CN115276756A - A low-orbit satellite constellation optimization design method to ensure service quality - Google Patents

A low-orbit satellite constellation optimization design method to ensure service quality Download PDF

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CN115276756A
CN115276756A CN202210706998.1A CN202210706998A CN115276756A CN 115276756 A CN115276756 A CN 115276756A CN 202210706998 A CN202210706998 A CN 202210706998A CN 115276756 A CN115276756 A CN 115276756A
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戴翠琴
秦杰鹏
许涛
谢颖
廖明霞
唐宏
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Chongqing University of Post and Telecommunications
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Abstract

The invention requests to protect a low earth 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, effectiveness and completeness of the satellite constellation and provides the definition of the service quality of the satellite constellation. Bit error rate, signal-to-noise ratio and survivability are introduced to express the reliability of a satellite constellation; introducing coverage to indicate the effectiveness of the satellite constellation; the completeness of a user in a satellite constellation is represented by a user matching degree. On the basis, a service quality threshold value is set, a service quality value is calculated, the ratio of the total system capacity of the target area to the constellation construction cost is set as an objective function, the objective function value is iteratively optimized by utilizing the global search capability of the genetic algorithm to obtain an initial constellation solution, and the initial solution is secondarily optimized by utilizing the local search capability of the tabu search algorithm to output the optimal constellation parameter. The invention is oriented to regional users, and optimizes and designs the efficient and economic low-orbit satellite constellation according to the user requirements and the service quality guarantee.

Description

一种保障服务质量的低轨卫星星座优化设计方法An Optimum Design Method for LEO Satellite Constellation with Guaranteed Quality of Service

技术领域technical field

本发明属于无线通信技术领域。具体涉及一种保障服务质量的低轨卫星星座优化设计方法。The invention belongs to the technical field of wireless communication. Specifically, the invention relates to a low-orbit satellite constellation optimization design method for guaranteeing service quality.

背景技术Background technique

如今的地面网络具有一定的局限性,例如:基站大多分布在人口稠密的地区,人口稀疏的山区、沙漠和海洋的设备无法接入互联网,同时地面基站容易受自然灾害和战争等不可抗拒的因素的破坏。卫星通信以其地面限制小、灵活组网、覆盖范围广、服务质量高等诸多优点可以很好的补充地面网络的不足。Today's terrestrial networks have certain limitations. For example, base stations are mostly distributed in densely populated areas, and devices in sparsely populated mountainous areas, deserts, and oceans cannot access the Internet. At the same time, terrestrial base stations are vulnerable to irresistible factors such as natural disasters and wars. destruction. Satellite communication, with its advantages of small ground restrictions, flexible networking, wide coverage, and high service quality, can well supplement the insufficiency of ground networks.

卫星星座设计是卫星通信设计和组网的基础。卫星星座设计将多个具有相同或相似类型和功能的卫星分布在相似或互补的轨道上,并在集中式或分布式网络的管理控制下,协同完成一定的通信任务。由于单颗卫星无法实现对设计目标区域的实时覆盖,因此需要采用多颗卫星组成卫星星座,扩大卫星通信的覆盖范围。多颗卫星组成的卫星星座通过构建星间链路和地面站馈线链路形成卫星网络,从而实现全球范围内的互联与互通。卫星星座设计是卫星网络部署和运行的前提,它决定了卫星网络的运行和应用水平。传统的星座方案设计方法主要包括几何解析法和基于仿真的比较分析方法两大类。这两类方法仅仅针对卫星的覆盖性能优化。然而,如果星座设计忽略了设计的目标区域的用户需求和服务质量,将导致星座资源的冗余,从而造成星座成本的增加和星座服务能力的欠缺。Satellite constellation design is the basis of satellite communication design and networking. The satellite constellation design distributes multiple satellites with the same or similar types and functions in similar or complementary orbits, and under the management and control of a centralized or distributed network, they cooperate to complete certain communication tasks. Since a single satellite cannot achieve real-time coverage of the design target area, it is necessary to use multiple satellites to form a satellite constellation to expand the coverage of satellite communications. The satellite constellation composed of multiple satellites forms a satellite network through the construction of inter-satellite links and ground station feeder links, thereby achieving global interconnection and intercommunication. Satellite constellation design is the premise of satellite network deployment and operation, which determines the operation and application level of satellite network. Traditional constellation scheme design methods mainly include geometric analysis method and simulation-based comparative analysis method. These two types of methods are only optimized for the coverage performance of satellites. However, if the constellation design ignores the user requirements and service quality of the designed target area, it will lead to redundancy of constellation resources, resulting in an increase in constellation cost and a lack of constellation service capability.

CN107329146B,一种导航卫星低轨监测星座的优化设计方法,充分考虑了现有技术基础和未来技术发展趋势,分析了导航卫星低轨监测星座的设计需求及约束条件,选取Walker-δ星座和太阳同步回归轨道,同时构建了包括监测站覆盖因子、性能因子和星座轨道参数的评价准则,由此优化设计的导航卫星低轨监测星座具有较好的监测性能;按照本发明提出的导航卫星低轨监测星座优化设计方法,能够有效地实现导航卫星低轨监测星座的优化设计,技术方案科学、优化,可实现性强;设计的星座能够用较少的卫星总数,实现较大的监测站覆盖因子和性能因子。CN107329146B, a method for optimal design of navigation satellite low-orbit monitoring constellation, fully considers the existing technical basis and future technology development trend, analyzes the design requirements and constraints of navigation satellite low-orbit monitoring constellation, selects Walker-δ constellation and sun Synchronously return to the orbit, and construct the evaluation criterion that comprises monitoring station coverage factor, performance factor and constellation orbit parameter simultaneously, the navigation satellite low-orbit monitoring constellation of optimization design thus has better monitoring performance; According to the navigation satellite low-orbit that the present invention proposes The optimal design method of the monitoring constellation can effectively realize the optimal design of the low-orbit monitoring constellation of navigation satellites. The technical scheme is scientific, optimized, and highly achievable; the designed constellation can use a small number of satellites to achieve a larger coverage factor of monitoring stations and performance factors.

该发明面向南北半球中低纬度地区建立了多目标优化模型,优化目标包括最小覆盖因子和监测站覆盖性能因子。该优化目标仅仅考虑了卫星对地的覆盖特性。优化目标过于单一容易导致卫星资源的浪费。同时,该发明优化设计星座参数是采用传统的仿真加数学分析去确定最终的星座参数,并没有采用目前研究比较主流的智能优化算法求解。由此容易产生工作量过大、受限于研究人的经验以及最后求解的星座不是最优星座的问题。本发明充分考虑多服务质量指标,不仅仅限于星座的覆盖性能,还考虑了星座抗毁性、通信误码率和信噪比、用户容量匹配度。通过使以上指标作为优化模型的约束条件,限制了搜索空间的范围。同时结合遗传算法和禁忌搜索算法两个智能优化算法对模型进行求解,大大降低了工作量且输出的星座满足服务质量要求。The invention establishes a multi-objective optimization model for the middle and low latitude regions of the northern and southern hemispheres, and the optimization objectives include the minimum coverage factor and the coverage performance factor of monitoring stations. This optimization objective only considers the coverage characteristics of the satellite to the ground. If the optimization goal is too single, it will easily lead to the waste of satellite resources. At the same time, the optimal design of constellation parameters in this invention uses traditional simulation plus mathematical analysis to determine the final constellation parameters, and does not use the mainstream intelligent optimization algorithm in current research to solve the problem. As a result, it is easy to cause problems such as excessive workload, limited experience of researchers, and problems that the finally solved constellation is not the optimal constellation. The present invention fully considers multiple service quality indexes, not only limited to constellation coverage performance, but also considers constellation invulnerability, communication bit error rate and signal-to-noise ratio, and user capacity matching degree. By making the above indicators as the constraints of 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.

发明内容Contents of the invention

本发明旨在解决以上现有技术的问题。提出了一种保障服务质量的低轨卫星星座优化设计方法。本发明的技术方案如下:The present invention aims to solve the above problems of the prior art. An optimal design method for low-orbit satellite constellations with guaranteed quality of service is proposed. Technical scheme of the present invention is as follows:

一种保障服务质量的低轨卫星星座优化设计方法,其包括以下步骤:A method for optimizing the design of low-orbit satellite constellations for quality of service, comprising the following steps:

S1、采用网格点法中的等经纬度方法将低轨卫星星座需要覆盖的目标区域划分为面积相等的Ng个区域;S1, using the method of equal longitude and latitude in the grid point method to divide the target area that the low-orbit satellite constellation needs to cover into N g areas with equal areas;

S2、确定星座的回归周期和轨道高度;S2. Determine the return period and orbital height of the constellation;

S3、使用遗传算法的全局搜索能力,设置算法的种群规模为200个以及迭代次数20代,得到一组Walker星座初始解;S3. Using 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, and obtain a set of initial solutions of the Walker constellation;

S4、设置低轨卫星星座的服务质量参数的阈值,包括可靠性、有效性以及星座的完成性,其中,可靠性包括:误码率、信噪比、星座的抗毁性;有效性包括:星座对目标区域的覆盖率;星座完成性包括星座对用户的匹配度;S4. Set thresholds for quality of service parameters of the low-orbit satellite constellation, including reliability, validity, and completion of the constellation. Reliability includes: bit error rate, signal-to-noise ratio, and invulnerability of the constellation; validity includes: The coverage of the constellation to the target area; the completeness of the constellation includes the matching degree of the constellation to the user;

S5、结合星座STK仿真数据和对应的公式计算相应的服务质量值;S5. Combining the constellation STK simulation data and corresponding formulas to calculate corresponding service quality values;

S6、判断计算的服务质量值是否满足设置的阈值,若是则根据星座计算目标函数值,若否,采用禁忌搜索算法更新优化的星座参数解向量,并继续回到S4步操作;S6. Determine whether the calculated service quality value satisfies 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、判断是否满足最大的迭代次数,若是,则输入最优的优化目标值和对应的星座参数,若否,回到S4步。S7. Determine whether the maximum number of iterations is satisfied, if yes, input the optimal optimization target value and corresponding constellation parameters, if not, return to step S4.

进一步的,所述S1、采用网格点法中等经度法将目标区域划分为面积相等的Ng个区域,具体包括:Further, in the S1, the target area is divided into N g areas with equal areas by using the grid point method and the medium longitude method, specifically including:

1)根据目标区域选定所有网格点左下角的经纬度坐标。1) Select the latitude and longitude coordinates of the lower left corner of all grid points according to the target area.

2)选择网格点的基本单位并划分目标区域。基本单位即横向和纵向跨度。2) Select the basic unit of grid points and divide the target area. The basic units are horizontal and vertical spans.

进一步的,所述步骤S2确定星座的回归周期和轨道高度,具体包括:Further, the step S2 determines the return period and orbit height of the constellation, specifically including:

根据地球自转周期Te和需要的低轨卫星回归圈数n来确定卫星的回归周期TSAT,其公式表达如式(1)所示,The return period T SAT of the satellite is determined according to the earth's rotation period T e and the required number of low-orbit satellite return cycles n, and its formula is shown in formula (1),

Figure BDA0003705769330000031
Figure BDA0003705769330000031

Te表示地球自转周期。利用得出的卫星周期可以通过式(2)计算出低轨卫星的高度h;T e represents the Earth's rotation period. The altitude h of the low-orbit satellite can be calculated by formula (2) using the obtained satellite period;

Figure BDA0003705769330000032
Figure BDA0003705769330000032

其中Re表示地球的半径,G表示重力常量,me表示地球的质量。Where R e represents the radius of the earth, G represents the gravitational constant, and m e represents the mass of the earth.

进一步的,所述步骤S3利用遗传算法初始化一组较好的初始解集,遗传算法有交叉和变异操作,这使得较大种群规模产生的下一代表现型具有多样性;将此运用到星座设计中,可以产生一组较好的星座初始解;Walker星座的参数形成的解向量包括[NSAT NP iPSAT ASAT],其分别表示单轨道卫星数、星座轨道平面数、轨道倾角、卫星天线的发射功率、卫星天线的等效面积。Further, the step S3 uses the genetic algorithm to initialize a set of better initial solution sets. The genetic algorithm has crossover and mutation operations, which makes the next-generation phenotypes generated by a larger population size diverse; apply this to the constellation design , a set of better constellation initial solutions can be generated; the solution vector formed by the parameters of the Walker constellation includes [ NSAT N P iP SAT A SAT ], which respectively represent the number of single-orbit satellites, the number of constellation orbital planes, the orbital inclination, and the satellite The transmitting power of the antenna and the equivalent area of the satellite antenna.

进一步的,所述步骤S4的设置服务质量阈值即设置信噪比、误码率、抗毁性、覆盖率、用户匹配度的阈值,具体符号表示为

Figure BDA0003705769330000041
Further, the setting of the quality of service threshold in step S4 is to set the threshold of signal-to-noise ratio, bit error rate, invulnerability, coverage, and user matching degree, and the specific symbols are expressed as
Figure BDA0003705769330000041

进一步的,所述步骤5中各服务质量指标的具体计算方式如下:Further, the specific calculation method of each quality of service index in the step 5 is as follows:

(1)当给定低轨卫星网络的误码率的阈值BER0时,通过式(3)计算系统的信噪比;(1) When the threshold BER 0 of the bit error rate of the low-orbit satellite network is given, the signal-to-noise ratio of the system is calculated by formula (3);

Figure BDA0003705769330000042
Figure BDA0003705769330000042

erfc(·)分别表示互补误差函数,Eb/N0表示系统的信噪比。Eb表示平均比特能量,N0表示噪声功率谱密度。erfc(·) represent 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)抗毁性是引用复杂网络的自然连通度来量化的,采用周期动态自然连通度来量化优化星座的抗毁性,如式(4)所示;(2) The invulnerability is quantified by referring to the natural connectivity of the complex network, and the periodic dynamic natural connectivity is used to quantify and optimize the invulnerability of the constellation, as shown in formula (4);

Figure BDA0003705769330000043
Figure BDA0003705769330000043

其中,AT(G)、

Figure BDA0003705769330000044
TSAT、NT、Ti、P(·)分别表示连接概率矩阵、周期动态的自然连通度、卫星回归周期、对卫星周期划为的时间片数、每个时间片的长度、求对应矩阵的自然连通度。AT(G)中的元素表示在卫星网络中的动态拓扑周期内两个节点的保持连接的概率,
Figure BDA0003705769330000045
是AT(G)的第i个特征根,在一个回归周期内,星座的任意两节点的连接概率可以通过获取STK星间链路建链数据求出。Among them, A T (G),
Figure BDA0003705769330000044
T SAT , NT , T i , P(·) respectively represent the connection probability matrix, the natural connectivity degree of periodic dynamics, the satellite return cycle, the number of time slices divided into satellite cycles, the length of each time slice, and the corresponding matrix natural connectivity. The elements in A T (G) represent the probability of two nodes staying connected during the dynamic topology period in the satellite network,
Figure BDA0003705769330000045
is the i-th characteristic root of A T (G). In 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)在星座回归周期内,星座对目标区域的覆盖率CV是卫星星座对目标区域所有网格点的覆盖情况的加权统计,其具体的计算公式如式(5)所示。(3) In the constellation regression period, the coverage rate CV of the constellation to the target area is the weighted statistics of the coverage of all grid points in the target area by the satellite constellation, and its specific calculation formula is shown in formula (5).

Figure BDA0003705769330000046
Figure BDA0003705769330000046

其中Ng为地面网格点数,L为划分的时隙数,若t时刻,星座对网格点i覆盖,则yit=1,否则yit=0,以上计算通过传入星座参数并获取STK中星座对地面网格点的覆盖数据计算得出。Where N g is the number of grid points on the ground, L is the number of divided time slots, if at time t, the constellation covers grid point i, then y it =1, otherwise y it =0, the above calculation is obtained by passing in the constellation parameters and obtaining It is calculated from the coverage data of the constellation on the ground grid points in STK.

进一步的,所述步骤S5中(4)用户匹配度S定义为在时隙0,1,2,...,L-1上,卫星资源对地面不同网格点区域的用户的容量需求的匹配程度,其值在0~1之间,用户匹配度越大,即为卫星星座对地面不同区域的用户需求越匹配,其计算步骤如下;Further, in the step S5 (4), the user matching degree S is defined as the ratio of satellite resources to the capacity requirements of users in different grid point areas on the ground on time slots 0, 1, 2, ..., L-1 Matching degree, its value is between 0 and 1, the greater the user matching degree, that is, the more the satellite constellation matches the user needs in different areas on the ground, the calculation steps are as follows;

将信噪比带入式(6)计算单颗卫星下行速率R;Bring the signal-to-noise ratio into formula (6) to calculate the downlink rate R of a single satellite;

Figure BDA0003705769330000051
Figure BDA0003705769330000051

PSAT表示卫星的发射功率、GSAT表示卫星天线增益、Gr用户的天线增益、Lf表示路径损耗、LM表示链路余量、T表示系统的噪声温度以及K表示玻尔兹曼常数,天线的增益由式(7)计算; PSAT is the transmit power of the satellite, G SAT is the satellite antenna gain, G r is the antenna gain of the user, L f is the path loss, L M is the link margin, T is the noise temperature of the system, and K is the Boltzmann constant , the gain of the antenna is calculated by equation (7);

Figure BDA0003705769330000052
Figure BDA0003705769330000052

其中ηSAT表示每个天线的效率,ASAT表示天线的等效面积,f表示系统的工作频率,c表示光速;Wherein η 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;

单颗卫星的容量,即可以服务的用户数,表示为式(8);The capacity of a single satellite, that is, the number of users that can be served, is expressed as formula (8);

Figure BDA0003705769330000053
Figure BDA0003705769330000053

R为单颗卫星下行数据速率,ηMAE为卫星天线的多址调制的效率,Ruser为用户的数据速率,根据ITU设定的T1服务标准其值为1.554Mbps;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 data rate of the user, and its value is 1.554Mbps according to the T1 service standard set by the ITU;

由此,用户匹配度的由式(9)计算得出;Thus, the user matching degree is calculated by formula (9);

Figure BDA0003705769330000054
Figure BDA0003705769330000054

其中,Ng为地面划分的网格点数,STFtn为在第t个时隙对第n个网格点的容量匹配情况;在任意时隙t(t=0,1,2,....,L-1)内,如果卫星星座对网格点N的容量提供大于等于该网格点的总的卫星通信人口数,STFtn=1,反之,STFtn=0,如式(10)所示;Among them, N g is the number of grid points divided by the ground, STF tn is the capacity matching situation of the nth grid point in the tth time slot; in any time slot t(t=0,1,2,... ., L-1), if the satellite constellation provides the capacity of the grid point N greater than or equal to the total satellite communication population of the grid point, STF tn = 1, otherwise, STF tn = 0, such as formula (10) shown;

Figure BDA0003705769330000055
Figure BDA0003705769330000055

D(n)表示地面网格点的卫星通信用户数,可以通过此网格点的人口数N(n)、通信用户的比例

Figure BDA0003705769330000061
以及卫星用户的比例
Figure BDA0003705769330000062
的乘积计算,即第n个网格点的卫星通信用户数为
Figure BDA0003705769330000063
Ct(n)为t时隙内,网格点n中可见卫星数量提供的容量,根据单星容量和从STK获取的地面网格点可见卫星数计算。D(n) represents the number of satellite communication users of the ground grid point, the population N(n) who can pass through this grid point, and the proportion of communication users
Figure BDA0003705769330000061
and the proportion of satellite users
Figure BDA0003705769330000062
The product of is calculated, that is, the number of satellite communication users at the nth grid point is
Figure BDA0003705769330000063
C t (n) is the capacity provided by the number of visible satellites in the grid point n in time slot t, calculated according to the single-satellite capacity and the number of visible satellites in the ground grid point obtained from STK.

进一步的,所述步骤S6目标函数值的计算方式如下。Further, the calculation method of the objective function value in step S6 is as follows.

定义t时隙内,卫星星座对网格点n提供服务的卫星集合为St={m|θnm≥θmin},θnm是网格点n对卫星m的仰角,θmin是为达到良好通信条件的最低仰角,则式(10)中Ct(n)表示为式(11)所示;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 }, where θ nm is the elevation angle of grid point n to satellite m, and θ min is to achieve The lowest elevation angle of good communication conditions, then C t (n) in formula (10) is expressed as shown in formula (11);

Figure BDA0003705769330000064
Figure BDA0003705769330000064

由此在整个周期内,卫星星座为目标区域提供的容量即是各个时隙内提供的容量的总和;Therefore, in the whole period, the capacity provided by the satellite constellation for the target area is the sum of the capacities provided in each time slot;

Figure BDA0003705769330000065
Figure BDA0003705769330000065

则目标函数值-网络的费效比表示为以上容量总和与构建网络的开销的比值,即如式(13)所示;Then the objective function value-the cost-effectiveness ratio of the network is expressed as the ratio of the sum of the above capacities to the cost of constructing the network, as shown in formula (13);

Figure BDA0003705769330000066
Figure BDA0003705769330000066

进一步的,所述步骤S6采用禁忌搜索算法优化星座参数是指:Further, said step S6 using the tabu search algorithm to optimize the constellation parameters refers to:

1)获取遗传算法的输出解作为禁忌搜索算法的当前解并设置服务质量阈值;1) Obtain the output solution of the genetic algorithm as the current solution of the tabu search algorithm and set the quality of service threshold;

2)判断是否满足优化目标保持不变,若是,则输出结果;若否,则进入下一步;2) Judging whether the optimization goal is satisfied remains unchanged, if so, then output the result; if not, then enter the next step;

3)对当前解做邻域操作生成邻域解,根据服务质量约束和目标函数值Ψ从邻域中确定候选解;3) Perform neighborhood operations on the current solution to generate neighborhood solutions, and determine candidate solutions from the neighborhood according to the quality of service constraints and the objective function value Ψ;

4)对候选解判断藐视原则是否满足,若是,则用藐视原则准则的最佳状态解替代当前解;并用最佳状态的解替换最早进入禁忌表的对象;4) Judging whether the defiance principle is satisfied for the candidate solution, if so, replace the current solution with the best state solution that despises the principle criterion; and replace the object that first entered the taboo list with the best state solution;

5)判断候选解对应的各对象的禁忌状态,选择候选解集中非禁忌对象对应的最佳状态为当前的新解,同时用与之对应的禁忌对象替换最早进入禁忌表的禁忌对象;5) Judge the taboo state of each object corresponding to the candidate solution, select the best state corresponding to the non-taboo object in the candidate solution set as the current new solution, and replace the taboo object that entered the taboo table the earliest with the corresponding taboo object;

6)判断算法中优化目标值是否变化,若是,则结束算法并输出优化的星座参数[NSAT NP i PSAT ASAT]和最大的目标函数值Ψ否则转到步骤3)。6) Determine whether the optimization target value in the algorithm changes, if so, end the algorithm and output the optimized constellation parameters [ NSAT N P i P SAT A SAT ] and the maximum objective function value Ψ; otherwise, go to step 3).

本发明的优点及有益效果如下:Advantage of the present invention and beneficial effect are as follows:

本发明提出了一种保障服务质量的低轨卫星星座优化设计方法,定义低轨卫星星座设计的服务质量指标为可靠性、有效性以及完成性。可靠性表示此星座给用户提供较小差错和高质量的通信性能,具体量化指标包括误码率、信噪比以及星座的抗毁性。有效性是指低轨卫星星座组成的网络针对目标区域所有用户提供服务的能力,具体量化指标是星座的覆盖率。完成性是指网络针对不同地域用户需求的匹配情况,具体指标为用户匹配度。在服务质量约束中,一个卫星回归周期内,用户匹配度对比了目标区域网格点实际的卫星通信人口数与网格点可见卫星可容纳的用户数,通过两者的关系和单星的容量定义了其计算公式,属于本发明的一个独特的创新。本发明的主要创新在于,利用卫星仿真软件STK,根据实际需求,设置服务质量约束,并结合算法优化星座的费效比,设计资源利用率较高的低成本星座。在已有的研究中,星座优化设计都是考虑面向区域覆盖性能,很少有结合星地用户链路和星间链路建立优化约束指标进行优化设计的。本发明的星座优化设计同时考虑低轨卫星星座星间和星地特性,并由此设计服务质量指标。因此本发明提出的服务质量约束设计体系是现有技术人员不容易想到的。本发明在卫星回归周期内,通过考虑星间链路建立,引入了复杂网络中的抗毁性指标自然连通度。为了适应卫星网络的动态性,设计了周期动态自然连通度表示卫星星座的抗毁性指标。同时,通过考虑星地用户链路,引入了误码率、信噪比并设计用户匹配度,利用以上指标建立了星座对地面用户需求的联系。因此本发明充分考虑了星座固有的抗毁性和星地用户之间资源的匹配性,设计的星座具有较高的性价比。在算法方面,本方法为了避免传统的星座优化设计中单一算法的局限性,结合遗传算法的全局搜索能力和禁忌搜索算法的局部搜索能力,以达到使目标不陷入局部解且对星座参数解空间充分搜索的目的。由此设计出满足服务质量指标的最优低轨卫星星座。The 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 completeness. Reliability means that the constellation provides users with small errors and high-quality communication performance, and the specific quantitative indicators include bit error rate, signal-to-noise ratio, and invulnerability of the constellation. Effectiveness refers to the ability of the network composed of low-orbit satellite constellations to provide services to all users in the target area, and the specific quantitative index is the coverage of the constellation. Completion refers to how the network matches the needs of users in different regions, and the specific indicator is the degree of user matching. In the quality of service constraint, within a satellite return cycle, the user matching degree compares the actual satellite communication population of the grid point in the target area with the number of users that can be accommodated by the visible satellite of the grid point, through the relationship between the two and the capacity of a single satellite Its calculation formula is defined, which belongs to a unique innovation of the present invention. The main innovation of the present invention is to use the 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 studies, the constellation optimization design always considers the area-oriented coverage performance, and there are few optimization designs that combine satellite-ground user links and inter-satellite links to establish optimization constraints. The constellation optimization design of the present invention considers the inter-satellite and satellite-ground characteristics of the low-orbit satellite constellation at the same time, and thus designs the service quality index. Therefore, the quality of service constraint design system proposed by the present invention is not easy to think of by those skilled in the art. The invention introduces the natural connectivity of the invulnerability index in the complex network by considering the establishment of the inter-satellite link in the satellite return period. In order to adapt to the dynamic nature of the satellite network, a periodic dynamic natural connectivity is designed to represent the invulnerability index of the satellite constellation. At the same time, by considering the satellite-ground user link, introducing the bit error rate, signal-to-noise ratio and designing the user matching degree, using the above indicators to establish the relationship between the constellation and the ground user requirements. Therefore, the present invention fully considers the inherent invulnerability of the constellation and the resource matching between satellite and ground users, and the designed constellation has higher cost performance. In terms of algorithms, in order to avoid the limitations of a single algorithm in the 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 the local solution and the solution space of the constellation parameters. full search purposes. Therefore, the optimal low-orbit satellite constellation that meets the service quality index is designed.

附图说明Description of drawings

图1是本发明提供优选实施例整体流程图;Fig. 1 is the overall flowchart of the preferred embodiment provided by the present invention;

图2是遗传算法初始化星座流程图;Fig. 2 is a flowchart of genetic algorithm initialization constellation;

图3是禁忌搜索算法二次寻优示意图。Fig. 3 is a schematic diagram of the secondary optimization of the tabu search algorithm.

具体实施方式Detailed ways

下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、详细地描述。所描述的实施例仅仅是本发明的一部分实施例。The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

本发明解决上述技术问题的技术方案是:The technical scheme that the present invention solves the problems of the technologies described above is:

一种保障服务质量的低轨卫星星座优化设计方案,通过设置服务质量约束并结合遗传算法和禁忌搜索算法得出最优的卫星星座。其中星座的服务质量指标包括可靠性、有效性以及完成性。具体的量化指标是误码率、信噪比、抗毁性、覆盖率、用户匹配度。以上指标与星座性能紧密相关,所以设置以上指标的约束即对星座参数的解空间做一定的约束。通过在算法程序中设计以上指标的约束条件,并结合优化目标即星座的最大的费效比,迭代求解,最后输出满足服务质量约束的最大费效比的卫星星座参数。A low-orbit satellite constellation optimization design scheme that guarantees service quality. The optimal satellite constellation is obtained by setting service quality constraints and combining genetic algorithms and tabu search algorithms. Constellation's quality of service indicators include reliability, effectiveness, and completeness. The specific quantitative indicators are bit error rate, signal-to-noise ratio, invulnerability, coverage rate, and user matching degree. The above indicators are closely related to the performance of the constellation, so setting the constraints on the above indicators is to make certain constraints on the solution space of the constellation parameters. By designing the constraint conditions of the above indicators in the algorithm program, combined with the optimization goal that is the maximum cost-effectiveness ratio of the constellation, iteratively solves it, and finally outputs the satellite constellation parameters that satisfy the service quality constraints and the maximum cost-effectiveness ratio.

具体步骤如下:Specific steps are as follows:

第一步:我们在设定区域目标个数时,将中国地区用STK软件自动划分为n个区域(具体划分个数按照选定经纬度间隔划分结果为准,通常取3°)。用于根据人口分布图,计算各个网格点的人口数。Step 1: When setting the target number of regions, we will automatically divide the Chinese region into n regions with the STK software (the specific number of divisions shall be based on the results of the division of the selected longitude and latitude intervals, usually 3°). It is used to calculate the population of each grid point according to the population distribution map.

第二步:根据目标区域,确定基本星座构型为Walker星座,确定卫星星座中每颗卫星的回归周期TS,由此确定了星座卫星轨道高度h。轨道高度确定后,Walker星座需优化的参数即为单轨道卫星数、卫星轨道数、轨道倾角、单星天线发射功率、天线的有效面积,符号表示为[NSAT NP i PSAT ASAT]。The second step: according to 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 thus determine the constellation satellite orbit height h. After the orbital height is determined, the parameters to be optimized for the Walker constellation are the number of single-orbit satellites, the number of satellite orbits, the orbital inclination, the transmit power of a single-satellite antenna, and the effective area of the antenna. The symbols are expressed as [ NSAT N P i P SAT A SAT ] .

第三步:利用遗传算法初始化一组全局较优解,目的是避免搜索目标陷入局部最优解。The third step: use the genetic algorithm to initialize a group of global optimal solutions, the purpose is to avoid the search target from falling into the local optimal solution.

第四步:设置服务质量指标的阈值,包括信噪比、误码率、抗毁性、覆盖率、用户匹配度,其符号表示为

Figure BDA0003705769330000091
并结合STK软件中仿真的优化参数组成的星座性能数据和服务质量指标计算公式计算相应的服务质量值。Step 4: Set the threshold of service quality indicators, including signal-to-noise ratio, bit error rate, invulnerability, coverage, and user matching degree. The symbols are expressed as
Figure BDA0003705769330000091
And combined with the constellation performance data composed of optimized parameters simulated in the STK software and the calculation formula of the service quality index, the corresponding service quality value is calculated.

第五步:禁忌搜索算法都是从解向量的领域去寻求最优解,因此通过禁忌搜索算法的局部搜索能力,对初始解集进行二次寻优。算法中通过第四步所述的计算方法判断是否满足服务质量阈值,通过循环迭代,当满足优化目标Ψ不变时,输出最优的星座参数构型和最大的优化目标。Step 5: The tabu search algorithm seeks the optimal solution from the field of the solution vector, so through the local search ability of the tabu search algorithm, the initial solution set is searched twice. In the algorithm, the calculation method described in the fourth step is used to judge whether the service quality threshold is met, and through loop iterations, when the optimization target Ψ is satisfied, the optimal constellation parameter configuration and the largest optimization target are output.

优选的,所述第二步,确定卫星周期和轨道的计算方法如下:Preferably, in the second step, the calculation method for determining satellite period and orbit is as follows:

根据地球自转周期Te和需要的低轨卫星回归圈数n来确定卫星的回归周期TSAT,其公式表达如式(1)所示。The return period T SAT of the satellite is determined according to the earth rotation period T e and the required number of low-orbit satellite return cycles n, and its formula expression is shown in formula (1).

Figure BDA0003705769330000092
Figure BDA0003705769330000092

利用得出的卫星周期可以通过式(2)计算出低轨卫星的高度h。The altitude h of the low-orbit satellite can be calculated by formula (2) using the obtained satellite period.

Figure BDA0003705769330000093
Figure BDA0003705769330000093

优选的,所述第三步,利用遗传算法初始化一组全局较优解。遗传算法有交叉和变异操作,这使得较大种群规模产生的下一代表现型具有多样性。将此运用到星座设计中,可以产生一组较好的星座初始解。Walker星座的参数形成的解向量包括[NSAT NP i PSATASAT],其分别表示单轨道卫星数、星座轨道平面数、轨道倾角、卫星天线的发射功率、卫星天线的等效面积。Preferably, in the third step, a group of global optimal solutions is initialized using a genetic algorithm. Genetic algorithms have crossover and mutation operations, which make the phenotypes of the next generation generated by larger population sizes diverse. Applying this to constellation design can produce a set of better constellation initial 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 orbital planes, the orbital inclination, the transmitting power of the satellite antenna, and the equivalent area of the satellite antenna.

初始化Walker星座参数的具体步骤为:The specific steps to initialize the Walker constellation parameters are:

1)设置种群规模为200,迭代次数20代。1) Set the population size to 200, and the number of iterations to 20 generations.

2)计算星座优化目标函数值Ψ。2) Calculate the constellation optimization objective function value Ψ.

3)判断终止条件。3) Determine the termination condition.

4)若是,则生成初始星座参数解集。若否,则进行选择、交叉、变异操作,并回到第二步。(设置的交叉概率为0.8,变异概率为0.1)。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 set to 0.1).

具体流程如图2所示。The specific process is shown in Figure 2.

优选的,所述第四步,在优化算法计算抗毁性、覆盖率、用户匹配度三个服务质量指标时,需要传入优化中的星座参数给STK,然后分别获取在卫星回归周期内,星间链路建链数据、卫星覆盖数据以及地面网格对星座的可见性数据。Preferably, in the fourth step, when the optimization algorithm calculates the three service quality indicators of invulnerability, coverage, and user matching degree, the constellation parameters in the optimization need to be passed in to STK, and then obtained in the satellite return period respectively, Inter-satellite link establishment data, satellite coverage data, and ground grid-to-constellation visibility data.

优选的,所述第四步,分别对信噪比、误码率、抗毁性、覆盖率、用户匹配度进行定义和计算。包括以下计算公式:Preferably, the fourth step is to define and calculate signal-to-noise ratio, bit error rate, invulnerability, coverage rate, and user matching degree respectively. Include the following calculation formulas:

1)当给定低轨卫星网络的误码率的阈值BER0时,可以通过式(3)计算系统的信噪比SNR。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 (SNR) of the system can be calculated by formula (3).

Figure BDA0003705769330000101
Figure BDA0003705769330000101

2)抗毁性是引用复杂网络的自然连通度来量化的。复杂网络中的指标自然连通度具有严格单调的特性。它表示网络中每个节点的闭环数量的总和,可以衡量网络的冗余。自然连通度可以用来衡量一个网络中存在的代替路劲的冗余性。其公式如式(4)所示。2) Invulnerability is quantified by referring to the natural connectivity of complex networks. The index natural connectivity in complex networks is strictly monotonic. It represents the sum of the number of closed loops of each node in the network, which can measure the redundancy of the network. Natural connectivity can be used to measure the redundancy of alternative paths existing in a network. Its formula is shown in formula (4).

Figure BDA0003705769330000102
Figure BDA0003705769330000102

其中,λi是图G(V,E)的邻接矩阵A(G)的第i个特征根,由此,一个网络的自然连通度为网络的邻接矩阵的特征谱然后取自然对数后的平均值。然而由于低轨卫星网络拓扑的快速动态变化,静态网络的自然连通度并不适合于卫星网络。Among them, λ i is the i-th characteristic root of the adjacency matrix A(G) of the graph G(V,E), thus, the natural connectivity of a network is the characteristic spectrum of the adjacency matrix of the network and then taking the natural logarithm average value. However, due to the fast and dynamic changes of the topology of the LEO satellite network, the natural connectivity of the static network is not suitable for the satellite network.

采用周期动态自然连通度来量化优化星座的抗毁性。如式(5)所示。The periodic dynamic natural connectivity is used to quantify and optimize the invulnerability of the constellation. As shown in formula (5).

Figure BDA0003705769330000103
Figure BDA0003705769330000103

其中,AT(G)表示连接概率矩阵。AT(G)中的元素表示在卫星网络中的动态拓扑周期内两个节点的保持连接的概率。在一个回归周期内,星座的任意两节点的连接概率可以通过获取STK星间链路建链数据求出。Among them, A T (G) represents the connection probability matrix. The elements in A T (G) represent the probability of two nodes staying connected during the dynamic topology period in the satellite network. In 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)在星座回归周期内,星座对目标区域的覆盖率是卫星星座对目标区域所有网格点的覆盖情况的加权统计。其具体的计算公式如式(6)所示。3) During the constellation regression period, the coverage rate of the constellation to the target area is the weighted statistics of the coverage of all grid points of the target area by the satellite constellation. Its specific calculation formula is shown in formula (6).

Figure BDA0003705769330000111
Figure BDA0003705769330000111

其中Ng为地面网格点数,L为划分的时隙数。若t时刻,星座对网格点i覆盖,则yit=1,否则yit=0。以上计算通过传入星座参数并获取STK中星座对地面网格点的覆盖数据计算得出。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)将单星回归周期TS划分成不同的小的时隙ΔT。划分的时隙数L可以通过单星回归周期TS和ΔT比值得出。在每个时隙中,卫星的位置可以当作不变。用户匹配度定义为在时隙0,1,2,...,L-1上,卫星资源对地面不同网格点区域的用户的容量需求的匹配程度,其值在0~1之间,用户匹配度越大,即为卫星星座对地面不同区域的用户需求越匹配。其计算步骤如下:4) Divide the single-satellite return period T S into different small time slots ΔT. The divided time slot number L can be obtained by the single-satellite return period T S and the ratio of ΔT. In each time slot, the position of the satellite can be considered as constant. 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 at time slots 0, 1, 2, ..., L-1, and its value is between 0 and 1. The greater the user matching degree, the more the satellite constellation matches the user requirements of different areas on the ground. Its calculation steps are as follows:

首先,将信噪比带入式(7)计算单颗卫星下行速率R。First, bring the signal-to-noise ratio into equation (7) to calculate the downlink rate R of a single satellite.

Figure BDA0003705769330000112
Figure BDA0003705769330000112

PSAT表示卫星的发射功率、GSAT表示卫星天线增益、Gr用户的天线增益、Lf表示各种传输损耗、LM表示链路余量、T表示系统的噪声温度以及K表示玻尔兹曼常数。天线的增益由式(8)计算。 PSAT is the transmit power of the satellite, G SAT is the satellite antenna gain, G r is the antenna gain of the user, L f is the various transmission losses, L M is the link margin, T is the noise temperature of the system, and K is Boltz Mann constant. The gain of the antenna is calculated by formula (8).

Figure BDA0003705769330000113
Figure BDA0003705769330000113

其中ηSAT表示每个天线的效率,ASAT表示天线的等效面积,f表示系统的工作频率,c表示光速。Where η 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.

其次,由单颗卫星下行数据速率R、用户的数据速率Ruser(根据ITU设定的T1服务标准其值为1.554Mbps。)以及卫星天线的多址调制的效率ηMAE计算出单颗卫星的容量。单星容量即为可以服务的用户数,表示为式(9)。Secondly, by single satellite downlink data rate R, user's data rate R user (according to the T1 service standard that ITU sets, its value is 1.554Mbps.) and the efficiency η MAE of the multiple access modulation of satellite antenna calculates the single satellite capacity. The capacity of a single satellite is the number of users that can be served, expressed as formula (9).

Figure BDA0003705769330000121
Figure BDA0003705769330000121

最后,用户匹配度由式(10)计算得出。Finally, user matching degree is calculated by formula (10).

Figure BDA0003705769330000122
Figure BDA0003705769330000122

其中,Ng为地面划分的网格点数,STFtn为在第t个时隙对第n个网格点的容量匹配情况。在任意时隙t(t=0,1,2,....,L-1)内,如果卫星星座对网格点n的容量提供大于等于该网格点的总的卫星通信人口数,STFtn=1,反之,STFtn=0。如式(11)所示。Among them, N g is the number of grid points divided by the ground, and STF tn is the capacity matching situation of the nth grid point in the tth time slot. In any time slot t(t=0,1,2,....,L-1), if the capacity of the satellite constellation to the grid point n provides more than or equal to the total satellite communication population of the grid point, STF tn =1, otherwise, STF tn =0. As shown in formula (11).

Figure BDA0003705769330000123
Figure BDA0003705769330000123

D(n)表示地面网格点的卫星通信用户数,可以通过此网格点的人口数N(n)、通信用户的比例

Figure BDA0003705769330000124
以及卫星用户的比例
Figure BDA0003705769330000125
的乘积计算,即第n个网格点的卫星通信用户数为
Figure BDA0003705769330000126
Ct(n)为t时隙内,网格点n中可见卫星数量提供的容量。它可以根据单星容量和从STK获取的地面网格点可见卫星数计算。D(n) represents the number of satellite communication users of the ground grid point, the population N(n) who can pass through this grid point, and the proportion of communication users
Figure BDA0003705769330000124
and the proportion of satellite users
Figure BDA0003705769330000125
The product of is calculated, that is, the number of satellite communication users at the nth grid point is
Figure BDA0003705769330000126
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 according to the single satellite capacity and the number of visible satellites of the ground grid points obtained from STK.

优选的,所述第四步利用禁忌搜索算法进行局部寻优,其特征在于,利用遗传算法优化的解作为禁忌搜索算法输入的初始解,利用禁忌搜索算法的邻域操作的局部搜索能力再进行局部二次寻优。其具体步骤如下: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 by the tabu search algorithm, and the local search capability of the neighborhood operation of the tabu search algorithm is used to perform Local quadratic optimization. The specific steps are as follows:

7)获取遗传算法的输出解作为禁忌搜索算法的当前解并设置服务质量阈值。7) Obtain the output solution of the genetic algorithm as the current solution of the tabu search algorithm and set the quality of service threshold.

8)判断是否满足优化目标保持不变,若是,则输出结果。若否,则进入下一步。8) Judging whether the optimization objective is satisfied remains unchanged, and if so, output the result. If not, go to the next step.

9)对当前解做邻域操作生成邻域解,根据服务质量约束和目标函数值Ψ从邻域中确定候选解。9) Perform neighborhood operations on the current solution to generate neighborhood solutions, and determine candidate solutions from the neighborhood according to the quality of service constraints and the objective function value Ψ.

10)对候选解判断藐视原则是否满足,若是,则用藐视原则准则的最佳状态解替代当前解。并用最佳状态的解替换最早进入禁忌表的对象。10) Judging whether the candidate solution satisfies the defiance principle, if so, replace the current solution with the best state solution under the defiance principle criterion. And replace the object that enters the tabu list earliest with the solution of the best state.

11)判断候选解对应的各对象的禁忌状态,选择候选解集中非禁忌对象对应的最佳状态为当前的新解,同时用与之对应的禁忌对象替换最早进入禁忌表的禁忌对象。11) Determine the taboo state of each object corresponding to the candidate solution, select the best state corresponding to the non-taboo object in the candidate solution set as the current new solution, and replace the tabu object that entered the tabu list first with the corresponding tabu object.

12)判断算法中优化目标值是否变化,若是,则结束算法并输出优化的星座参数[NSAT NP i PSAT ASAT]和最大的目标函数值Ψ否则转到步骤3)12) Determine whether the optimization target value in the algorithm changes, if so, end the algorithm and output the optimized constellation parameters [ NSAT N P i P SAT A SAT ] and the maximum objective function value Ψ Otherwise go to step 3)

其具体流程如图3所示。Its specific process is shown in Figure 3.

本发明内容所涉及的概念和模型如下:The concepts and models involved in the content of the present invention are as follows:

1.网络模型1. Network model

本发明的主要场景是对中国地区愿意加入卫星通信的用户的星座覆盖。空间段由低轨卫星组成的低轨卫星宽带网络组成,能为用户提供至少1.554Mbps的数据速率。地面段由地面用户和地面宽带网络组成。卫星宽带网络能很好的弥补地面网络对偏远地区和极端环境地区覆盖的不足,还能为基站遭遇自然灾害造成的通信中断提供通信服务。由于中国地区的人口极度不均匀,因此如果不考虑用户的分布特性在星座设计中,将会造成卫星资源的浪费以及提高卫星星座的设计成本。本模型是根据实际的低轨卫星宽带网络服务质量设定以及地面网格点中的用户分布去设计一种保障用户需求和服务质量的低轨卫星星座。The main scenario of the present invention is the constellation coverage for users willing to join the satellite communication in the Chinese region. 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 terrestrial segment consists of terrestrial users and terrestrial broadband networks. Satellite broadband network can well make up for the lack of ground network coverage in remote areas and extreme environment areas, and can also provide communication services for communication interruptions caused by natural disasters in base stations. Because the population in China is extremely uneven, if the distribution characteristics of users are not considered in the constellation design, it will cause waste of satellite resources and increase the design cost of satellite constellations. This model is based on the actual low-orbit satellite broadband network service quality setting and the user distribution in the ground grid points to design a low-orbit satellite constellation that guarantees user needs and service quality.

2.本发明技术方案如下:2. The technical scheme of the present invention is as follows:

本发明提出了一种保障服务质量的低轨卫星星座优化设计方法。首先,建立了包含LEO卫星星座和地面用户的系统模型。然后,一种保障服务质量的低轨卫星星座优化设计方法包括从星座可靠性、有效性、完成性三个方面进行星座设计。其中,可靠性考虑了误码率、信噪比以及抗毁性。有效性考虑了星座覆盖率。完成性考虑了用户匹配度。通过设置以上指标的阈值以及定义其计算方式,即建立服务质量约束以限定星座的优化解空间。最后,通过遗传算法初始化星座参数,以及禁忌搜索算法二次寻优,输出最优星座参数以及最大的目标函数值Ψ。The invention proposes a low-orbit satellite constellation optimization design method that guarantees service quality. First, a system model including the LEO satellite constellation and ground users is established. Then, an optimal design method of low-orbit satellite constellation that guarantees service quality includes constellation design from three aspects: constellation reliability, effectiveness, and completeness. Among them, reliability takes into account bit error rate, signal-to-noise ratio and invulnerability. Validity takes into account constellation coverage. Completion takes into account user fit. By setting the thresholds of the above indicators and defining their calculation methods, the quality of service constraints are established to limit the optimal solution space of the constellation. Finally, the constellation parameters are initialized through the genetic algorithm, and the tabu search algorithm is optimized twice, and the optimal constellation parameters and the maximum objective function value Ψ are output.

还需要说明的是,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、商品或者设备不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、方法、商品或者设备所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括所述要素的过程、方法、商品或者设备中还存在另外的相同要素。It should also be noted that the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes Other elements not expressly listed, or elements inherent in the process, method, commodity, or apparatus are also included. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

以上这些实施例应理解为仅用于说明本发明而不用于限制本发明的保护范围。在阅读了本发明的记载的内容之后,技术人员可以对本发明作各种改动或修改,这些等效变化和修饰同样落入本发明权利要求所限定的范围。The above embodiments should be understood as only for illustrating the present invention but not for limiting the protection scope of the present invention. After reading the contents 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 (9)

1. A low-orbit satellite constellation optimization design method for guaranteeing service quality is characterized by comprising the following steps:
s1, dividing a target area to be covered by a low-orbit satellite constellation into N areas with equal areas by adopting an equal longitude and latitude method in a grid point methodgAn area;
s2, determining a regression cycle and an orbit height of the constellation;
s3, using the global search capability of the genetic algorithm, setting the population scale of the algorithm to be 200 and the iteration times to be 20, and obtaining a group of Walker constellation initial solutions;
s4, setting a threshold value of the service quality parameter of the low-orbit satellite constellation, wherein the threshold value comprises reliability, effectiveness and constellation completeness, and the reliability comprises the following steps: bit error rate, signal-to-noise ratio, and constellation survivability; the effectiveness includes: coverage rate of the constellation to the target area; the constellation completeness comprises the matching degree of the constellation to the user;
s5, calculating a corresponding service quality value by combining the constellation STK simulation data and a corresponding formula;
s6, judging whether the calculated service quality value meets a set threshold value, if so, calculating a target function value according to the constellation, if not, updating an optimized constellation parameter solution vector by adopting a tabu search algorithm, and continuing to return to the operation of the step S4;
and S7, judging whether the maximum iteration times are met, if so, inputting an optimal optimization target value and a corresponding constellation parameter, and if not, returning to the step S4.
2. The method for optimally designing a low earth orbit satellite constellation for guaranteeing service quality according to claim 1, wherein the step S1 of dividing the target area into n areas with equal areas by using an equal longitude and latitude method in a grid point method specifically comprises the steps of:
1) Selecting longitude and latitude coordinates of the lower left corners of all grid points according to the target area;
2) A basic unit of grid points, i.e. the lateral and longitudinal span, is selected and the target area is divided.
3. The method for optimally designing a low-earth-orbit satellite constellation for guaranteeing the service quality according to claim 1, wherein the step S2 of determining the regression cycle and the orbit height of the constellation specifically comprises the following steps:
according to the period of rotation of the earth TeAnd the required regression cycle number n of the low-orbit satellite to determine the regression cycle T of the satelliteSATThe formula expression is shown as formula (1),
Figure FDA0003705769320000021
Teshowing the rotation period of the earth. Calculating the height h of the low-orbit satellite by using the obtained satellite period according to the formula (2);
Figure FDA0003705769320000022
wherein R iseRepresenting the radius of the earth, G representing the constant of gravity, meRepresenting the mass of the earth.
4. The method for optimizing design of low-earth-orbit satellite constellation for guaranteeing service quality as claimed in claim 1, wherein the step S3 initializes a set of better initial solution sets by using genetic algorithm, the genetic algorithm has crossover and mutation operations, which makes the next generation phenotype generated by larger population scale have diversity; applying this to constellation design, a better set of constellation initial solutions can be generated; the solution vector formed by the parameters of the Walker constellation includes NSAT NP i PSAT ASAT]Which respectively represent the number of single orbit satellites, the number of planes of constellation orbits, the orbital inclination, the transmitting power of the satellite antenna, and the equivalent area of the satellite antenna.
5. The method according to claim 1, wherein the setting of the qos threshold in step S4 is to set snr, ber, and rssiThe specific symbols are expressed as
Figure FDA0003705769320000024
6. The method according to claim 5, wherein the specific calculation manner of each service quality indicator in step 5 is as follows:
(1) Threshold BER given bit error rate of low earth orbit satellite network0Calculating the signal-to-noise ratio of the system through the formula (3);
Figure FDA0003705769320000023
erfc (·) denotes the complementary error function, E, respectivelyb/N0Representing the signal-to-noise ratio of the system. EbRepresenting the average bit energy, N0Representing the noise power spectral density.
(2) The survivability is quantified by referring to the natural connectivity of a complex network, and the survivability of the constellation is quantified and optimized by adopting the periodic dynamic natural connectivity, as shown in a formula (4);
Figure FDA0003705769320000031
wherein A isT(G)、
Figure FDA0003705769320000032
TSATNT、TiP (-) respectively represents a connection probability matrix, a natural connectivity of period dynamics, a satellite regression period, the number of time slices divided into the satellite period, the length of each time slice, and a natural connectivityT(G) The element in (a) represents the probability of two nodes remaining connected within a dynamic topology cycle in the satellite network,
Figure FDA0003705769320000035
is AT(G) In a regression period, the connection probability of any two nodes of the constellation can be obtained by obtaining link establishing data of the STK inter-satellite link.
(3) In the constellation regression cycle, the coverage rate CV of the constellation to the target area is weighted statistics of the coverage condition of the satellite constellation to all grid points of the target area, and a specific calculation formula is shown in formula (5).
Figure FDA0003705769320000033
Wherein N isgThe number of the ground grid points is L, the number of the divided time slots is L, if the constellation covers the grid point i at the moment t, yit=1, otherwise yit=0, calculated by passing in constellation parameters and obtaining coverage data of the constellation in the STK to the ground grid points.
7. The method according to claim 5, wherein in step S5, (4) the user matching degree S is defined as a degree of matching of satellite resources to capacity requirements of users in different grid point regions on the ground at time slots 0,1, 2.., L-1, and the value is between 0 and 1, and the greater the user matching degree, i.e., the more matched the user requirements of the satellite constellation to different regions on the ground, the more matched the calculation steps are as follows;
carrying the signal-to-noise ratio into (6) to calculate the downlink rate R of a single satellite;
Figure FDA0003705769320000034
PSATrepresenting the transmission power, G, of the satelliteSATIndicating satellite antenna gain, GrAntenna gain, L, of a userfRepresents path loss, LMIndicating link margin, T indicating noise temperature of system and K indicating boltzmannConstant, the gain of the antenna is calculated by equation (7);
Figure FDA0003705769320000041
wherein etaSATIndicates the efficiency of each antenna, ASATThe equivalent area of the antenna is shown, f is the working frequency of the system, and c is the speed of light;
the capacity of a single satellite, i.e., the number of users that can be served, is expressed by equation (8);
Figure FDA0003705769320000042
r is the downlink data rate, eta, of a single satelliteMAEEfficiency of multiple access modulation for satellite antennas, RuserFor the data rate of the user, the value is 1.554Mbps according to the T1 service standard set by ITU;
therefore, the user matching degree is calculated by the formula (9);
Figure FDA0003705769320000043
wherein, NgNumber of grid points, STF, divided for groundtnCapacity matching condition of the nth grid point in the tth time slot; within any time slot t (t =0,1, 2...., L-1), if the satellite constellation provides a capacity for a mesh point N equal to or greater than the total satellite communication population for that mesh point, the STFtn=1, otherwise, STFtn=0 as shown in formula (10);
Figure FDA0003705769320000044
d (N) represents the number of satellite communication users at the ground grid point, the number of population N (N) passing through the grid point and the proportion of the communication users
Figure FDA0003705769320000045
And the proportion of satellite users
Figure FDA0003705769320000046
The product of (a), i.e. the number of satellite communication users at the nth grid point is
Figure FDA0003705769320000047
CtAnd (n) the capacity provided by the number of visible satellites in the grid point n in the time slot t is calculated according to the capacity of a single satellite and the number of visible satellites in the ground grid point obtained from the STK.
8. The method for optimally designing a low-earth-orbit satellite constellation for ensuring the service quality as recited in claim 1, wherein the objective function value in the step S6 is calculated as follows.
Defining the satellite set S of the satellite constellation providing service to the grid point n in the t time slott={m|θnm≥θmin},θnmIs the elevation angle, theta, of the grid point n to the satellite mminIs the lowest elevation angle for achieving good communication conditions, C in equation (10)t(n) is represented by formula (11);
Figure FDA0003705769320000051
thus, in the whole period, the capacity provided by the satellite constellation for the target area is the sum of the capacities provided in the time slots;
Figure FDA0003705769320000052
the cost-to-efficiency ratio of the objective function value-network is expressed as the ratio of the sum of the capacities to the overhead of constructing the network, i.e. as shown in equation (13);
Figure FDA0003705769320000053
9. the method for optimizing and designing a low earth orbit satellite constellation with guaranteed service quality as claimed in claim 8, wherein the step S6 of optimizing constellation parameters by using a tabu search algorithm is characterized by comprising the following steps:
1) Acquiring an output solution of a genetic algorithm as a current solution of a tabu search algorithm and setting a service quality threshold;
2) Judging whether the optimization target is met and keeping unchanged, if so, outputting a result; if not, entering the next step;
3) Performing neighborhood operation on the current solution to generate a neighborhood solution, and determining a candidate solution from the neighborhood according to the service quality constraint and the objective function value psi;
4) Judging whether the scofflaw principle is satisfied or not for the candidate solution, if so, replacing the current solution with the optimal state solution of the scofflaw principle criterion; replacing the object which enters the tabu table earliest with the solution in the best state;
5) Judging the taboo state of each object corresponding to the candidate solution, selecting the best state corresponding to the non-taboo object in the candidate solution set as the current new solution, and simultaneously replacing the taboo object entering the taboo table earliest by the taboo object corresponding to the current new solution;
6) Judging whether the optimized target value in the algorithm changes, if so, ending the algorithm and outputting an optimized constellation parameter [ N ]SAT NPi PSAT ASAT]And the maximum objective function value Ψ, otherwise go to step 3).
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