, G _SAT represents the satellite antenna gain, G _r the user's antenna gain, L _f represents the path loss, L _M represents the link margin, T represents the noise temperature of the system and K represents Boltzmann's constant. , the gain of the antenna is calculated by equation (7);

Among them, eta _SAT represents the efficiency of each antenna, A _SAT represents the equivalent area of the antenna, f represents the operating frequency of the system, and c represents the speed of light;

The capacity of a single satellite, that is, the number of users it can serve, is expressed as Equation (8);

R is the downlink data rate of a single satellite, η _MAE is the efficiency of multiple access modulation of the satellite antenna, R _user is the user's data rate, and its value is 1.554Mbps according to the T1 service standard set by the ITU;

Therefore, the user matching degree is calculated by Equation (9);

Among them, N _g is the number of grid points divided on the ground, STF _tn is the capacity matching situation of the nth grid point in the t time slot; in any time slot t (t=0,1,2,... ., L-1), if the capacity of the satellite constellation to grid point N provides a capacity greater than or equal to the total satellite communication population of the grid point, STF _tn = 1, otherwise, STF _tn = 0, as shown in Equation (10) shown;

D(n) represents the number of satellite communication users at a ground grid point, the number of people who can pass through this grid point, N(n), and the proportion of communication users. And the proportion of satellite users/> Calculation of the product, that is, the number of satellite communication users at the nth grid point is C _t (n) is the capacity provided by the number of visible satellites in grid point n in time slot t, calculated based on the single satellite capacity and the number of visible satellites at ground grid points obtained from STK.

Further, the calculation method of the objective function value in step S6 is as follows.

Define the set of satellites that the satellite constellation provides services to grid point n in time slot t as S _t ={m|θ _nm ≥θ _min }, θ _nm is the elevation angle of grid point n to satellite m, and θ _min is the The lowest elevation angle for good communication conditions, then C _t (n) in equation (10) is expressed as shown in equation (11);

Therefore, during the entire cycle, the capacity provided by the satellite constellation for the target area is the sum of the capacity provided in each time slot;

Then the objective function value-the cost-effectiveness ratio of the network is expressed as the ratio of the sum of the above capacities and the cost of building the network, as shown in Equation (13);

Further, the step S6 of using the tabu search algorithm to optimize the constellation parameters refers to:

1) Obtain the output solution of the genetic algorithm as the current solution of the tabu search algorithm and set the service quality threshold;

2) Determine whether the optimization goal remains unchanged, if so, output the result; if not, proceed to the next step;

3) Perform neighborhood operations on the current solution to generate neighborhood solutions, and determine candidate solutions from the neighborhood based on service quality constraints and objective function values Ψ;

4) Determine whether the candidate solution satisfies the contempt principle. If so, replace the current solution with the best state solution based on the contempt principle criterion; and replace the earliest object that enters the taboo list with the best state solution;

5) Determine the taboo status of each object corresponding to the candidate solution, select the best status corresponding to the non-taboo object in the candidate solution set as the current new solution, and replace the earliest taboo object that enters the taboo list with the corresponding taboo object;

6) Determine whether the optimization target value in the algorithm changes. If so, end the algorithm and output the optimized constellation parameters [N _SAT N _Pi P _SAT A _SAT ] and the maximum objective function value Ψ. Otherwise, go to step 3).

The advantages and beneficial effects of the present invention are as follows:

The present invention proposes a low-orbit satellite constellation optimization design method that guarantees service quality, and defines the service quality indicators of low-orbit satellite constellation design as reliability, effectiveness and completion. Reliability means that this constellation provides users with small errors and high-quality communication performance. Specific quantitative indicators include bit error rate, signal-to-noise ratio, and the invulnerability of the constellation. Effectiveness refers to the ability of a network composed of low-orbit satellite constellations to provide services to all users in the target area. The specific quantitative indicator is the coverage of the constellation. Completeness refers to the matching of the network to the needs of users in different regions. The specific indicator is user matching. In the service quality constraint, within a satellite return period, the user matching degree compares the actual satellite communication population at the grid point in the target area with the number of users that the visible satellite at the grid point can accommodate. Through the relationship between the two and the capacity of a single satellite, Its calculation formula is defined, which is a unique innovation of the present invention. The main innovation of this invention is to use satellite simulation software STK to set service quality constraints according to actual needs, and combine the algorithm to optimize the cost-effectiveness ratio of the constellation to design a low-cost constellation with high resource utilization. In the existing research, the constellation optimization design considers the regional coverage performance, and few of them combine the satellite-ground user link and the inter-satellite link to establish optimization constraint indicators for optimization design. The constellation optimization design of the present invention simultaneously considers the inter-satellite and satellite-ground characteristics of the low-orbit satellite constellation, and designs service quality indicators accordingly. Therefore, the service quality constraint design system proposed by the present invention is not easy for those skilled in the art to think of. The present invention introduces the natural connectivity of the invulnerability index in the complex network by considering the inter-satellite link establishment during the satellite return period. In order to adapt to the dynamics of satellite networks, a periodic dynamic natural connectivity index is designed to represent the invulnerability index of satellite constellations. At the same time, by considering the satellite-ground user link, the bit error rate, signal-to-noise ratio and user matching degree are introduced, and the above indicators are used to establish the connection between the constellation and the needs of ground users. Therefore, the present invention fully considers the inherent invulnerability of the constellation and the matching of resources between satellite and ground users, and the designed constellation has a high cost performance. In terms of algorithm, in order to avoid the limitations of a single algorithm in traditional constellation optimization design, this method combines the global search ability of the genetic algorithm and the local search ability of the tabu search algorithm to achieve the goal of not falling into local solutions and solving the constellation parameter solution space. Adequate search purposes. From this, the optimal low-orbit satellite constellation that meets the service quality indicators is designed.

Description of the drawings

Figure 1 is an overall flow chart of a preferred embodiment of the present invention;

Figure 2 is the genetic algorithm initialization constellation flow chart;

Figure 3 is a schematic diagram of the secondary optimization of the tabu search algorithm.

Detailed ways

The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the accompanying drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

The technical solution of the present invention to solve the above technical problems is:

An optimization design scheme for low-orbit satellite constellations that guarantees service quality. The optimal satellite constellation is obtained by setting service quality constraints and combining genetic algorithms and tabu search algorithms. The service quality indicators of the constellation include reliability, effectiveness and completion. Specific quantitative indicators are bit error rate, signal-to-noise ratio, invulnerability, coverage, and user matching. The above indicators are closely related to the constellation performance, so setting the constraints of the above indicators means placing certain constraints on the solution space of the constellation parameters. By designing the constraints of the above indicators in the algorithm program, combined with the optimization goal, which is the maximum cost-effectiveness ratio of the constellation, iterative solution, and finally output the satellite constellation parameters with the maximum cost-effectiveness ratio that satisfies the service quality constraints.

Specific steps are as follows:

Step 1: When we set the target number of regions, we use STK software to automatically divide China into n regions (the specific number of divisions is based on the selected longitude and latitude intervals, usually 3°). Used to calculate the population of each grid point based on the population distribution map.

Step 2: Based on the target area, determine the basic constellation configuration as the Walker constellation, determine the return period T _S of each satellite in the satellite constellation, and thereby determine the constellation satellite orbit height h. After the orbit height is determined, the parameters that need to be optimized for the Walker constellation are the number of single-orbit satellites, the number of satellite orbits, the orbital inclination, the single-star antenna transmit power, and the effective area of the antenna. The symbol is expressed as [N _SAT N _P i P _SAT A _SAT ] .

The third step: Use the genetic algorithm to initialize a set of globally optimal solutions to avoid the search target falling into the local optimal solution.

Step 4: Set the threshold of service quality indicators, including signal-to-noise ratio, bit error rate, invulnerability, coverage rate, and user matching degree. Its symbol is expressed as The corresponding service quality value is calculated based on the constellation performance data composed of optimized parameters simulated in the STK software and the service quality index calculation formula.

Step 5: The tabu search algorithm seeks the optimal solution from the field of solution vectors. Therefore, the initial solution set is optimized twice through the local search capability of the tabu search algorithm. In the algorithm, the calculation method described in the fourth step is used to determine whether the service quality threshold is met. Through loop iteration, when the optimization goal Ψ is satisfied and remains unchanged, the optimal constellation parameter configuration and the maximum optimization goal are output.

Preferably, in the second step, the calculation method for determining the satellite period and orbit is as follows:

The return period T _SAT of the satellite is determined based on the earth's rotation period _Te and the required number of return turns n of the low-orbit satellite, and its formula is shown in equation (1).

Using the obtained satellite period, the height h of the low-orbit satellite can be calculated through equation (2).

Preferably, in the third step, a genetic algorithm is used to initialize a set of globally optimal solutions. Genetic algorithms have crossover and mutation operations, which enable diversity in the phenotypes of the next generation produced by larger population sizes. Applying this to constellation design can produce a better set of initial constellation solutions. The solution vector formed by the parameters of the Walker constellation includes [N _SAT N _P i P _SAT A _SAT ], which respectively represent the number of single-orbit satellites, the number of constellation orbit planes, orbital inclination, the transmission power of the satellite antenna, and the equivalent area of the satellite antenna.

The specific steps to initialize Walker constellation parameters are:

1) Set the population size to 200 and the number of iterations to 20 generations.

2) Calculate the constellation optimization objective function value Ψ.

3) Determine the termination conditions.

4) If yes, generate an initial constellation parameter solution set. If not, perform selection, crossover, and mutation operations, and return to the second step. (The crossover probability is set to 0.8 and the mutation probability is 0.1).

The specific process is shown in Figure 2.

Preferably, in the fourth step, when the optimization algorithm calculates the three service quality indicators of invulnerability, coverage, and user matching, it is necessary to pass in the constellation parameters in the optimization to STK, and then obtain them respectively during the satellite return period. Inter-satellite link establishment data, satellite coverage data, and ground grid visibility data to the constellation.

Preferably, in the fourth step, the signal-to-noise ratio, bit error rate, invulnerability, coverage rate, and user matching degree are respectively defined and calculated. Includes the following calculation formulas:

1) When the threshold BER ₀ of the bit error rate of the low-orbit satellite network is given, the signal-to-noise ratio SNR of the system can be calculated by equation (3).

2) Invulnerability is quantified by citing the natural connectivity of complex networks. The indicator natural connectivity in complex networks has strictly monotonic properties. It represents the sum of the number of closed loops for each node in the network and can measure the redundancy of the network. Natural connectivity can be used to measure the redundancy of alternative paths in a network. Its formula is shown in formula (4).

Among them, λ _i is the i-th characteristic root of the adjacency matrix A(G) of the graph G(V,E). Therefore, the natural connectivity of a network is the characteristic spectrum of the adjacency matrix of the network and then takes the natural logarithm of average value. However, due to the rapid dynamic changes in low-orbit satellite network topology, the natural connectivity of static networks is not suitable for satellite networks.

Periodic dynamic natural connectivity is used to quantify and optimize the invulnerability of the constellation. As shown in formula (5).

Among them, A _T (G) represents the connection probability matrix. The elements in A _T (G) represent the probability that two nodes remain connected during the dynamic topology period in the satellite network. Within a regression period, the connection probability of any two nodes of the constellation can be calculated by obtaining the STK inter-satellite link establishment data.

3) During the constellation return period, the coverage rate of the target area by the constellation is the weighted statistics of the coverage of all grid points in the target area by the satellite constellation. Its specific calculation formula is shown in Equation (6).

Among them, N _g is the number of ground grid points, and L is the number of divided time slots. If the constellation covers grid point i at time t, then y _it =1, otherwise y _it =0. The above calculation is calculated by passing in the constellation parameters and obtaining the coverage data of the constellation on the ground grid points in STK.

4) Divide the single-star return period T _S into different small time slots ΔT. The number of divided time slots L can be obtained by the ratio of the single-star return period T _S and ΔT. In each time slot, the position of the satellite can be considered unchanged. The user matching degree is defined as the matching degree of satellite resources to the capacity requirements of users in different grid point areas on the ground in time slots 0, 1, 2,..., L-1. Its value is between 0 and 1. The greater the user matching degree, the better the satellite constellation matches the needs of users in different areas on the ground. The calculation steps are as follows:

First, the signal-to-noise ratio is put into equation (7) to calculate the downlink rate R of a single satellite.

P _SAT represents the transmit power of the satellite, G _SAT represents the satellite antenna gain, G _r the user's antenna gain, L _f represents various transmission losses, L _M represents the link margin, T represents the noise temperature of the system and K represents Boltz Mann constant. The gain of the antenna is calculated by equation (8).

Among them, eta _SAT represents the efficiency of each antenna, A _SAT represents the equivalent area of the antenna, f represents the operating frequency of the system, and c represents the speed of light.

Secondly, the single satellite's downlink data rate R, the user's data rate R _user (the value is 1.554Mbps according to the T1 service standard set by the ITU.) and the multiple access modulation efficiency η _MAE of the satellite antenna are used to calculate the single satellite's capacity. The capacity of a single star is the number of users that can be served, expressed as equation (9).

Finally, the user matching degree is calculated by Equation (10).

Among them, N _g is the number of grid points divided on the ground, and STF _tn is the capacity matching situation of the n-th grid point in the t-th time slot. In any time slot t (t=0, 1, 2, ...., L-1), if the capacity of the satellite constellation to grid point n provides a total satellite communication population greater than or equal to the grid point, STF _tn =1, otherwise, STF _tn =0. As shown in formula (11).

D(n) represents the number of satellite communication users at a ground grid point, the number of people who can pass through this grid point, N(n), and the proportion of communication users. And the proportion of satellite users/> Calculation of the product, that is, the number of satellite communication users at the nth grid point is C _t (n) is the capacity provided by the number of visible satellites in grid point n in time slot t. It can be calculated based on the single satellite capacity and the number of visible satellites at ground grid points obtained from STK.

Preferably, the fourth step uses a tabu search algorithm to perform local optimization, which is characterized in that the solution optimized by the genetic algorithm is used as the initial solution input to the tabu search algorithm, and the local search capability of the neighborhood operation of the tabu search algorithm is used for further optimization. Local quadratic optimization. The specific steps are as follows:

7) Obtain the output solution of the genetic algorithm as the current solution of the tabu search algorithm and set the service quality threshold.

8) Determine whether the optimization goal remains unchanged, and if so, output the result. If not, go to the next step.

9) Perform neighborhood operations on the current solution to generate neighborhood solutions, and determine candidate solutions from the neighborhood based on service quality constraints and objective function values Ψ.

10) Determine whether the candidate solution satisfies the contempt principle. If so, replace the current solution with the best state solution based on the contempt principle criterion. And replace the earliest object that enters the tabu list with the best solution.

11) Determine the taboo status of each object corresponding to the candidate solution, select the best status corresponding to the non-taboo object in the candidate solution set as the current new solution, and replace the earliest taboo object that enters the taboo list with the corresponding taboo object.

12) Determine whether the optimization target value in the algorithm has changed. If so, end the algorithm and output the optimized constellation parameters [N _SAT N _P i P _SAT A _SAT ] and the maximum objective function value Ψ. Otherwise, go to step 3)

The specific process is shown in Figure 3.

The concepts and models involved in the content of this invention are as follows:

1. Network model

The main scenario of the present invention is constellation coverage for users in China who are willing to join satellite communications. The space segment consists of a low-orbit satellite broadband network composed of low-orbit satellites, which can provide users with a data rate of at least 1.554Mbps. The ground segment consists of ground users and ground broadband networks. Satellite broadband networks can well make up for the lack of coverage of terrestrial networks in remote areas and extreme environmental areas, and can also provide communication services for communication interruptions caused by natural disasters at base stations. Since the population in China is extremely uneven, if the distribution characteristics of users are not considered in the constellation design, it will cause a waste of satellite resources and increase the design cost of the satellite constellation. This model is based on the actual low-orbit satellite broadband network service quality settings and user distribution in ground grid points to design a low-orbit satellite constellation that guarantees user needs and service quality.

2. The technical solution of the present invention is as follows:

The present invention proposes a low-orbit satellite constellation optimization design method that ensures service quality. First, a system model including LEO satellite constellation and ground users was established. Then, a low-orbit satellite constellation optimization design method that ensures service quality includes constellation design from three aspects: constellation reliability, effectiveness, and completeness. Among them, reliability takes into account the bit error rate, signal-to-noise ratio and invulnerability. Effectiveness takes constellation coverage into account. Completeness takes into account user fit. By setting the thresholds of the above indicators and defining their calculation methods, service quality constraints are established to limit the optimal solution space of the constellation. Finally, the genetic algorithm is used to initialize the constellation parameters and the tabu search algorithm is used for secondary optimization to output the optimal constellation parameters and the maximum objective function value Ψ.

It should also be noted that the terms "comprises," "comprises," or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements not only includes those elements, but also includes Other elements are not expressly listed or are inherent to the process, method, article or equipment. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article, or device that includes the stated element.

It should be understood that the above embodiments are only used to illustrate the present invention and are not intended to limit the protection scope of the present invention. After reading the description of the present invention, skilled persons can make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims (8)

1. A low-orbit satellite constellation optimization design method that ensures service quality, which is characterized by including the following steps: S1. Use the equal longitude and latitude method in the grid point method to divide the target area that the low-orbit satellite constellation needs to cover into N _g areas of equal area; S2. Determine the return period and orbital altitude of the constellation; S3. Use the global search capability of the genetic algorithm, set the population size of the algorithm to 200 and the number of iterations to 20 generations to obtain a set of initial solutions to the Walker constellation; S4. Set the thresholds for the service quality parameters of the low-orbit satellite constellation, including reliability, effectiveness, and constellation completion. Reliability includes: bit error rate, signal-to-noise ratio, and constellation invulnerability; effectiveness includes: The coverage of the target area by the constellation; the completion of the constellation includes the matching degree of the constellation to the user; S5. Calculate the corresponding service quality value by combining the constellation STK simulation data and the corresponding formula; S6. Determine whether the calculated service quality value meets the set threshold. If so, calculate the objective function value according to the constellation. If not, use the tabu search algorithm to update the optimized constellation parameter solution vector, and continue to return to step S4; S7. Determine whether the maximum number of iterations is met. If so, enter the optimal optimization target value and corresponding constellation parameters. If not, return to step S4; The specific calculation method of each service quality indicator in step S5 is as follows: (1) When the threshold BER of the bit error rate of the low-orbit satellite network is given, BER ₀ , the signal-to-noise ratio of the system is calculated by equation (3); erfc(·) represents the complementary error function respectively, E _b /N ₀ represents the signal-to-noise ratio of the system; E _b represents the average bit energy, and N ₀ represents the noise power spectral density; (2) Invulnerability is quantified by citing the natural connectivity of complex networks, and periodic dynamic natural connectivity is used to quantify and optimize the invulnerability of the constellation, as shown in equation (4); Among them, A _T (G), T _SAT , N _T , _Ti , and P(·) respectively represent the connection probability matrix, the natural connectivity of periodic dynamics, the satellite return period, the number of time slices divided into satellite cycles, the length of each time slice, and the natural connectivity Degree, the elements in A _T (G) represent the probability that two nodes remain connected during the dynamic topology period in the satellite network,/> is the i-th characteristic root of A _T (G). Within a regression period, the connection probability of any two nodes of the constellation can be obtained by obtaining the STK inter-satellite link construction data; (3) During the constellation regression period, the coverage CV of the target area by the constellation is the weighted statistics of the coverage of all grid points in the target area by the satellite constellation. Its specific calculation formula is as shown in Equation (5); Where N _g is the number of ground grid points, L is the number of divided time slots. If the constellation covers grid point i at time t, then y _it = 1, otherwise y _it = 0. The above calculation is obtained by passing in the constellation parameters and It is calculated from the coverage data of the constellation on the ground grid points in STK. 2. A low-orbit satellite constellation optimization design method that guarantees service quality according to claim 1, characterized in that, in S1, the target area is divided into n equal areas by using the equal longitude and latitude method in the grid point method. areas, specifically including: 1) Select the latitude and longitude coordinates of the lower left corner of all grid points according to the target area; 2) Select the basic unit of the grid point and divide the target area. The basic unit is the horizontal and vertical span. 3. A low-orbit satellite constellation optimization design method that guarantees service quality according to claim 1, characterized in that step S2 determines the return period and orbital height of the constellation, specifically including: The return period T _SAT of the satellite is determined based on the earth's rotation period T _e and the required number of return turns n of the low-orbit satellite. The formula is shown in Equation (1), T _e represents the rotation period of the earth; using the obtained satellite period, the height h of the low-orbit satellite can be calculated through equation (2); Where _Re represents the radius of the Earth, G represents the gravitational constant, and _me represents the mass of the Earth. 4. A low-orbit satellite constellation optimization design method that guarantees service quality according to claim 1, characterized in that step S3 uses a genetic algorithm to initialize a set of better initial solutions, and the genetic algorithm has crossover and mutation. Operation, which makes the next generation phenotype generated by the population size diverse; applying this to constellation design can produce a better set of initial constellation solutions; the solution vector formed by the parameters of the Walker constellation includes [N _SAT N _P i P _SAT A _SAT ], which respectively represent the number of single-orbit satellites, the number of constellation orbit planes, the orbital inclination, the transmit power of the satellite antenna, and the equivalent area of the satellite antenna. 5. A low-orbit satellite constellation optimization design method that guarantees service quality according to claim 1, characterized in that setting the service quality threshold in step S4 is to set the signal-to-noise ratio, bit error rate, invulnerability, The threshold value of coverage and user matching degree, the specific symbol is expressed as 6. A low-orbit satellite constellation optimization design method that guarantees service quality according to claim 5, characterized in that (4) user matching degree S in step S5 is defined as in time slots 0, 1, 2, ..., on L-1, the matching degree of satellite resources to the capacity needs of users in different grid point areas on the ground. Its value is between 0 and 1. The greater the matching degree of users, the greater the matching degree of the user, which is the degree of matching between the satellite constellation and the capacity requirements of users in different grid point areas on the ground. The more closely matched the user needs are, the calculation steps are as follows; Put the signal-to-noise ratio into equation (6) to calculate the downlink rate R of a single satellite; P _SAT represents the transmit power of the satellite, G _SAT represents the satellite antenna gain, G _r the user's antenna gain, L _f represents the path loss, L _M represents the link margin, T represents the noise temperature of the system and K represents Boltzmann's constant. , the gain of the antenna is calculated by equation (7); Among them, eta _SAT represents the efficiency of each antenna, A _SAT represents the equivalent area of the antenna, f represents the operating frequency of the system, and c represents the speed of light; The capacity of a single satellite, that is, the number of users it can serve, is expressed as Equation (8); R is the downlink data rate of a single satellite, η _MAE is the efficiency of multiple access modulation of the satellite antenna, R _user is the user's data rate, and its value is 1.554Mbps according to the T1 service standard set by the ITU; Therefore, the user matching degree is calculated by Equation (9); Among them, N _g is the number of grid points divided on the ground, STF _tn is the capacity matching situation of the nth grid point in the t time slot; in any time slot t (t=0,1,2,... ., L-1), if the capacity of the satellite constellation to grid point N provides a capacity greater than or equal to the total satellite communication population of the grid point, STF _tn = 1, otherwise, STF _tn = 0, as shown in Equation (10) shown; D(n) represents the number of satellite communication users at a ground grid point, the number of people who can pass through this grid point, N(n), and the proportion of communication users. And the proportion of satellite users/> Calculation of the product, that is, the number of satellite communication users at the nth grid point is C _t (n) is the capacity provided by the number of visible satellites in grid point n in time slot t, calculated based on the single satellite capacity and the number of visible satellites at ground grid points obtained from STK. 7. A low-orbit satellite constellation optimization design method that guarantees service quality according to claim 1, characterized in that the calculation method of the objective function value in step S6 is as follows: Define the set of satellites that the satellite constellation provides services to grid point n in time slot t as S _t ={m|θ _nm ≥θ _min }, θ _nm is the elevation angle of grid point n to satellite m, and θ _min is the The lowest elevation angle for good communication conditions, then C _t (n) in equation (10) is expressed as shown in equation (11); Therefore, during the entire cycle, the capacity provided by the satellite constellation for the target area is the sum of the capacity provided in each time slot; Then the objective function value-the cost-effectiveness ratio of the network is expressed as the ratio of the sum of the above capacities and the cost of building the network, as shown in Equation (13); 8. A low-orbit satellite constellation optimization design method that guarantees service quality according to claim 7, characterized in that step S6 uses a tabu search algorithm to optimize the constellation parameters means: 1) Obtain the output solution of the genetic algorithm as the current solution of the tabu search algorithm and set the service quality threshold; 2) Determine whether the optimization goal remains unchanged, if so, output the result; if not, proceed to the next step; 3) Perform neighborhood operations on the current solution to generate neighborhood solutions, and determine candidate solutions from the neighborhood based on service quality constraints and objective function values Ψ; 4) Determine whether the candidate solution satisfies the contempt principle. If so, replace the current solution with the best state solution based on the contempt principle criterion; and replace the earliest object that enters the taboo list with the best state solution; 5) Determine the taboo status of each object corresponding to the candidate solution, select the best status corresponding to the non-taboo object in the candidate solution set as the current new solution, and replace the earliest taboo object that enters the taboo list with the corresponding taboo object; 6) Determine whether the optimization target value in the algorithm has changed. If so, end the algorithm and output the optimized constellation parameters [N _SAT N _Pi P _SAT A _SAT ] and the maximum objective function value Ψ. Otherwise, go to step 3).