Moving target-oriented double-star observation improved genetic task planning method

Abstract

The invention discloses a moving target-oriented double-star observation improved genetic task planning method, and belongs to the technical field of aerospace. The implementation method comprises the following steps: establishing a moving target-oriented double-star observation task planning model, establishing a decomposition principle according to a constraint condition, decomposing an observation task, and changing a continuous planning problem into a discrete planning problem. Constructing a collaborative double-path genetic algorithm to solve a planning problem, and for a task coding mode, adopting satellite position sorting coding to avoid task conflicts in crossing and variation processes; iterating the initial population through a collaborative double-path genetic algorithm, differentiating an iterative path 1 and a path 2, and optimizing the population of the path 1 by using the population of the path 2; the method comprises the following steps of: designing a plurality of groups of collaborative sub-reference optimization strategies, constructing a nonlinear self-adaptive sine and cosine crossover operator and a self-adaptive mutation operator, forming a moving target-oriented double-star observation improved genetic task planning method, and realizing continuous observation and tracking of a moving target according to the improved genetic task planning method.

Description

Technical Field

[0001] This invention relates to an improved genetic mission planning method for dual-satellite observation of moving targets, belonging to the field of aerospace technology. Background Technology

[0002] In today's rapidly developing high-tech society, mobile target tracking and analysis have penetrated into all walks of life. From military defense to intelligent transportation systems, and then to logistics management and public safety, how to accurately identify, track, and predict the behavior of these dynamic targets has become an important research topic for improving efficiency, safety, and decision-making quality.

[0003] Satellites play a crucial role in dynamic target tracking due to their high-precision positioning and long-term continuous observation capabilities. Especially with the support of dual-satellite observation technology, using two satellites to observe targets from different perspectives can significantly improve positioning accuracy and motion trajectory capture capabilities. However, with the increasing number of satellites and the diversification of mission requirements, achieving rational satellite scheduling under complex time constraints and limited resources is a key challenge in mission planning. In dual-satellite observation mode, mission planning needs to ensure effective collaboration between satellites to avoid resource waste. Scientific and rational mission planning can effectively improve the utilization efficiency of satellite resources, ensure the successful completion of observation missions, and provide strong support for the precise positioning and real-time tracking of dynamic targets.

[0004] Current mobile target mission planning problems face challenges due to the high dynamics of observations and complex observation constraints. To address these challenges, an improved genetic mission planning method for moving target dual-satellite observations is proposed. A mobile target dual-satellite observation mission planning model is established, and a collaborative dual-path genetic algorithm framework is constructed. After iterative differentiation of the initial population, the population for path 1 performs a global search, while the population for path 2 performs a local search. After each iteration, feedback information is exchanged between the populations of different paths, with the population of path 2 assisting the population of path 1 in optimization. Based on the model's optimization indices, multiple sets of collaborative exponential optimization strategies are designed, and nonlinear adaptive sine and cosine crossover operators and adaptive mutation operators are constructed. Finally, the collaborative dual-path genetic algorithm is used to obtain the planning results. Summary of the Invention

[0005] To address the challenges of high dynamics and complex constraints in observing moving targets, this invention aims to provide an improved genetic task planning method for dual-satellite observation of moving targets. A dual-satellite observation task planning model is established, and a decomposition principle is established based on the constraints to decompose the observation task, transforming the continuous planning problem into a discrete one. A cooperative dual-path genetic algorithm is proposed to solve the planning problem. For task encoding, satellite position sorting is used to avoid task conflicts during crossover and mutation processes. Through the cooperative dual-path genetic algorithm, the initial population is iterated and then differentiated into path 1 and path 2, with the population of path 2 assisting the population of path 1 for optimization. Multiple sets of cooperative exponential optimization strategies are designed, and nonlinear adaptive sine and cosine crossover operators and adaptive mutation operators are constructed to form an improved genetic task planning method for dual-satellite observation of moving targets, enabling continuous observation and tracking of moving targets.

[0006] The objective of this invention is achieved through the following technical solution.

[0007] The improved genetic mission planning method for binary star observation oriented towards moving targets disclosed in this invention includes the following steps:

[0008] Step 1: Based on the principle of observation task decomposition, preprocess the time window of the given satellite for the moving target to realize the transformation from a continuous task planning optimization problem to a discrete combinatorial optimization problem.

[0009] The principle of decomposing observation tasks follows these constraints: First, each subtask is the smallest unit of the task and cannot be further divided. Second, the observation time of each subtask cannot be less than L. min Third, the observation time of the sub-task cannot exceed L. max After decomposing the time window based on the above constraints, the mission planning problem is transformed into a two-satellite selection problem in a discrete time dimension. That is, for each sub-task, within the allocated time window, two satellites are selected from the candidate satellite set to perform observations, realizing the transformation from a continuous mission planning optimization problem to a discrete combinatorial optimization problem.

[0010] Step 2: Introduce observation variables, set multiple constraints to meet the feasibility requirements of mission scheduling, and establish a dual-satellite observation mission planning model oriented towards moving targets.

[0011] The observed variables are:

[0012] The observation task contains m observation targets, forming the target set TSet:

[0013] TSet = {T i |i=1,2,3,…,m} (1)

[0014] Target T iThe observation task information includes start time, end time, observation duration, set of subtasks, and number of subtasks:

[0015] T i ={ST i ,ET i Period i SatSet i ,STNum i} (2)

[0016] Target T i The corresponding collection of subtasks is SubSet i :

[0017] SubSet i ={SubT i,j |j=1,2,3,…,STNum i} (3)

[0018] For subtask SubT i,j This includes start time, end time, observation duration, available satellite set, and number of available satellites:

[0019] SubT i,j ={ST i,j ,ET i,j Period i,j SatSet i,j ,SatNum i,j} (4)

[0020] For satellites in the optional satellite set SatSet i,j Represented as:

[0021]

[0022] In the formula, This represents the optional satellite number for the j-th subtask of the i-th target.

[0023] The observation constellation consists of z observation satellites, forming a satellite set called SatSet:

[0024] SatSet = {S p |p=1,2,3,…,z} (6)

[0025] Constraints: Satellites must complete their missions within a window of effective line-of-sight with the target. Each satellite can only observe one mission at a time. Each sub-mission requires observation by two satellites simultaneously. A target is considered fully observed only when all its sub-missions are completed.

[0026] Step 3: Encode the subtasks decomposed from the task in Step 1 using a two-bit sequential encoding method.

[0027] With n subtasks, the chromosome length is 2^n. Each gene position is filled with integers according to the order of the target and subtasks. Every two gene positions are treated as a whole, called a bigenomic position. There are a total of n bigenomic positions, the same as the number of subtasks. The bigenomic position does not directly represent the satellite number, but rather identifies the selection order of the satellite chosen for the corresponding subtask in the set of available satellites, i.e., it is numbered with k. This encoding strategy ensures that the chromosome is a valid solution during the crossover and mutation processes of the genetic algorithm, unconstrained by the available satellite numbers of the subtasks, simplifying operation and effectively improving the algorithm's execution efficiency.

[0028] Step 4: Based on the observed variables and constraints in Step 2, construct the objective function for the mission planning problem by taking the number of satellite switching times and the maximum load of a single satellite as optimization objectives.

[0029] The number of satellite switching operations described in step four is:

[0030] Using a dual-satellite observation method, the observation period is divided into multiple sub-task periods throughout the entire observation process. When different satellites are used for the same target, there will be a switching of the observation satellites. The number of switching is expressed by the following formula:

[0031]

[0032] condition1:S i,j,1 =S i,j+1,1 andS i,j,2 =S i,j+1,2

[0033] condition2:(S i,j,1 ≠S i,j+1,1 andS i,j,2 =S i,j+1,2 )or(S i,j,1 =S i,j+1,1 andS i,j,2 ≠S i,j+1,2 )

[0034] condition3:S i,j,1 ≠S i,j+1,1 andS i,j,2 ≠S i,j+1,2

[0035] In the formula, n i,j This refers to the total number of satellite switching operations in the j-th subtask of target i. i,j,1 S refers to the first satellite used by target i in the j-th observation sub-task. i,j,2 Refers to the second satellite.

[0036] For two consecutive observation subtasks targeting the same target, the number of switching operations is 0 if they use the same satellite, 1 if they use only one satellite, and 2 if they use two different satellites.

[0037] Therefore, within a mission planning cycle, the total number of satellite handovers is equal to the sum of the number of satellite handovers for each sub-mission, as expressed below:

[0038]

[0039] In the formula, SwitchNum represents the total number of satellite switches within a mission planning cycle, and STNum... i This represents the number of subtasks for each objective, where m is the total number of objectives.

[0040] The maximum load of a single satellite as described in step four is:

[0041] Calculate the actual observation duration for each satellite, which is the sum of the observation times for the sub-tasks corresponding to the selected satellites:

[0042]

[0043] Calculate the actual observation duration for each satellite; the maximum payload for a single satellite is the largest payload in the total set of satellite observation durations.

[0044] SatTime max =max{SatTime p |p=1,2...z} (10)

[0045] z refers to the total number of satellites involved in mission planning within the entire constellation.

[0046] The optimization objective function described in step four is:

[0047] The optimization goal is to minimize the number of satellite handovers and the maximum load on a single satellite.

[0048] min[SwitchNum,SatTime max (11)

[0049] For SwitchNum and SatTime max After weighting, the objective function is obtained as follows:

[0050]

[0051] Step 5: A collaborative dual-path genetic algorithm is used to solve the task planning model constructed in Step 2, in order to achieve coordinated optimization of the various indicators in the objective function of Step 4. The specific implementation method is as follows:

[0052] 1) Initialize the population using a random method.

[0053] 2) The initial population is iterated q times according to the steps of the standard genetic algorithm to obtain a better population.

[0054] 3) Following the method of taking the current superior population as path 1 and selecting the top Num% of individuals with high fitness to form path 2, the superior population is divided into path 1 and path 2.

[0055] 4) Randomly divide the population from either path 1 or path 2 into two equal-sized populations, namely population 1 and population 2. In population 1, sort the individuals in ascending order according to the number of satellite switches (SwitchNum) from step 4, and select the top Num1% of individuals with the fewest satellite switches to obtain subpopulation 1. In population 2, sort the individuals according to the maximum single-satellite load (SatTime) from step 4. max Sort the data in ascending order and select individuals with smaller maximum single-star loads to obtain subpopulation 2. It is necessary to ensure that the number of individuals selected in the two subpopulations is the same.

[0056] Based on the number of single-star switches in step four (SwitchNum(x)) i ) and single-star maximum load SatTime max (x i ), generate two subpopulations from population 1 and population 2:

[0057]

[0058] P1 is a set of individuals with fewer satellite switching times, and P2 is a set of individuals with smaller maximum single-satellite load.

[0059] 5) Select one individual from subpopulation 1 and one individual from subpopulation 2 as parent generation 1 and parent generation 2, respectively.

[0060]

[0061] That is, parent generation 1x (1) Randomly select from P1, parent generation 2x (2) Select randomly from P2.

[0062] 6) Perform uniform crossover operation based on the adaptive crossover operator of path 1 or path 2.

[0063] For the population along path 1, an adaptive sinusoidal crossover operator is used, and its calculation formula is as follows:

[0064]

[0065] For the population along path 2, an adaptive cosine crossover operator is used. The formula is as follows:

[0066]

[0067] Among them, P cmax For the maximum crossover probability, P cmin Let f' be the number of parents with higher fitness among the two individuals to be crossovered, representing the minimum crossover probability. max f is the value with the highest fitness in the population. avg This represents the average fitness of the population.

[0068] 7) After crossover generates new individuals, perform replacement mutation on the population and establish an adaptive mutation operator, as shown in the following formula:

[0069]

[0070] Among them, mRate1 is the lower mutation rate, mRate2 is the higher mutation rate, and bestf is the highest fitness value in the parent generation.

[0071] 8) The iteration ends when path 1 and path 2 complete their iterations. After each iteration, a feedback exchange strategy is constructed to exchange individuals in the two populations.

[0072] 9) After each iteration, Path 2 selects the top Num2 individuals with high fitness from the current population of Path 1 and incorporates them into itself. At the same time, the high-quality offspring generated by Path 2 after local optimization replace the individuals with the lowest fitness in Path 1 in equal numbers, realizing the synergistic optimization of global search and local search, improving the overall fitness of the population, and thus improving the optimization level and solution efficiency of the binary star observation improved genetic task planning method.

[0073] Beneficial effects:

[0074] 1. The improved genetic task planning method for dual-satellite observation oriented to moving targets disclosed in this invention transforms the continuous observation task planning problem into a discrete observation task planning problem by performing trajectory prediction on the moving target and decomposing the target task. It simplifies the complex planning problem between satellites and targets into the problem of selecting the corresponding observation satellite for each sub-task that meets the constraints, thereby reducing the complexity of the improved genetic task planning problem for dual-satellite observation.

[0075] 2. The improved genetic task planning method for dual-star observation oriented towards moving targets disclosed in this invention adopts a cooperative dual-path framework, which differentiates the initial population into path 1 and path 2 after iteration, with path 2 cooperating to assist path 1, thereby improving the optimization level and solution efficiency of the improved genetic task planning method for dual-star observation.

[0076] 3. The improved genetic task planning method for dual-satellite observation oriented towards moving targets disclosed in this invention constructs two subpopulations with better performance from the population based on two indicators: the number of satellite switching and the maximum load of a single satellite. Individuals are randomly selected from each subpopulation as parents, which improves the multi-objective adaptability of individual selection, enhances population diversity, and thus improves the global optimization capability of the improved genetic task planning method for dual-satellite observation.

[0077] 4. The improved genetic task planning method for dual-star observation oriented towards moving targets disclosed in this invention maintains the diversity of the population performing global search by establishing differentiated nonlinear adaptive crossover operators, prevents evolutionary stagnation, and reduces the probability of high-quality solutions being destroyed in the local search population, thereby improving the optimization level and solution efficiency of the improved genetic task planning method for dual-star observation.

[0078] 5. The improved genetic task planning method for dual-star observation oriented towards moving targets disclosed in this invention, by establishing differentiated adaptive mutation operators, retains high-quality individuals in the local search population while maintaining the diversity of the global search population. This not only prevents the algorithm from converging to a local optimum prematurely, but also promotes the exploration of the improved genetic task planning method for dual-star observation in a wider solution space, thereby improving the optimization level and solution efficiency of the improved genetic task planning method for dual-star observation. Attached Figure Description

[0079] Figure 1 This is a flowchart of the improved genetic mission planning method for binary star observation oriented towards moving targets disclosed in this invention.

[0080] Figure 2 This is a diagram illustrating the encoding method of the improved genetic task planning method for dual-star observation oriented towards moving targets disclosed in this invention under a dual-population genetic algorithm.

[0081] Figure 3 This is a comparison chart of the fitness values ​​of the improved genetic task planning method for moving target dual-star observation disclosed in this invention, and the conventional genetic algorithm and the multi-starting-point chaotic simulated annealing algorithm.

[0082] Figure 4 This is a comparison chart of the number of satellite switching operations between the improved genetic mission planning method for dual-satellite observation of moving targets disclosed in this invention and the conventional genetic algorithm and the multi-starting-point chaotic simulated annealing algorithm.

[0083] Figure 5 This is a comparison chart of the maximum single-star load of the improved genetic mission planning method for dual-star observation of moving targets disclosed in this invention, and the conventional genetic algorithm and the multi-starting-point chaotic simulated annealing algorithm.

[0084] Figure 6This is a diagram showing the optimal task planning scheme obtained by solving the improved genetic task planning method for moving target binary star observation disclosed in this invention. Detailed Implementation

[0085] To better illustrate the purpose and advantages of the present invention, the invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0086] To verify the feasibility of the method, the following mission background was set: the Walker constellation consists of 24 satellites, evenly distributed in three orbits at an altitude of 1600 km and an inclination of 102.49°, with 3 moving targets and a planning duration of 900 seconds.

[0087] like Figure 1 As shown in the figure, this embodiment discloses an improved genetic mission planning method for binary star observations targeting moving targets. The specific implementation steps are as follows:

[0088] Step 1: Based on the principle of observation task decomposition, preprocess the time window of the given satellite for the moving target to realize the transformation from a continuous task planning optimization problem to a discrete combinatorial optimization problem;

[0089] The principle of decomposing observation tasks follows these constraints: First, a subtask is the smallest unit of the task and cannot be further divided; second, the observation time of a subtask cannot be less than L. min Third, the observation time of the sub-task cannot exceed L. max After decomposing the time window based on the above constraints, the task planning problem is transformed into a two-satellite selection problem in a discrete time dimension; that is, for each sub-task, two satellites are selected from the candidate satellite set to perform observations within the allocated time window, realizing the transformation from a continuous task planning optimization problem to a discrete combinatorial optimization problem.

[0090] The initial state parameters of the three moving targets in this simulation are shown in Table 1, where x, y, and z are the target positions, and v is the initial state parameter. x v y v z It is the speed of the target.

[0091] Table 1 Initial state parameters of the moving target

[0092]

[0093] The moving target observation task was decomposed according to the constraint-based task decomposition rule, and the decomposition results are shown in the table below. The observation task of target 1 was divided into 9 segments, and each sub-task segment had no fewer than 10 selectable satellites; the observation task of target 2 was divided into 11 segments, and each sub-task segment had no fewer than 5 selectable satellites; the observation task of target 3 was divided into 11 segments, and each sub-task segment had no fewer than 8 selectable satellites.

[0094] Table 2. Target 1 Observation Task Breakdown Table

[0095]

[0096] Table 3. Target 2 Observation Task Breakdown Table

[0097]

[0098] Table 4. Target 3 Observation Task Breakdown Table

[0099]

[0100]

[0101] Step 2: Introduce observation variables, set multiple constraints to meet the feasibility requirements of mission scheduling, and establish a dual-satellite observation mission planning model oriented towards moving targets.

[0102] The observed variables are:

[0103] The observation task contains m observation targets, forming the target set TSet:

[0104] TSet = {T i |i=1,2,3,…,m} (1)

[0105] Target T i The observation task information includes start time, end time, observation duration, set of subtasks, and number of subtasks:

[0106] T i ={ST i ,ET i Period i SatSet i ,STNum i} (2)

[0107] Target T i The corresponding collection of subtasks is SubSet i :

[0108] SubSet i ={SubT i,j|j=1,2,3,…,STNum i} (3)

[0109] For subtask SubT i,j This includes start time, end time, observation duration, available satellite set, and number of available satellites:

[0110] SubT i,j ={ST i,j ,ET i,j Period i,j SatSet i,j ,SatNum i,j} (4)

[0111] For satellites in the optional satellite set SatSet i,j Represented as:

[0112]

[0113] In the formula, Indicates the optional satellite number for the j-th subtask of the i-th objective;

[0114] The observation constellation consists of z observation satellites, forming a satellite set called SatSet:

[0115] SatSet = {S p |p=1,2,3,…,z} (6)

[0116] Constraints: Satellites must complete their missions within a window of effective line of sight with the target; each satellite can only observe one mission at a time; each sub-mission requires observation by two satellites simultaneously; a target is considered fully observed only when all its sub-missions are completed.

[0117] Step 3: Encode the subtasks decomposed from the task in Step 1 using a two-bit sequential encoding method.

[0118] With n subtasks, the chromosome length is 2n. Each gene position is filled with integers according to the order of the target and subtasks. Every two gene positions are treated as a whole and called a bigenomic position. There are a total of n bigenomic positions, which is the same as the number of subtasks. The setting of the bigenomic position does not directly represent the satellite number, but rather identifies the selection order of the satellite selected by the corresponding subtask in the set of available satellites, i.e., it is numbered with k. This encoding strategy ensures that the chromosome is an effective solution in the crossover and mutation process of the genetic algorithm, without being constrained by the available satellite numbers of the subtasks. It is easy to operate and can effectively improve the execution efficiency of the algorithm.

[0119] like Figure 2As shown in the figure, the optional satellites are coded as [123514], including three sub-tasks. Furthermore, the first sub-task's optional satellite set contains three satellites, namely S... 14 S 16 and S 22 The first number "1" in the first subtask's bipartite position indicates the first satellite S in the optional satellite set. 14 The second number "2" indicates the second satellite S. 16 Similarly, the second subtask's optional satellite set contains 5 satellites, and the first number "3" in its double-locus sequence indicates the 3rd satellite S in the optional satellite set. 22 The second number "5" indicates the 5th satellite S. 38 And so on.

[0120] Step 4: Based on the observed variables and constraints in Step 2, construct the objective function for the mission planning problem by taking the number of satellite switching times and the maximum load of a single satellite as optimization objectives.

[0121] The number of satellite switching operations described in step four is:

[0122] Using a dual-satellite observation method, the observation period is divided into multiple sub-task periods throughout the entire observation process. When different satellites are used for the same target, there will be a switching of the observation satellites. The number of switching is expressed by the following formula:

[0123]

[0124] condition1:S i,j,1 =S i,j+1,1 andS i,j,2 =S i,j+1,2

[0125] condition2:(S i,j,1 ≠S i,j+1,1 andS i,j,2 =S i,j+1,2 )or(S i,j,1 =S i,j+1,1 andS i,j,2 ≠S i,j+1,2 )

[0126] condition3:S i,j,1 ≠S i,j+1,1 andS i,j,2 ≠S i,j+1,2

[0127] In the formula, n i,j S refers to the total number of satellite switching operations in the j-th subtask of target i; i,j,1S refers to the first satellite used by target i in the j-th observation sub-task. i,j,2 Refers to the second satellite;

[0128] For two consecutive observation subtasks of the same target, if they use the same satellite, the number of switching is 0; if only one satellite is the same, the number of switching is 1; if both satellites are different, the number of switching is 2.

[0129] Therefore, within a mission planning cycle, the total number of satellite handovers is equal to the sum of the number of satellite handovers for each sub-mission, as expressed below:

[0130]

[0131] In the formula, SwitchNum represents the total number of satellite switches within a mission planning cycle, and STNum... i This indicates the number of subtasks for each objective, where m is the total number of objectives.

[0132] The maximum load of a single satellite as described in step four is:

[0133] Calculate the actual observation duration for each satellite, which is the sum of the observation times for the sub-tasks corresponding to the selected satellites:

[0134]

[0135] The actual observation duration of each satellite is calculated, and the maximum payload of a single satellite is the largest of the total observation durations of all satellites.

[0136] SatTime max =max{SatTime p |p=1,2...z} (10)

[0137] Here, z refers to the total number of satellites involved in mission planning throughout the entire constellation;

[0138] The optimization objective function described in step four is:

[0139] The optimization goal is to minimize the number of satellite handovers and the maximum load on a single satellite.

[0140] min[SwitchNum,SatTime max (11)

[0141] For SwitchNum and SatTime max After weighting, the objective function is obtained as follows:

[0142]

[0143] Step 5: Use a collaborative dual-path genetic algorithm to solve the task planning model constructed in Step 2, so as to achieve coordinated optimization of various indicators in the objective function in Step 4.

[0144] 1) First, the population is initialized using a random method;

[0145] 2) The initial population is iterated q times according to the steps of the standard genetic algorithm to obtain a better population;

[0146] 3) Following the method of taking the current superior population as path 1 and selecting the top 20% of individuals with higher fitness to form path 2, the superior population is divided into path 1 and path 2.

[0147] 4) Randomly divide the population from either path 1 or path 2 into two equal-sized populations, namely population 1 and population 2. In population 1, sort the individuals in ascending order according to the number of satellite switches (SwitchNum) from step 4, and select the top 50% of individuals with the fewest satellite switches to obtain subpopulation 1. In population 2, sort the individuals according to the maximum single-satellite load (SatTime) from step 4. max Sort the data in ascending order and select individuals with smaller maximum single-star loads to obtain subpopulation 2. It is necessary to ensure that the number of individuals selected in the two subpopulations is the same.

[0148] Based on the number of single-star switches in step four (SwitchNum(x)) i ) and single-star maximum load SatTime max (x i ), generate two subpopulations from population 1 and population 2:

[0149]

[0150] Wherein, P1 is a set of individuals based on the number of satellite handovers, and P2 is a set of individuals based on the maximum load of a single satellite.

[0151] 5) Select one individual from subpopulation 1 and one individual from subpopulation 2 as parent generation 1 and parent generation 2 respectively;

[0152]

[0153] That is, parent generation 1x (1) Randomly select from P1, parent generation 2x (2) Randomly select from P2;

[0154] 6) Perform uniform crossover operation based on the adaptive crossover operator of path 1 or path 2;

[0155] For the population along path 1, an adaptive sinusoidal crossover operator is used, and its calculation formula is as follows:

[0156]

[0157] For the population along path 2, an adaptive cosine crossover operator is used; the formula is as follows:

[0158]

[0159] Among them, P cmax For the maximum crossover probability, P cmin To minimize the crossover probability, for the population along path 1, P cmax Take 0.9, P cmin Taking 0.7, for the population along path 2, P cmax P is 0.7. cmin The value is 0.5. f′ is the number of the parent individuals with higher fitness among the two parent individuals to be crossbred. max f is the value with the highest fitness in the population. avg The average fitness of the population;

[0160] 7) After crossover generates new individuals, perform replacement mutation on the population and establish an adaptive mutation operator, as shown in the following formula:

[0161]

[0162] Where mRate1 is the lower mutation rate, mRate2 is the higher mutation rate, and bestf is the highest fitness value among the parents. For the population of path 1, mRate1 is 0.1 and mRate2 is 0.2; for the population of path 2, mRate1 is 0.05 and mRate2 is 0.1.

[0163] 8) When path 1 and path 2 complete the iteration, the current iteration round ends; after each iteration round, design a feedback exchange strategy to exchange individuals in the two populations.

[0164] After each iteration, Path 2 selects the top 20% of individuals with high fitness from the current population of Path 1 and incorporates them into itself; at the same time, the high-quality offspring generated by Path 2 after local optimization replace the individuals with the lowest fitness in Path 1 in equal numbers, realizing the coordinated optimization of global search and local search, and improving the overall fitness of the population.

[0165] Step 6: Repeat Step 5 until termination. The termination condition is reaching the preset number of iterations of 500. The obtained solution for the improved genetic task planning for the dual-star observation of the moving target is the planning sequence corresponding to the chromosome with the largest fitness value in the path 1 population, which is the planning solution for the moving target observation task.

[0166] Under the same problem context, the conventional genetic algorithm (GA), the multi-starting-point chaotic simulated annealing algorithm (MS-CSA), and the cooperative dual-path genetic algorithm (CDP-GA) disclosed in this invention are compared. The simulation iteration diagram is shown below. Figure 3-5 As shown in Table 5, the comparison results are as follows. Simulation results of the three algorithms show that CDP-GA outperforms GA and MS-CSA in all indicators. Specifically, the fitness value of CDP-GA reaches 1.9802, which is about 23.0% and 39.3% higher than that of GA (1.6110) and MS-CSA (1.4212), respectively. In terms of satellite handover times, CDP-GA only requires 21 handovers, compared to 33 for GA and 38 for MS-CSA, representing reductions of 36.4% and 44.7%, respectively, significantly reducing system energy consumption and load. For the maximum load on a single satellite, CDP-GA's value is 513.2, lower than GA's 681.5 and MS-CSA's 686.5, with reductions of 24.7% and 25.3%, respectively, indicating that CDP-GA has stronger optimization capabilities in load balancing. Although CDP-GA takes slightly longer to run than GA, its computational cost is similar to that of MS-CSA, and it demonstrates a significant advantage in solution quality, fully showcasing the good balance between efficiency and performance achieved by CDP-GA. Finally, the optimal planning result obtained using CDP-GA is as follows: Figure 6 As shown.

[0167] Table 5 Comparison of Algorithm Results

[0168]

[0169] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is merely a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An improved genetic task planning method for binary star observations targeting moving targets, characterized by: Includes the following steps: Step 1: Based on the principle of observation task decomposition, preprocess the time window of the given satellite for the moving target to realize the transformation from a continuous task planning optimization problem to a discrete combinatorial optimization problem; Step 2: Introduce observation variables, set multiple constraints to meet the feasibility requirements of mission scheduling, and establish a dual-satellite observation mission planning model oriented towards moving targets. Step 3: Encode the subtasks decomposed from the task in Step 1 using a two-bit sequential coding method; Step 4: Based on the observed variables and constraints in Step 2, construct the objective function for the mission planning problem by taking the number of satellite switching times and the maximum load of a single satellite as optimization objectives. Step 5: Use the collaborative dual-path genetic algorithm to solve the task planning model constructed in Step 2, so as to achieve coordinated optimization of various indicators in the objective function in Step 4. Step 6: Repeat Step 5 until termination. The termination condition is reaching the preset number of iterations. The obtained solution for the improved genetic task planning for dual-star observation of moving targets is the planning sequence corresponding to the chromosome with the largest fitness value in the path 1 population. This is the planning result of the moving target observation task, thus realizing the improved genetic task planning for dual-star observation of moving targets.

2. The improved genetic task planning method for binary star observation oriented towards moving targets as described in claim 1, characterized in that: The specific implementation method of step one is as follows: the observation task decomposition principle follows the following constraints: first, the subtask is the smallest unit of the task and cannot be further divided; second, the observation time of the subtask cannot be less than L. min Third, the observation time of the sub-task cannot exceed L. max After decomposing the time window based on the above constraints, the task planning problem is transformed into a two-satellite selection problem in a discrete time dimension; that is, for each sub-task, two satellites are selected from the candidate satellite set to perform observations within the allocated time window, realizing the transformation from a continuous task planning optimization problem to a discrete combinatorial optimization problem.

3. The improved genetic task planning method for binary star observation oriented towards moving targets as described in claim 2, characterized in that: The observed variables in step two are, The observation task contains m observation targets, forming the target set TSet: TSet={T i ∣i=1,2,3,…,m} (1) Target T i The observation task information includes start time, end time, observation duration, set of subtasks, and number of subtasks: T i ={ST i ,ET i ,Period i ,SatSet i ,STNum i } (2) Target T i The corresponding collection of subtasks is SubSet i : SubSet i ={SubT i,j ∣j=1,2,3,…,STNum i } (3) For subtask SubT i,j This includes the start time, end time, observation duration, available satellite set, and number of available satellites: thin i,j ={ST i,j ,ET i,j ,Period i,j ,SatSet i,j ,VillageNumber i,j } (4) For satellites in the optional satellite set SatSet i,j Represented as: In the formula, Indicates the optional satellite number for the j-th subtask of the i-th target; The observation constellation consists of z observation satellites, forming a satellite set called SatSet: SatSet={S p ∣p=1,2,3,…,z} (6) Constraints: Satellites must complete their missions within a window of effective line of sight with the target; each satellite can only observe one mission at a time; each sub-mission requires observation by two satellites simultaneously; a target is considered fully observed only when all its sub-missions are completed.

4. The improved genetic task planning method for binary star observation oriented towards moving targets as described in claim 3, characterized in that: In step three, there are n subtasks, so the chromosome length is 2n. Each gene position is filled with integers according to the order of the target and subtasks. Every two gene positions are considered as a whole, called a bigenomic position. There are a total of n bigenomic positions, the same as the number of subtasks. The bigenomic position does not directly represent the satellite number, but rather identifies the selection order of the satellite chosen by the corresponding subtask in the set of available satellites, i.e., it is numbered with k. An encoding strategy ensures that the chromosome is a valid solution during the crossover and mutation processes of the genetic algorithm, unconstrained by the available satellite numbers of the subtasks.

5. The improved genetic task planning method for binary star observation oriented towards moving targets as described in claim 4, characterized in that: The number of satellite switching times in step four is: Using a dual-satellite observation method, the observation period is divided into multiple sub-task periods throughout the entire observation process. When different satellites are used for the same target, there will be a switching of the observation satellites. The number of switching is expressed by the following formula: condition1:S i,j,1 =S i,j+1,1 andS i,j,2 =S i,j+1,2 condition2:(S i,j,1 ≠S i,j+1,1 andS i,j,2 =S i,j+1,2 )or(S i,j,1 =S i,j+1,1 andS i,j,2 ≠S i,j+1,2 ) condition3:S i,j,1 ≠S i,j+1,1 andS i,j,2 ≠S i,j+1,2 In the formula, n i,j S refers to the total number of satellite switching operations in the j-th subtask of target i; i,j,1 S refers to the first satellite used by target i in the j-th observation sub-task. i,j,2 Refers to the second satellite; For two consecutive observation subtasks of the same target, if they use the same satellite, the number of switching is 0; if only one satellite is the same, the number of switching is 1; if both satellites are different, the number of switching is 2. Therefore, within a mission planning cycle, the total number of satellite handovers is equal to the sum of the number of satellite handovers for each sub-mission, as expressed below: In the formula, SwitchNum represents the total number of satellite switches within a mission planning cycle, and STNum... i This represents the number of subtasks for each objective, where m is the total number of objectives.

6. The improved genetic mission planning method for binary star observation oriented towards moving targets as described in claim 5, characterized in that: In step four, the maximum load on a single satellite is, Calculate the actual observation duration for each satellite, which is the sum of the observation times for the sub-tasks corresponding to the selected satellites: Calculate the actual observation duration for each satellite; the maximum payload for a single satellite is the largest payload in the total set of satellite observation durations. SatTime max = max{SatTime p |p=1,2...z} (10) z refers to the total number of satellites involved in mission planning within the entire constellation.

7. The improved genetic mission planning method for binary star observation oriented towards moving targets as described in claim 6, characterized in that: In step four, the objective function is optimized as follows: The optimization goal is to minimize the number of satellite handovers and the maximum load on a single satellite. min[SwitchNum,SatTime max ](11) For SwitchNum and SatTime max After weighting, the objective function is obtained as follows:

8. The improved genetic mission planning method for binary star observation oriented towards moving targets as described in claim 7, characterized in that: Step 5, the implementation method is as follows: 1) Initialize the population using a random method; 2) The initial population is iterated q times according to the steps of the standard genetic algorithm to obtain a better population; 3) Following the method of taking the current superior population as path 1 and selecting the top Num% individuals with high fitness to form path 2, the superior population is divided into path 1 and path 2. 4) Randomly divide the population of either path 1 or path 2 into two equal-sized populations, namely population 1 and population 2; in population 1, sort them in ascending order according to the number of satellite switches (SwitchNum) from step 4, and select the top Num1% of individuals with the fewest satellite switches to obtain subpopulation 1; in population 2, sort them according to the maximum single-satellite load (SatTime) from step 4. max Sort the data in ascending order and select individuals with smaller maximum single-star loads to obtain subpopulation 2. It is necessary to ensure that the number of individuals selected in the two subpopulations is the same. Based on the number of single-star switches in step four (SwitchNum(x)) i ) and single-star maximum load SatTime max (x i ), generate two subpopulations from population 1 and population 2: Wherein, P1 is a set of individuals based on the number of satellite handovers, and P2 is a set of individuals based on the maximum load of a single satellite. 5) Select one individual from subpopulation 1 and one individual from subpopulation 2 as parent generation 1 and parent generation 2 respectively; That is, parent generation 1x (1) Randomly select from P1, parent generation 2x (2) Randomly select from P2; 6) Perform uniform crossover operation based on the adaptive crossover operator of path 1 or path 2; For the population along path 1, an adaptive sinusoidal crossover operator is used, and its calculation formula is as follows: For the population along path 2, an adaptive cosine crossover operator is used; the formula is as follows: Among them, P cmax For the maximum crossover probability, P cmin Let f' be the number of parents with higher fitness among the two individuals to be crossovered, representing the minimum crossover probability. max f is the value with the highest fitness in the population. avg The average fitness of the population; 7) After crossover generates new individuals, perform replacement mutation on the population and establish an adaptive mutation operator, as shown in the following formula: Where mRate1 is the lower mutation rate, mRate2 is the higher mutation rate, and bestf is the highest fitness value among the parents. 8) When path 1 and path 2 complete the iteration, the current iteration round ends; after each iteration round, a feedback exchange strategy is constructed to exchange individuals in the two populations. 9) After each iteration, Path 2 selects the top Num2 individuals with high fitness from the current population of Path 1 and incorporates them into itself; at the same time, the high-quality offspring generated by Path 2 after local optimization replace the individuals with the lowest fitness in Path 1 in equal numbers, realizing the synergistic optimization of global search and local search, improving the overall fitness of the population, and thus improving the optimization level and solution efficiency of the improved genetic task planning method for binary star observation.

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