A method for optimizing ground track distribution of remote sensing satellites based on genetic algorithm

Abstract

The present application relates to a kind of optimization method of remote sensing satellite ground track distribution based on genetic algorithm, belong to the technical field of remote sensing satellite constellation networking design, solve how to meet the problem of the remote sensing requirement of specific area in prior art.Through obtaining the parameter information of remote sensing satellite to establish remote sensing satellite ground track distribution deployment scheme and determine deployment objective function and constraint condition;Based on this, the ground track deployment scheme of remote sensing satellite is solved using fast non-dominated genetic algorithm, and the optimal deployment scheme is obtained.The revisit time interval of distribution target in region is realized to be smaller, the coverage is larger, the image type is more, and the number of satellite needing ground track adjustment is less.

Description

Technical Field

[0001] This invention relates to the field of remote sensing satellite constellation network design technology, and in particular to an optimization method for the distribution of remote sensing satellite ground trajectories based on genetic algorithms. Background Technology

[0002] In satellite remote sensing missions, specific requirements such as high timeliness, high coverage, and multi-sensor collaboration are often put forward for targets distributed in certain key areas. However, the initial orbits of remote sensing satellites in orbit are often not designed for specific areas, making it difficult to meet the requirements of remote sensing missions. Summary of the Invention

[0003] Based on the above analysis, the embodiments of the present invention aim to provide an optimization method for the ground trajectory distribution of remote sensing satellites based on genetic algorithms, in order to solve the problem that existing remote sensing satellites are designed according to specific regions and are difficult to meet the requirements of remote sensing missions.

[0004] On one hand, embodiments of the present invention provide an optimization method for the distribution of remote sensing satellite ground trajectories based on genetic algorithms, specifically including:

[0005] Obtain parameter information from remote sensing satellites;

[0006] Based on the parameter information, a remote sensing satellite ground trajectory distribution allocation scheme is established, and the allocation objective function and constraints are determined.

[0007] Based on the parameter information, a fast non-dominated genetic algorithm is used to solve the remote sensing satellite ground trajectory allocation scheme to obtain the optimal allocation scheme. The optimal allocation scheme is the number of remote sensing satellites, their identifiers, and the nadir position information of the adjusted satellite ground trajectory obtained based on the objective function and constraints.

[0008] The distribution of remote sensing satellite ground trajectories is optimized based on the optimal allocation scheme.

[0009] Optionally, the parameter information includes: the type and number of satellites to be allocated, and the target orbit parameters of the satellites.

[0010] Optionally, the step of establishing a remote sensing satellite ground trajectory allocation scheme and determining the allocation objective function and constraints based on the parameter information includes:

[0011] Multiple ground trajectory distribution allocation schemes were obtained based on the allocation of satellite types, numbers, and target orbit parameters;

[0012] Obtain the average coverage time interval, regional coverage, minimum image resolution, number of sensor types, and orbital parameters of the remote sensing satellites required for the remote sensing mission;

[0013] Based on the obtained average coverage time interval, regional coverage, minimum image resolution, number of sensor types, and orbital parameters of the remote sensing satellites, the objective function and constraints of the allocation scheme are obtained.

[0014] Optionally, the objective function includes:

[0015] Minimize the maximum revisit interval, maximize target coverage within the mission area, maximize sensor types, and minimize the number of satellites.

[0016] Optionally, the constraints include:

[0017] The target average revisit time interval of the ground trajectory distribution and allocation scheme must be less than or equal to the average coverage time interval;

[0018] The target coverage rate within the mission area of ​​the ground trajectory distribution and allocation scheme must be greater than or equal to the area coverage rate.

[0019] The image resolution of the ground trajectory distribution and allocation scheme must be better than the minimum image resolution.

[0020] The number of sensor types in the ground trajectory distribution and allocation scheme must be greater than or equal to the number of sensor types mentioned above;

[0021] In the ground trajectory distribution and allocation scheme, the adjusted semi-major axis, eccentricity, and inclination of the k-th remote sensing satellite must be equal to the parameters of the satellite's maneuver orbit design.

[0022] Optionally, the step of solving the remote sensing satellite ground trajectory allocation scheme based on a fast non-dominated genetic algorithm to obtain the optimal allocation scheme specifically includes:

[0023] Based on the number of satellites and satellite orbital parameters, a ground trajectory distribution allocation scheme for chromosome expression is obtained;

[0024] Multiple ground trajectory distribution allocation schemes for chromosome expression are constructed to obtain an initial population; the initial population includes chromosomes corresponding to the number of ground trajectory distribution allocation schemes.

[0025] Based on the initial population, a fast non-dominated sorting method is used to perform non-dominated stratification of chromosomes within it;

[0026] Based on non-dominated stratification, the crowding degree of the chromosomes in each non-dominated stratification is calculated to obtain the chromosomes ranked by superiority or inferiority in each stratification.

[0027] Chromosomes ranked by quality in each layer are selected, crossovered, and mutated to generate subpopulations;

[0028] The algorithm continues until the termination condition is met, at which point the solution is output, which is the optimal allocation scheme.

[0029] Optionally, the step of performing non-dominated stratification of chromosomes based on the initial population using a fast non-dominated sorting method includes:

[0030] Based on the objective function of the allocation scheme, each chromosome in the population is stratified to obtain the dominance and non-dominance relationships between chromosomes.

[0031] Based on the dominance and non-dominance relationships between chromosomes, multiple non-dominance strata are obtained according to the non-dominance sorting method.

[0032] Optionally, the crowding degree calculation of the chromosomes in each non-dominated stratum based on non-dominated stratification, to obtain chromosomes ranked by superiority or inferiority, includes:

[0033] The optimal chromosome is obtained by calculating the crowding degree of each chromosome in each non-inferior stratum;

[0034] Construct the crowding distance for each chromosome within the initialization layer;

[0035] Sort each chromosome within the layer based on the objective function;

[0036] Increase the selection advantage of sorted chromosomes;

[0037] The final crowding distance is obtained according to the crowding distance formula, and the crowding distance of chromosomes in all layers is obtained;

[0038] The chromosomes in each non-inferior stratum are ranked according to their superiority or inferiority based on the crowding distance of the chromosomes within each stratum.

[0039] Optionally, the non-dominated layer obtained based on the dominance and non-dominance relationships between chromosomes includes:

[0040] For all chromosomes in a population of size P: P is the number of ground trajectory distribution schemes;

[0041] Let i=1;

[0042] For j = 1, 2, ..., P, and j ≠ i, compare chromosome x. i and x j The relationship of dominance and non-domination between them;

[0043] If no individual x exists j Better than x i Then x i Marked as a non-dominant chromosome;

[0044] Let i = i + 1 and repeat the calculation until all non-dominant chromosomes in the population are found.

[0045] Optionally, based on the number of satellites allocated and their orbital parameters, a ground trajectory distribution allocation scheme for chromosome expression is obtained, including:

[0046] Based on the number of remote sensing satellites involved in the allocation, chromosomes containing the corresponding number of genes are obtained;

[0047] Each gene contains the satellite orbital parameters, which include the longitude and latitude of the nadir trajectory and parameters indicating whether the orbit needs to be maneuvered.

[0048] On the other hand, compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0049] 1. By improving the genetic algorithm and utilizing the orbital maneuvering capability of remote sensing satellites, the ground distribution trajectory of multiple remote sensing satellites is redesigned through algorithm optimization. With less cost of orbital adjustment, the imaging efficiency of multiple satellites on the mission area is improved, thus meeting the remote sensing mission requirements of specific areas.

[0050] 2. By establishing a model for optimizing the distribution of remote sensing satellite ground trajectories with a focus on comprehensive benefits; encoding the optimization problem of remote sensing satellite ground trajectory distribution; and solving the remote sensing satellite ground trajectories using a fast non-dominated genetic algorithm, the output is the number of remote sensing satellites that need to have their ground trajectories adjusted, their identifiers, and the position information of the nadir points of the adjusted satellite ground trajectories.

[0051] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0052] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0053] Figure 1 This is a flowchart of the optimization method for remote sensing satellite ground trajectory distribution based on genetic algorithm according to an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of the coding method for the remote sensing satellite ground trajectory distribution optimization scheme provided in an embodiment of the present invention;

[0055] Figure 3 A flowchart of the remote sensing satellite ground trajectory distribution optimization algorithm provided in an embodiment of the present invention. Detailed Implementation

[0056] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0057] A specific embodiment of the present invention discloses an optimization method for the distribution of remote sensing satellite ground trajectories based on a genetic algorithm, such as... Figure 1 As shown.

[0058] Step S1: Obtain parameter information from remote sensing satellites.

[0059] Specifically, the minimum image resolution, average coverage time interval, regional coverage, number of sensor types, and orbital parameters of the remote sensing satellites required for the remote sensing mission are obtained, and constraints on the objective function are formulated based on the above conditions.

[0060] It should be noted that the satellite's semi-major axis, orbital eccentricity, orbital inclination, and these parameters after the maneuver do not change with the ground trajectory distribution; that is, the parameters after the maneuver are the same as the preset parameter values.

[0061] Step S2: Based on the parameter information of remote sensing satellites, establish a remote sensing satellite ground dispatching scheme and determine the dispatching objective function and constraints.

[0062] Specifically, the objective of solving the remote sensing satellite ground trajectory distribution and allocation problem is defined as follows: comprehensively considering satellite constraints, satellite maneuvering orbit design, and specific mission requirements, to determine the types, quantities, and target orbit parameters of satellites that need to be allocated, and to comprehensively consider the maximum revisit interval, coverage, and sensor type indicators of the satellites for a specific mission area, so as to achieve a high overall efficiency in satellite imaging of the mission area. The symbol definitions for the remote sensing satellite ground trajectory distribution and allocation model are shown in the table below.

[0063]

[0064]

[0065] According to the above symbol definition, the objective function for remote sensing satellite ground trajectory distribution allocation based on the comprehensive benefit priority strategy and oriented towards a specific mission can be expressed as:

[0066] 1) Minimize the maximum revisit interval

[0067] Min(T max (p))=Min[Max(Ae l,j -Ab l,j )] (Formula 1)

[0068] 2) Maximize target coverage within the task area

[0069]

[0070] 3) Maximize sensor type

[0071] Max(V sensor (p)) or Min(1 / V) sensor (p))(Formula 3)

[0072] 4) Minimize the number of satellites

[0073] Min(S(p))(Formula 4)

[0074] The constraints are:

[0075] 1) The target average revisit time interval of the ground trajectory distribution and allocation scheme must be less than or equal to the threshold required by the remote sensing mission, that is, it must be less than or equal to the average coverage time interval of the remote sensing mission:

[0076]

[0077] 2) The target coverage rate within the task area of ​​the ground trajectory distribution and allocation scheme must be greater than or equal to the threshold required by the remote sensing task, that is, it must be greater than or equal to the coverage rate of the area.

[0078] C rate (p)≥C user (Formula 6)

[0079] 3) The image resolution of the ground trajectory distribution and allocation scheme must be greater than or equal to the threshold required by the remote sensing mission, that is, it must be greater than or equal to the minimum image resolution:

[0080] VR min (p)≥VR user (Formula 7)

[0081] 4) The number of sensor types in the ground trajectory distribution and allocation scheme must be greater than or equal to the threshold required by the remote sensing mission, that is, it must be greater than or equal to the number of sensor types:

[0082] VS(p)≥VS user (Formula 8)

[0083] 5) The adjusted semi-major axis, eccentricity, inclination, and right ascension of the ascending node of the k-th remote sensing satellite in the ground trajectory distribution and allocation scheme must be equal to the parameters of the satellite's maneuver orbit design.

[0084] a k =a k0 (Formula 9)

[0085] e k =e k0 (Formula 10)

[0086] i k =i k0 (Formula 11)

[0087] Step S3: Based on the parameter information, the remote sensing satellite ground trajectory adjustment scheme is solved using a fast non-dominated genetic algorithm to obtain the optimal adjustment scheme; the optimal adjustment scheme is the number of remote sensing satellites, their identifiers, and the position information of the ground trajectory nadir points of the adjusted satellites, obtained based on the objective function and constraints.

[0088] Specifically, the solution process mainly includes: encoding the genetic algorithm, generating the initial population, fast non-dominated sorting, calculating the crowding distance of individuals, selection operation, crossover and mutation operation, elite retention strategy, and terminating the solution when the algorithm termination condition is met, so as to obtain the optimal ground trajectory adjustment information.

[0089] The process of solving the remote sensing satellite ground trajectory distribution and allocation scheme is as follows:

[0090] Based on the number of satellites and satellite orbital parameters, a ground trajectory distribution allocation scheme for chromosome expression is obtained;

[0091] Multiple ground trajectory distribution allocation schemes for chromosome expression are constructed to obtain an initial population; the initial population includes chromosomes corresponding to the number of ground trajectory distribution allocation schemes.

[0092] Based on the initial population, a fast non-dominated sorting method is used to perform non-dominated stratification of chromosomes within it;

[0093] Based on non-dominated stratification, the crowding degree of the chromosomes in each stratum is calculated to obtain a new population.

[0094] The new population is selected, crossovered, and mutated to generate subpopulations;

[0095] The algorithm continues until the termination condition is met, at which point the solution is output, which is the optimal allocation scheme.

[0096] Specifically: In this embodiment,

[0097] 1) The ground trajectory distribution allocation scheme is encoded using a genetic algorithm to obtain the encoded ground trajectory distribution allocation scheme related to chromosomes;

[0098] 2) After encoding, an initial parent population containing N individuals is obtained;

[0099] If it is the first generation population, perform fast non-dominated sorting on the population, and then perform selection, crossover, and mutation to generate subpopulations.

[0100] If it is not a first-generation population, merge the population with its parent population;

[0101] The merged population containing 2N individuals is subjected to fast non-dominated sorting, crowding degree is calculated, and the better N individuals among these 2N individuals are selected according to the crowding degree comparison operator to generate a new generation population.

[0102] The new generation of populations is selected, crossovered, and mutated to generate its subpopulations;

[0103] Repeat step 2) until the algorithm termination condition is met, and output the solution.

[0104] S31: Genetic Algorithm Encoding - Remote Sensing Satellite Ground Trajectory Distribution Optimization Algorithm Encoding

[0105] Specifically, the maximum number of remote sensing satellites involved in the calculation is S, and the encoding parameter is flag. k Where k represents the k-th satellite (k∈[1,S]), and k is used to check whether the satellite needs to perform an orbital maneuver at the end, and to place the satellites that need to perform orbital maneuvers into the satellite set (flag). k =0 indicates that the k-th satellite does not need to perform orbital maneuvers, flag k =1 indicates that the k-th satellite needs to perform an orbital maneuver. The total number of satellites requiring orbital maneuvers is obtained as follows: When optimizing the distribution of remote sensing satellite ground trajectories, it is necessary to locate the satellite trajectories, which requires the participation of chromosomes. A total of S satellites are involved in the calculation, and each chromosome consists of S genes g. k (k≤S) composition; each gene g k It contains three parameters g k (lon k ,lat k ,flag k ), where lon k lat k The flag indicates the latitude and longitude of the nadir trajectory of the k-th satellite across a specific location within the mission area. k Indicate whether the k-th satellite needs to perform an orbital maneuver.

[0106] When optimizing the ground trajectory distribution, there are P possible ground trajectory distribution allocation schemes, and each chromosome (also called an individual) has R. j (j≤P) represents a solution to the genetic algorithm (P is a preset parameter of the genetic algorithm, representing the population size, i.e., the population has P chromosomes, or P individuals). Chromosome encoding uses real numbers; one chromosome represents a ground trajectory distribution scheme. Each chromosome in the scheme is as follows: Figure 2 As shown.

[0107] S32: Generate the initial population

[0108] When constructing the initial population, the population contains a total of P solutions (P chromosomes). Among them, a part of the solutions (the number is m, (m < P)) are constructed heuristically. During the construction process, considering the average distribution of the satellite sub-satellite point trajectories, try to avoid the overlap degree of the coverage areas of each trajectory with other trajectories being too large; another part of the solutions (the number is q, (q = P - m)) are generated by a random method, that is, randomly generate S initial positions within the task area, and the value of the encoding parameter flag is: a randomly generated integer [0, 1]. During the process of constructing the initial population, the constraints (Formula 5)-(Formula 11) need to be satisfied. After construction, the m solutions and the q solutions together form the P solutions of the initial population.

[0109] S33: Fast non-dominated sorting

[0110] For the multi-objective optimization problem, the concept of solution domination is as follows: For m objective functions {f1(x), f2(x), …, f M (x)} that need to be minimized, there are two solutions x i and x j . If for all m = 1, 2, …, M, there is f m (x i ) ≤ f m (x j ), and there is at least one m that satisfies f m (x i ) < f m (x j ), that is, the solution x i is better than the solution x j , then the solution x i dominates the solution x j , the solution x j is dominated by the solution x i ; If there is a solution x * , and there is no solution that is better than x * , then the solution x * is called a non-dominated solution or non-dominated individual.

[0111] Before performing the genetic algorithm selection operation, the population of size P is stratified through the non-dominated sorting algorithm. Calculate the objective functions of each individual (i.e., chromosome) in the population through (Formula 1)-(Formula 4), and compare the domination and non-domination relationships between individuals according to the above concept.

[0112] For all individuals in the population of size P

[0113] 1) Let i = 1;

[0114] 2) For j = 1, 2, …, P, and j ≠ i, compare the individuals x i and xj The relationship of dominance and non-domination between them;

[0115] 3) If no individual x exists j Better than x i Then x i Marked as a non-dominant individual;

[0116] 4) Let i = i + 1, go to step 2), until all non-dominant individuals in the population are found.

[0117] The set of all non-dominated individuals obtained using the above method is taken as the first level of non-dominated layer of the population. Then, all non-dominated individuals included in l1 are removed from the population P, and the second level of non-dominated layer is obtained from the remaining individuals according to steps 1)-4). The above operation is repeated until the entire population is divided into layers, and the set of individuals in each layer is a non-dominated set. The layer in which the i-th individual belongs is marked as i. rank , representing the non-dominated order of individual i.

[0118] S34: Crowding Calculation

[0119] According to step S33, non-dominated stratification is performed based on the objective function value, and the crowding distance of each individual within each stratum is calculated. The calculation steps are as follows:

[0120] 1) Initialize the crowding distance L[i] for each individual within the layer. d = 0, where L[i] d This represents the crowding distance of individual i in the Lth layer;

[0121] 2) Sort individuals in the same layer according to the value of the m-th objective function (the objective function is formula 1-formula 4). (Sorting method: For example, in the specific implementation, the first objective function (formula 1) is used as the main factor for calculation and sorting. Since the first objective function is the minimum value, formula 2 and formula 3 should be sorted in reverse order. Similarly, if the second objective function (formula 2) is used as the main factor for calculation and sorting, then formula 1 and formula 4 should be sorted in reverse order.)

[0122] 3) For the sorted individuals, in order to give the two individuals on the sorting edge a selection advantage, a pre-set, sufficiently large number W is given, L[0]. d =L[I] d=W, where 0 and I represent the crowding degree of marginal individuals after the individuals in the Lth layer are sorted according to the mth objective function; the smallest individual in the edge must be the individual with the best objective function, and the best individual has a selection advantage. The largest individual in the edge must be the individual with the worst objective function. Although the individual is poor, it is still included in the genetic selection calculation, which helps to expand the search neighborhood and prevent premature local convergence. Therefore, the two individuals in the edge are artificially assigned a sufficiently large number to make their crowding degree lower, thereby increasing the selection advantage of the two individuals.

[0123] 4) For non-marginal individuals ranked according to the objective function, when calculating the crowding distance,

[0124] The crowding distance for individual i is:

[0125]

[0126] Where Li m Let Max(L) be the m-th objective function value for the i-th individual. m ) and Min(L m Let be the maximum and minimum values ​​of the objective function for the m-th individual in the L-th layer;

[0127] 5) For different objective functions represented by (Formula 1)-(Formula 4), repeat steps 2) to 4) and accumulate the results according to (Formula 12) to obtain the final crowding distance L[i] of individual i in the Lth layer. d ;

[0128] 6) Following the steps above, calculate the crowding distance for all individuals within each layer.

[0129] S35: Crowding Comparison Operator

[0130] After step S33, fast non-dominated sorting, and step S34, crowding distance calculation, each individual i in the population receives two attributes: non-dominated order i. rank and crowded distance i d Define the mechanism: for two individuals i and j in the population, when i rank <j rank or i rank =j rank And i d >j d At that time, individual i is considered superior to individual j.

[0131] S36: Selection, Crossover, and Mutation

[0132] This invention employs common operators in genetic algorithms for selection, crossover, and mutation, requiring no special design. For example, selection can use a binary tournament strategy, randomly selecting two individuals and comparing their strengths and weaknesses based on crowding, choosing the superior individual. Crossover can use random crossover points, swapping the portions of the two parent individuals located on either side of the crossover point. Mutation can use a normal distribution mutation suitable for real-number encoding, randomly selecting a gene g from the chromosome. k (lon k ,lat k ,flag k ), respectively generating a mean of lon k lat k Two random numbers that conform to a normal distribution (variance has no special requirements) are used to replace lon. k and at k .

[0133] S37: Elite Maintenance

[0134] The elite strategy involves preserving the best individuals from the parent generation for direct transmission to the offspring. The methods employed are:

[0135] 1) Parent generation P t and offspring Q t All individuals combine to form a unified population P t =P t ∪Q t P t The number of individuals is 2N;

[0136] 2) Population P t A fast non-dominated sorting algorithm is used, and the local crowding distance of each individual is calculated. Individuals are then sorted according to their crowding degree by comparing their merits. Individuals are selected one by one until the number of individuals reaches N, at which point a new parent population P is formed. t+1 ;

[0137] 3) Based on this, a new round of selection, crossover, and mutation begins, forming a new offspring population Q. t+1 .

[0138] Step S4: Optimize the ground-based estimated distribution of remote sensing satellites based on the optimal allocation scheme.

[0139] A termination method combining maximum iteration count or runtime is adopted. Output is generated when the maximum iteration count reaches a preset value or the runtime reaches a preset value. Each chromosome in the final output has a solution, and the optimal solution among these solutions is obtained, which is the optimal allocation scheme. That is, the output includes the number of remote sensing satellites that need to be adjusted on the ground, their identifiers, and the position information of the ground nadir points of the adjusted satellite trajectories, thus obtaining the best ground trajectory allocation scheme.

[0140] This invention provides a method for optimizing the distribution of remote sensing satellite ground trajectories based on a fast non-dominated genetic algorithm. For a given remote sensing observation area, the distribution of remote sensing satellite ground trajectories is optimized and adjusted through orbital maneuvers, resulting in a shorter revisit time interval for targets distributed within the area, a larger coverage, a greater variety of image types, and a smaller number of satellites requiring ground trajectory adjustments.

[0141] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0142] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. An optimization method for remote sensing satellite ground trajectory distribution based on genetic algorithm, characterized in that, Specifically, it includes: Obtain parameter information from remote sensing satellites; The parameter information includes: the minimum image resolution required for the remote sensing mission, the average coverage time interval, the regional coverage rate, the number of sensor types, the orbital parameters of the remote sensing satellites, the types and number of satellites to be deployed, and the target orbital parameters of the satellites. Based on the parameter information, a remote sensing satellite ground trajectory distribution allocation scheme is established and the allocation objective function and constraints are determined, including: obtaining multiple ground trajectory distribution allocation schemes based on the type and number of satellites and the target orbit parameters of the satellites; and obtaining the objective function and constraints of the allocation scheme based on the obtained average coverage time interval, regional coverage, minimum image resolution, number of sensor types, and orbit parameters of the remote sensing satellites. The objective functions include: minimizing the maximum revisit interval, maximizing target coverage within the mission area, maximizing sensor type, and minimizing the number of satellites; The constraints include: the target average revisit time interval of the ground trajectory distribution and allocation scheme must be less than or equal to the average coverage time interval; the target coverage rate within the mission area of ​​the ground trajectory distribution and allocation scheme must be greater than or equal to the area coverage rate; the image resolution of the ground trajectory distribution and allocation scheme must be better than the minimum image resolution; the number of sensor types in the ground trajectory distribution and allocation scheme must be greater than or equal to the number of sensor types; and the adjusted semi-major axis, eccentricity, and inclination of the k-th remote sensing satellite in the ground trajectory distribution and allocation scheme must be equal to the parameters of the satellite's maneuvering orbit design. Based on the parameter information, a fast non-dominated genetic algorithm is used to solve the remote sensing satellite ground trajectory allocation scheme. The optimal allocation scheme is obtained when the maximum number of iterations reaches a preset value or the running time reaches a preset value. The optimal allocation scheme is the number of remote sensing satellites, their identifiers, and the nadir position information of the adjusted satellite ground trajectory obtained based on the objective function and constraints. The distribution of remote sensing satellite ground trajectories is optimized based on the optimal allocation scheme.

2. The optimization method for remote sensing satellite ground trajectory distribution based on genetic algorithm according to claim 1, characterized in that, The method of solving the remote sensing satellite ground trajectory allocation scheme based on the fast non-dominated genetic algorithm to obtain the optimal allocation scheme specifically includes: Based on the number of satellites and satellite orbital parameters, a ground trajectory distribution allocation scheme for chromosome expression is obtained; Multiple ground trajectory distribution allocation schemes for chromosome expression are constructed to obtain an initial population; the initial population includes chromosomes corresponding to the number of ground trajectory distribution allocation schemes. Based on the initial population, a fast non-dominated sorting method is used to perform non-dominated stratification of chromosomes within it; Based on non-dominated stratification, the crowding degree of the chromosomes in each non-dominated stratification is calculated to obtain the chromosomes ranked by superiority or inferiority in each stratification. Chromosomes ranked by quality in each layer are selected, crossovered, and mutated to generate subpopulations; The algorithm continues until the termination condition is met, at which point the solution is output, which is the optimal allocation scheme.

3. The optimization method for remote sensing satellite ground trajectory distribution based on genetic algorithm according to claim 2, characterized in that, The method of performing non-dominated stratification of chromosomes based on the initial population using a fast non-dominated sorting method includes: Based on the objective function of the allocation scheme, each chromosome in the population is stratified to obtain the dominance and non-dominance relationships between chromosomes. Based on the dominance and non-dominance relationships between chromosomes, multiple non-dominance strata are obtained according to the non-dominance sorting method.

4. The optimization method for remote sensing satellite ground trajectory distribution based on genetic algorithm according to claim 2 or 3, characterized in that, The crowding degree of the chromosomes in each non-dominated stratum is calculated based on the non-dominated stratum, and the chromosomes are obtained after ranking by superiority or inferiority, including: The optimal chromosome is obtained by calculating the crowding degree of each chromosome in each non-inferior stratum; Construct the crowding distance for each chromosome within the initialization layer; Sort each chromosome within the layer based on the objective function; Increase the selection advantage of sorted chromosomes; The final crowding distance is obtained according to the crowding distance formula, and the crowding distance of chromosomes in all layers is obtained; The chromosomes in each non-inferior stratum are ranked according to their superiority or inferiority based on the crowding distance of the chromosomes within each stratum.

5. The optimization method for remote sensing satellite ground trajectory distribution based on genetic algorithm according to claim 4, characterized in that, The non-dominated layer obtained based on the dominance and non-dominance relationships between chromosomes includes: For all chromosomes in a population of size P: P is the number of ground trajectory distribution schemes; Let i=1; for ,and Comparing chromosomes and The relationship of dominance and non-domination between them; If no individual exists Superior ,but Marked as a non-dominant chromosome; Let i = i + 1 and repeat the calculation until all non-dominated chromosomes in the population are found.

6. The optimization method for remote sensing satellite ground trajectory distribution based on genetic algorithm according to claim 2, characterized in that, Based on the number of satellites and their orbital parameters, a ground trajectory distribution allocation scheme for chromosome expression is obtained, including: Based on the number of remote sensing satellites involved in the allocation, chromosomes containing the corresponding number of genes are obtained; Each gene contains the satellite orbital parameters, which include the longitude and latitude of the nadir trajectory and parameters indicating whether the orbit needs to be maneuvered.

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