Task planning method and system for SAR satellite scanning mode with maximum multi-area coverage rate

Abstract

The invention provides an SAR satellite scanning mode task planning method and system for the maximum coverage rate of multiple areas, and the method comprises the steps: obtaining the geographic information, satellite load parameters and orbit information of a target area, and enabling the load parameters to comprise a continuous wave position set in a scanning mode; the population of the swarm intelligent optimization algorithm is initialized, individuals represent a task planning scheme, and individual parameters comprise a beam position set and task timing information; iteratively updating the population until a preset iteration termination condition is met, and seeking an optimal task planning scheme; and after iteration is finished, outputting an optimal planning scheme. The method effectively solves the problems that in the prior art, a task planning method for an SAR satellite scanning mode lacks, and an existing method is insufficient in the aspect of multi-target area coverage rate.

Description

Technical Field

[0001] The present invention relates to the field of satellite remote sensing technology, and particularly to a method and system for mission planning of an SAR satellite scanning mode aiming at the maximum multi-region coverage rate. Background Art

[0002] At present, synthetic aperture radar (SAR) satellite remote sensing technology plays an important role in the field of earth observation. The mission planning of SAR satellites aims to reasonably arrange the observation tasks of satellites to achieve specific observation goals. Traditional SAR satellite mission planning methods are mainly based on the strip mode. In this mode, the satellite observes along the orbit direction with a fixed attitude, and the coverage range is limited. However, with the rapid development of satellite payload technology and the increasing demand for obtaining SAR images with multiple resolutions, traditional strip mode mission planning is difficult to meet the growing mission requirements. As a new observation mode, the scanning mode can achieve a larger observation swath through the combination of multiple continuous wave positions, thereby improving the observation efficiency.

[0003] However, the existing SAR satellite mission planning methods have the following deficiencies in the scanning mode:

[0004] Lack of mission planning method for the scanning mode: Existing methods are mainly designed for the strip mode and cannot be directly applied to the scanning mode.

[0005] Do not fully consider the complex characteristics of satellite payloads: Traditional methods usually simplify the constraint conditions of satellite payloads and it is difficult to obtain good mission planning results in practical applications.

[0006] Low efficiency of the wave position update strategy: The scanning mode requires selecting multiple continuous wave positions to meet the swath requirements. The traditional wave position update strategy has low efficiency, which affects the iteration speed of mission planning.

[0007] Lack of processing logic for multi-target regions: In practical applications, it is usually necessary to observe multiple target regions simultaneously. Traditional methods are difficult to efficiently handle the mission planning problems of multi-target regions.

[0008] Insufficient computing efficiency: For mission planning with long time periods and large amounts of data, traditional methods usually use serial computing and the iteration rate is slow.

[0009] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present invention, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0010] In view of this, the present invention provides a mission planning method for SAR satellite scanning mode aiming at maximizing multi-region coverage rate, so as to solve the problems in the prior art, such as the lack of a mission planning method for SAR satellite scanning mode, the failure to fully consider various characteristics of satellite payloads and the actual working background, the low efficiency of the wave position update strategy, the lack of processing logic for multi-target regions, and the insufficient computing efficiency. The aim is to realize a mission planning scheme for obtaining the maximum observation coverage rate for multiple target regions in the SAR satellite scanning mode.

[0011] The present invention provides a mission planning method for SAR satellite scanning mode aiming at maximizing multi-region coverage rate, including the following steps:

[0012] Obtain the geographical information of at least one target region to be observed, the payload parameter information of the SAR satellite, and the orbit information; wherein, the payload parameter information includes multiple continuous wave position sets in the scanning mode.

[0013] Initialize the population of an optimization algorithm based on swarm intelligence, where each individual in the population represents a mission planning scheme, and the parameters of the individual include the wave position set selected for one or more observation tasks and the mission timing information.

[0014] Iteratively update the population until the preset iteration termination condition is met. In each iteration, for at least one individual, execute:

[0015] Based on the parameters of the individual, determine the actual working time period of the payload for one or more observation tasks.

[0016] Based on the actual working time period of the payload and the selected wave position set, determine the actual coverage area formed by the earth observation.

[0017] Calculate the intersection area between the actual coverage area and at least one target region.

[0018] Perform a union process on the intersection areas generated by one or more observation tasks to obtain the total target coverage area.

[0019] Calculate the objective function value representing the multi-region coverage rate according to the total target coverage area.

[0020] Update the population based on the objective function value and the rules of the optimization algorithm.

[0021] After the iteration ends, output the mission planning scheme represented by the individual corresponding to the optimal objective function value.

[0022] In some optional embodiments, the payload parameter information further includes the reference wave position information corresponding to each wave position set; the reference wave position information includes the maximum effective transit duration calculated by the set and the corresponding entry time when the wave position set is selected.

[0023] In some alternative embodiments, the parameters of the individual further include the working mode of the observation task and the side-looking direction.

[0024] In some alternative embodiments, the task timing information includes the payload working duration and the delay duration of the payload power-on time relative to the start time of the effective transit time window of the observation task;

[0025] Among them, the payload working duration and the delay duration are continuous variables; the numbers, working modes, and side-looking directions of the selected set of wave positions are discrete variables.

[0026] In some alternative embodiments, the payload parameter information further includes the minimum and maximum payload power-on duration constraints in the single-track mode and the double-track mode;

[0027] When updating the population, ensure that the payload working duration parameter satisfies the minimum and maximum power-on duration constraints corresponding to the working mode.

[0028] In some alternative embodiments, before initializing the population of the optimization algorithm, it further includes:

[0029] Based on the geographical information, payload parameter information, and orbit information, determine the effective transit time window of the SAR satellite for each target area.

[0030] In some alternative embodiments, after the step of determining the effective transit time window, it further includes:

[0031] If the effective transit time window of a single observation task is discontinuous, decompose it into one or more subtasks, and each subtask corresponds to a continuous effective shooting time period.

[0032] In some alternative embodiments, after decomposing into one or more subtasks, it further includes:

[0033] Divide the continuous effective shooting time period of each subtask into multiple time slices, and pre-compute the ground coverage geometric area corresponding to each time slice.

[0034] In some alternative embodiments, the step of determining the actual coverage area formed by the earth observation specifically includes:

[0035] For each subtask, identify all the time slices included in the actual working time period of the payload;

[0036] Perform a union operation on the ground coverage geometric areas corresponding to all the identified time slices, and then perform an intersection operation with the target geometric area to obtain the actual coverage area.

[0037] The present invention also provides a SAR satellite scanning mode mission planning system for maximizing multi-region coverage, including:

[0038] A data acquisition module configured to acquire geographical information of at least one target area to be observed, payload parameter information of the SAR satellite, and orbital information; wherein, the payload parameter information includes a set of multiple continuous wave positions in the scanning mode;

[0039] An initialization module configured to initialize a population of an optimization algorithm based on swarm intelligence, where each individual in the population represents a mission planning scheme, and the parameters of the individual include the set of wave positions selected for one or more observation tasks and task timing information;

[0040] An iterative processing module configured to iteratively update the population until a preset iteration termination condition is met, and seek an optimal mission planning scheme.

[0041] A result output module configured to output the mission planning scheme represented by the individual corresponding to the optimal objective function value after the iteration ends.

[0042] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure.

[0043] A SAR satellite scanning mode mission planning method and system of the present invention have the following beneficial effects:

[0044] The present invention uses a swarm intelligence optimization algorithm for mission planning in the SAR satellite scanning mode to search for an optimal mission planning scheme in a complex solution space. This algorithm transforms the satellite mission planning problem into an optimization problem of multi-dimensional parameters, where each parameter represents an aspect of the mission planning scheme, such as wave position selection, payload working time, etc. The algorithm continuously adjusts and optimizes the mission planning scheme through mutual learning and information sharing among individuals in the population, and gradually converges to the global optimal solution. In the scanning mode, the SAR satellite needs to select multiple continuous wave positions to achieve wide-swath observation, which makes the solution space of mission planning more complex. The swarm intelligence optimization algorithm can effectively cope with this complexity and search for a mission planning scheme that can maximize multi-region coverage under the premise of meeting various constraints, effectively improving the satellite observation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings.

[0046] Figure 1It is a flowchart of a SAR satellite scanning mode mission planning method for maximizing multi-region coverage rate according to an embodiment of the present invention;

[0047] Figure 2 It is a flowchart of another implementation manner of a SAR satellite scanning mode mission planning method for maximizing multi-region coverage rate according to an embodiment of the present invention;

[0048] Figure 3 It is a schematic diagram of the process of a single planning mission of a SAR satellite scanning mode mission planning method for maximizing multi-region coverage rate according to an embodiment of the present invention;

[0049] Figure 4 It is a schematic diagram of the optimal planning result of all final observation missions of a SAR satellite scanning mode mission planning method for maximizing multi-region coverage rate according to an embodiment of the present invention;

[0050] Figure 5 It is a schematic diagram of the structure of a SAR satellite scanning mode mission planning system for maximizing multi-region coverage rate according to an embodiment of the present invention. Detailed implementation manners

[0051] Now, example embodiments will be described more comprehensively with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0052] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0053] The flowcharts shown in the accompanying drawings are only exemplary illustrations and do not necessarily include all steps. For example, some steps can be further decomposed, while some steps can be combined or partially combined. Therefore, the actual execution order may be changed according to the actual situation.

[0054] Planning satellite observation missions, especially for synthetic aperture radar (SAR) operating in ScanSAR mode, with the aim of maximizing cumulative coverage over multiple geographically dispersed target areas while adhering to the operational constraints inherent in this scan mode, such as the need to use specific continuous wave position combinations (i.e., wave position sets) to achieve the required scan swath width, poses a significant technical challenge. Fundamentally, determining an optimal mission planning solution can be conceptualized as a complex optimization problem with a high-dimensional solution space defined by numerous mission parameters, including the selected wave position set and mission timing arrangement, etc., which necessarily requires computational strategies capable of effectively exploring this complex space. Such strategies typically rely on iterative optimization processes, where the effectiveness of potential solutions (i.e., mission planning solutions) is evaluated based on a quantified objective function such as the total coverage rate. To ensure the effectiveness of the optimization, accurate quantification of the objective function is crucial, which requires precise geometric calculations of the spatial interaction between the actual ground coverage area generated by the planned mission and the specified target area, and explicitly considering possible coverage overlaps between individual observation strips and with the target area through set operations. Therefore, applying iterative optimization techniques and guided by an objective function that can accurately reflect the total target coverage area achieved by the geometrically calculated combined observation coverage area can systematically explore the feasible parameter space of mission planning defined by scan mode operations. This optimization exploration process guided by accurate coverage assessment promotes convergence towards a mission planning solution that can generate maximum effective coverage for a specified set of multiple target areas, thereby enhancing the utilization efficiency of earth observation resources and mission execution effectiveness while satisfying the limitations of satellite platform and payload capabilities.

[0055] As Figure 1 shown, an embodiment of the present invention provides a method for SAR satellite scan mode mission planning oriented to maximum multi-region coverage rate, including the following steps:

[0056] P100. Obtaining the geographical information of at least one target area to be observed, the payload parameter information of the SAR satellite, and the orbital information is the basis of mission planning, ensuring the completeness of information in the planning process. The geographical information includes the set of longitude and latitude coordinate points of the target area, and specifically, the WGS84 coordinate system or other applicable geographical coordinate systems can be used. The payload parameter information covers multiple continuous wave position sets in the scan mode. The establishment of the wave position set is the key to achieving wide-swath observation in the scan mode. For example, wave position combinations that meet specific swath requirements can be pre-calculated and stored to form a wave position set library. The orbital information provides the satellite's operating trajectory, which is a necessary condition for determining the effective observation time window of the satellite for the target area.

[0057] P200. Initialize the population of an optimization algorithm based on swarm intelligence. Swarm intelligence optimization algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), etc., search for the optimal solution in a complex solution space by simulating the collaborative behavior of biological groups. Each individual in the population represents a task planning scheme, and its parameters include the set of wave positions selected for one or more observation tasks and task timing information. Task timing information includes the power-on time and working duration of the payload. These parameters jointly determine when and how the satellite observes the target area. The population size can be adjusted according to the complexity of the actual problem, for example, set to 100 - 200 individuals.

[0058] P300. Iteratively update the population until the preset iteration termination condition is met to seek the optimal task planning scheme. The iteration termination condition can be reaching the preset maximum number of iterations, such as 100 - 500 generations, or the fitness value of the population has not been significantly improved for several consecutive generations. In each iteration, for at least one individual, perform the following steps:

[0059] P310. Based on the parameters of the individual, determine the actual working time period of the payload for one or more observation tasks. This step converts the task planning scheme represented by the individual into specific payload operation instructions. For example, calculate the actual working time window of the payload according to the power-on time and working duration.

[0060] P320. Based on the actual working time period of the payload and the selected set of wave positions, determine the actual coverage area formed by the Earth observation. This step simulates the satellite's observation process and calculates the ground coverage range of the satellite during the actual working time period according to the working parameters of the payload and the satellite's attitude and orbit information.

[0061] P330. Calculate the intersection area between the actual coverage area and at least one target area. This step evaluates the effectiveness of the task planning scheme by calculating the overlapping part between the actual coverage area and the target area to determine the effective observation area of the satellite for the target area.

[0062] P340. Perform a union operation on the intersection areas generated by one or more observation tasks to obtain the total target coverage area. This step integrates the coverage effects of different observation tasks on the same target area to obtain the final total coverage area.

[0063] P350. Calculate the objective function value representing the multi-region coverage rate according to the total target coverage area. The design of the objective function should comprehensively consider multiple factors, such as coverage area, observation time, energy consumption, etc., and be adjusted and optimized according to actual needs. For example, the coverage area can be used as the main optimization objective, while certain constraints are imposed on the energy consumption.

[0064] P360. Update the population based on the objective function value and the optimization algorithm. This step is the core of the swarm intelligence optimization algorithm. Through the mutual learning and information sharing among individuals, the task planning scheme is continuously adjusted and optimized. For example, in the PSO algorithm, an individual updates its velocity and position according to its own historical best position and the global best position of the population.

[0065] P400. After the iteration ends, output the task planning scheme represented by the individual corresponding to the optimal objective function value. This scheme is the optimal SAR satellite scanning mode task planning scheme obtained by this method, and includes key parameters such as the waveband selection of each observation task and the load working time.

[0066] The embodiments of the present invention can effectively improve the multi-target area coverage rate of the SAR satellite in the scanning mode. Since in the scanning mode, the swath width increases, a larger area of the target area can be covered for a single pass. Therefore, when covering the same target area, the number of satellite switch - on and off times can be effectively reduced, the energy consumption can be reduced, and the service life of the satellite can be increased.

[0067] The SAR satellite scanning mode task planning method provided by the present invention for maximizing the multi - area coverage rate can achieve the following technical effects: For the SAR satellite scanning mode, a task planning method is provided, which can effectively solve the problem that there is a lack of a task planning scheme for this mode in the prior art. It can comprehensively consider various complex constraints of the satellite load, making the task planning result more suitable for the actual application scenario. By optimizing the waveband selection strategy and the objective function construction method, the multi - target area coverage rate can be improved. Using parallel computing, the efficiency of task planning is improved, and the task planning requirements for long time periods and large amounts of data can be met.

[0068] In some embodiments, the payload parameter information further includes reference wave position information corresponding to each wave position set; the reference wave position information includes the maximum effective transit duration calculated for the set and the corresponding entry time when this wave position set is selected. The embodiments of the present invention aim to further optimize the mission planning process and improve the search efficiency of the algorithm. In the scanning mode, each wave position set contains multiple consecutive wave positions, and each wave position corresponds to a different observation area. If optimization is directly performed for each wave position, the computational complexity will be very high. Therefore, the present invention introduces the concept of a reference wave position, simplifying the optimization of the wave position set to the optimization of a single reference wave position. There are various ways to select the reference wave position. For example, a wave position located at the center of the wave position set can be selected, or a representative wave position can be selected. In this embodiment, the wave position that enables the wave position set to have the maximum effective transit duration is selected as the reference wave position, which can better reflect the overall observation ability of the wave position set. The maximum effective transit duration refers to the longest time window during which the satellite can intersect with the target area when using this wave position set. The corresponding entry time is the satellite entry time corresponding to this maximum effective transit duration.

[0069] During the mission planning process, when a certain wave position set is selected, only the reference wave position corresponding to this set needs to be considered. Specifically, when determining the actual working time period of the payload, the startup time and working duration of the payload can be restricted according to the entry time and maximum effective transit duration of the reference wave position. For example, the startup time of the payload can be restricted near the entry time of the reference wave position, and the working duration of the payload can be restricted within the range of the maximum effective transit duration. In this way, the number of optimization variables can be significantly reduced, improving the efficiency of mission planning.

[0070] By introducing the concept of the reference wave position, the embodiments of the present invention simplify the optimization of the wave position set to the optimization of a single wave position during the mission planning process, effectively reducing the computational complexity, improving the efficiency of mission planning, and at the same time ensuring the quality of the mission planning result, which can effectively enhance the observation efficiency and mission execution ability of the SAR satellite.

[0071] In some embodiments, the parameters of the individual further include the working mode and the side-looking direction of the observation mission. The working mode refers to the working state of the SAR satellite payload, such as the single-track mode or the double-track mode. The single-track mode means that the satellite can perform at most one observation mission within one orbital period, and the double-track mode means that the satellite can perform at most one observation mission within two orbital periods. Under different working modes, the energy constraints and mission execution efficiency of the payload are different. The side-looking direction refers to the observation direction of the SAR satellite, such as left-looking or right-looking. Under different side-looking directions, the observation geometric relationship and observation effect of the satellite on the same target area are different.

[0072] In swarm intelligence optimization algorithms, taking the working mode and the side-looking direction as parameters of individuals can expand the search space and improve the flexibility and adaptability of mission planning. Specifically, when updating the population, the mode and side-looking direction of individuals can be adjusted according to the objective function value and the rules of the optimization algorithm. For example, if the objective function value of the current individual is low, its working mode or side-looking direction can be tried to be changed to find a better mission planning solution.

[0073] To further improve the efficiency of mission planning, the mode and side-looking direction can be discretized. For example, the mode can be set to 0 or 1, representing the single-track mode and the double-track mode respectively, and the side-looking direction can be set to 0 or 1, representing looking left and looking right respectively. In this way, the mode and side-looking direction can be transformed into discrete variables, simplifying the optimization process.

[0074] Embodiments of the present invention can make more full use of various working modes of satellite payloads by including the working mode and the side-looking direction in the consideration scope of mission planning, can effectively improve the flexibility and adaptability of SAR satellite mission planning, and help to obtain a mission planning solution with a higher coverage rate.

[0075] In some embodiments, the mission timing information includes the payload working duration and the delay duration of the payload power-on time relative to the start time of the effective transit time window of the observation mission; wherein, the payload working duration and the delay duration are continuous variables; the number of the selected waveband set, the working mode and the side-looking direction are discrete variables. In this embodiment, the mission timing information is decomposed into two continuous variables, namely the payload working duration and the delay duration, and it is clear that the number of the selected waveband set, the working mode and the side-looking direction are discrete variables. This representation method of mixed variables can more flexibly describe the mission planning solution and adapt to various actual constraints.

[0076] The payload working duration refers to the actual working time length of the SAR satellite payload in an observation mission, and its value range is affected by factors such as payload performance and energy constraints. The delay duration refers to the delay time of the payload power-on time relative to the start time of the effective transit time window of the observation mission, and its value range is affected by factors such as satellite attitude adjustment and data transmission. The effective transit time window refers to the time range during which the satellite can effectively observe the target area, and its start time and end time are determined by factors such as the satellite orbit and the target area position.

[0077] In swarm intelligence optimization algorithms, the load working duration and delay duration are regarded as continuous variables, and various optimization algorithms can be used to solve them, such as the gradient descent method, Newton's method, etc. The numbers of the selected beam position sets, working modes, and side-looking directions are regarded as discrete variables, and various discrete optimization algorithms can be used to solve them, such as genetic algorithms, simulated annealing algorithms, etc. By combining continuous variables and discrete variables, the task planning scheme can be described more comprehensively and the quality of task planning can be improved.

[0078] In the embodiments of the present invention, a method of combining continuous variables and discrete variables is adopted to describe the task planning scheme, which can consider various actual constraints more comprehensively, improve the flexibility and adaptability of task planning, and help obtain a task planning scheme with a higher coverage rate. Compared with the prior art task planning methods that only use discrete variables or only use continuous variables, the technical solution of the present invention can more effectively balance the efficiency and quality of task planning.

[0079] In some embodiments, the load parameter information further includes the shortest and longest power-on duration constraints of the load in single-track mode and double-track mode; when updating the population, ensure that the load working duration parameter meets the shortest and longest power-on duration constraints of the corresponding working mode. In this embodiment, for the energy constraint of the SAR satellite load, the shortest and longest power-on duration constraints of the load in single-track mode and double-track mode are introduced, and these constraints are forced to be met when updating the population.

[0080] The energy of the SAR satellite load is limited, so it is necessary to reasonably plan the working time of the load. Under different working modes, the energy consumption and task execution efficiency of the load are different. In single-track mode, the satellite can perform at most one observation task within one orbital period, so the energy demand for the load is relatively low, but the task execution efficiency is also relatively low. In double-track mode, the satellite can perform at most one observation task within two orbital periods, so it can perform observation tasks for a longer time, improving the task execution efficiency, but the energy demand for the load is also relatively high.

[0081] To ensure the feasibility of the task planning scheme, it is necessary to consider the energy constraint of the load. Specifically, it is necessary to limit the power-on duration of the load within a certain range, that is, to meet the shortest and longest power-on duration constraints of the load. The shortest power-on duration refers to the minimum time length required for the load to work normally, and the longest power-on duration refers to the longest time length that the load can continuously work without exceeding the energy limit.

[0082] When updating the population, it is necessary to check the load working duration parameter of each individual to ensure that it meets the shortest startup duration and the longest startup duration constraints under the corresponding working mode. If the constraint conditions are not met, the load working duration parameter can be adjusted, for example, set it to the shortest startup duration or the longest startup duration.

[0083] By considering the energy constraints of the SAR satellite payload and forcing it to meet the shortest startup duration and the longest startup duration constraints of the payload, the embodiment of the present invention ensures the feasibility of the mission planning scheme, avoids mission execution failures caused by insufficient energy, and helps improve the mission execution success rate and data acquisition quality of the SAR satellite.

[0084] In some embodiments, before initializing the population of the optimization algorithm, it further includes: determining the effective transit time window of the SAR satellite for each target area based on geographical information, payload parameter information, and orbit information. By pre-determining the effective transit time window, this technical feature narrows the search space of mission planning and improves the efficiency of mission planning.

[0085] The effective transit time window refers to the time range during which the SAR satellite can effectively observe the target area, and its start time and end time are determined by factors such as the satellite orbit, the position of the target area, and the satellite attitude. By pre-determining the effective transit time window before initializing the population of the optimization algorithm, those time periods during which the satellite cannot effectively observe the target area can be excluded, thereby narrowing the search space of mission planning.

[0086] There can be various specific methods for determining the effective transit time window. For example, according to the satellite orbit and the position of the target area, the distance between the satellite and the target area can be calculated. If this distance is less than a certain threshold, it is considered that the satellite can effectively observe the target area during this time period. In addition, the factor of satellite attitude can also be considered, such as whether the side-looking direction of the satellite can cover the target area.

[0087] By pre-determining the effective transit time window, it is possible to avoid searching those invalid time periods during mission planning, thereby improving the efficiency of mission planning. In addition, since the effective transit time window is the basis of mission planning, pre-determining the effective transit time window also helps improve the quality of mission planning.

[0088] By pre-determining the effective transit time window, the embodiment of the present invention narrows the search space of mission planning, thereby improving the efficiency of mission planning and reducing the computational complexity. At the same time, since the effective transit time window is the basis of mission planning, pre-determining the effective transit time window also helps improve the quality of mission planning and can reduce the computational cost while ensuring the coverage rate.

[0089] In some embodiments, after the step of determining the effective transit time window, the following steps are further included: If the effective transit time window of a single observation task is discontinuous, it is decomposed into one or more subtasks, and each subtask corresponds to a continuous effective shooting time period. This is aimed at solving the problem that the effective transit time window of a single observation task may be discontinuous due to various factors, such as the shape of the target area, satellite attitude changes, etc. If the task planning is directly carried out for the discontinuous effective transit time window, the planning result will be inaccurate, and there may even be a situation where the target area cannot be effectively covered. Therefore, in this embodiment, the discontinuous effective transit time window is decomposed into one or more subtasks, and each subtask corresponds to a continuous effective shooting time period, thereby improving the accuracy and reliability of the task planning. For example, assume that the effective transit time window of a certain observation task is [T1, T2] ∪ [T3, T4], where T1 < T2 < T3 < T4, then it can be decomposed into two subtasks, corresponding to the time periods [T1, T2] and [T3, T4] respectively. Each subtask can be independently planned for the task, so as to better meet the observation requirements of the target area. There can be various specific methods for decomposing the subtasks. For example, it can be decomposed according to the break points of the effective transit time window (such as between T2 and T3), or it can be decomposed according to the shape of the target area. By decomposing the discontinuous effective transit time window into multiple subtasks, the observation relationship between the satellite and the target area can be more accurately described, the accuracy and reliability of the task planning are improved, and the coverage effect of complex target areas can be effectively enhanced.

[0090] In some embodiments, after being decomposed into one or more subtasks, the following steps are further included: The continuous effective shooting time period of each subtask is divided into multiple time slices, and the ground coverage geometric area corresponding to each time slice is pre-calculated. The embodiments of the present invention are aimed at improving the accuracy and efficiency of task planning. Dividing the continuous effective shooting time period into multiple time slices can transform the continuous task planning problem into a discrete task planning problem, thereby simplifying the optimization process. Pre-calculating the ground coverage geometric area corresponding to each time slice can avoid repeated calculation of the coverage area during the task planning process and improve the calculation efficiency. There can be various ways to divide the time slices. For example, it can be divided at a fixed time interval, or it can be divided according to the satellite attitude change. The number of time slices can be adjusted according to the accuracy requirements of the actual problem, such as set to 10 seconds, 30 seconds or 60 seconds. The calculation of the ground coverage geometric area can adopt various existing geometric calculation methods, such as polygon clipping, polygon area calculation, etc. In order to improve the calculation efficiency, a pre-calculation method can be adopted to pre-calculate and store the ground coverage geometric area corresponding to each time slice, and directly call it during the task planning process.

[0091] In the embodiments of the present invention, by transforming the continuous task planning problem into a discrete task planning problem and adopting a pre-computation method to avoid repeated calculations, the computational complexity can be effectively reduced, the efficiency of task planning can be improved, and the accuracy of task planning can be ensured at the same time, so that the final task planning result can better meet the actual observation requirements.

[0092] In some embodiments, the steps of determining the actual coverage area formed by earth observation specifically include:

[0093] For each subtask, identify all the time slices included in the actual working time period of the payload;

[0094] Perform a union operation on the ground coverage geometric regions corresponding to all the identified time slices, and then perform an intersection operation with the target geometric region to obtain the actual coverage area.

[0095] The embodiments of the present invention provide a specific method to determine the actual coverage area formed by earth observation. Since the continuous effective shooting time period has been divided into multiple time slices and the ground coverage geometric regions corresponding to each time slice have been pre-computed, the actual coverage area can be obtained by identifying all the time slices included in the actual working time period of the payload, performing a union operation on the ground coverage geometric regions corresponding to these time slices, and then performing an intersection operation with the target geometric region.

[0096] To identify whether a time slice is included in the actual working time period of the payload, it can be achieved by comparing the start time and end time of the time slice with the start time and end time of the actual working time period of the payload. If the start time of the time slice is later than or equal to the start time of the actual working time period of the payload, and the end time of the time slice is earlier than or equal to the end time of the actual working time period of the payload, then it is considered that the time slice is included in the actual working time period of the payload. The union operation of the ground coverage geometric regions can adopt various existing geometric calculation methods, such as polygon merging, image superposition, etc. To improve the calculation efficiency, a parallel calculation method can be adopted to perform the union operation on the ground coverage geometric regions corresponding to multiple time slices simultaneously.

[0097] The embodiments of the present invention can accurately determine the actual coverage area formed by earth observation by identifying the time slices included in the actual working time period of the payload, performing a union operation on the ground coverage geometric regions corresponding to these time slices, and then performing an intersection operation with the target geometric region, providing an accurate data basis for subsequent calculation of the objective function value, and effectively improving the reliability of the task planning result.

[0098] Such as Figure 2As shown in the figure, it is a task planning method for the SAR satellite scanning mode with the largest multi-region coverage rate provided by another embodiment of the present invention, including the following steps:

[0099] S1. Obtain the multi-target area information set, payload information set, observation task information, corresponding orbital recurrence information set, and time slice information set for each sub-task.

[0100] S11. Obtain the multi-target observation area information.

[0101] The multi-target area set Area = {Target1, Target a , …, Target n_target}, where n_target is the number of areas to be observed.

[0102] The information of a single area Target a = {P1, …, P z , …, P n_point}, where n_point is the number of longitude and latitude coordinate points;

[0103] P z represents the z-th longitude and latitude coordinate point, and P z = [Lon z , Lat z , Lon z is the longitude value of the z-th coordinate point, and Lat z is the latitude value of the z-th coordinate point.

[0104] For example, Regions = {Target1, Target2, Target3}, and there are a total of 3 target areas.

[0105] S12. Obtain the payload information.

[0106] The side-looking set q = {1, 2}, where 1 means the satellite takes a left side-looking view, and 2 means the satellite takes a right side-looking view;

[0107] The waveband set Band = {1, …, b, …, n_band}, where n_band is the total number of wavebands;

[0108] The time for the satellite to orbit the earth once is T_Circle seconds;

[0109] Mode set Mode = {0, 1}, restricted by the energy of the payload. 0 is the single-track mode. The payload can be powered on once within T_Circle seconds, with the shortest power-on time of the payload being Single_Min seconds and the longest power-on time being Single_Max seconds. 1 is the double-track mode. The payload can be powered on at most once within 2 * T_Circle seconds when the satellite orbits the Earth twice. The longest power-on time of the payload is Double_Min seconds, and the longest power-on time is Double_Max seconds. In the double-track mode, if the interval time between the shutdown moment of the current orbit and the power-on moment of the next orbit is less than T_Circle seconds, it does not meet the energy constraint of the payload, and the payload cannot be powered on in the next orbit.

[0110] The swath width in the scanning mode is breadth kilometers;

[0111] The set of the number of wave positions that meet the swath width requirement in the scanning mode Width = {w1, …, w c , …, w n_width};

[0112] where w a represents the number of the c-th set of consecutive wave positions that meet the swath width breadth, and n_width represents the number of elements in the Width set, that is, there are n_width groups of consecutive wave position sets that meet the swath width breadth. The c-th set of consecutive wave position sets is {c, …, c + w c - 1}.

[0113] For example, q = {1, 2}, Band = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}, the satellite passes by in one orbit in 6000s, Mode = {0, 1}, single-track mode, the shortest power-on time of the payload is 80 seconds, and the longest power-on time is 160 seconds; double-track mode, the longest power-on time of the payload is 160 seconds, and the longest power-on time is 320 seconds;

[0114] The swath width in the scanning mode is 40KM, and the set of the number of wave positions that meet the swath width is Width = {4, 4, 4, 5, 5, 5, 5, 6, 6, 6}, and n_breadth is 10.

[0115]

[0116]

[0117] For example, for the 4th set of wave position sets, the number of wave positions in the set is 5, the wave position set starts from 4 and ends at wave position 8 (i.e., 4 + 5 - 1), and the wave position set is {4, 5, 6, 7, 8}.

[0118] S13. Obtain the set of observation task information.

[0119] Observation task set Task q,b ={1,..,i q,b ,…,n_task}, where i q,b is the i-th observation task, which adopts side-looking q and band b, where n_task is the total number of observation tasks.

[0120] For example, Task q,b ={1,2,3,4}.

[0121] S14. Obtain the orbit recurrence information of observation task i q,b .

[0122] With different side-lookings and different wave positions, the differences in the shooting area range for observation tasks are significant.

[0123] i q,b =[flag i ,window i ,delta i ,rally i represents the orbit recurrence information of observation task i q,b , where flag i is the flag indicating whether it is an effective transit when observing task i q,b at side-looking q and wave position b, flag i =[true,false], true means effective transit and there is an intersection with the multi-target area, false means ineffective transit and there is no intersection with the multi-target area;

[0124] Time window window i =[wstart i ,wend i , wstart i is the entry time of observation task i qb , wend i is the departure time of observation task i q,b ;

[0125] Transit duration delta i =wstart i -wend i ;

[0126] rally i is the set of subtasks of observation task i q,b that capture an intersection with the multi-target area (one subtask corresponds to a valid shooting area), where subtask represents observation task i q,bThe r-th sub-task, i.e., the r-th sub-region captured, r_target represents the observation task i q,b The number of captured regions;

[0127] Indicates the sub-task The orbital recurrence information of, tw i,r Is the sub-task The transit time window of, d i,r Is the observation task The transit duration of, s i,r Is the observation task The time slice information of;

[0128] The time window tw i,r =[start i,r , end i,r , start i,r Is the sub-task The entry time of, end i,r Is the sub-task The departure time of;

[0129] The transit duration d i,r =start i,r -end i,r ;

[0130] The set of time slices Among them, Is the j-th time slice of the sub-task , k is the number of time slices of the sub-task .

[0131] For example, obtain the orbital recurrence information when 4 tasks respectively adopt left-looking or right-looking and respectively adopt 15 wave positions. So each observation task has a total of 2 * 15 = 30 combinations, and all observation tasks have a total of 4 * 30 = 120 combinations; taking one of the combinations as an example, for example, the orbital recurrence information when the second transit task adopts left-looking and the wave position is 3 is, [true, [2024-12-27-04-26-12, 2024-12-27-04-28-48], 156, rally2], and the sub-task set is rally2.

[0132] Among them Is the first sub-task of the second transit task with a left-looking wave position of 3, Among them s 2,1 Is the time slice set of the first sub-task of the corresponding second task, i.e., the first captured region, s 2,2It is the set of time slices for the second subtask corresponding to the second task, that is, the second area captured;

[0133] S15. Obtain the subtask of the j-th time slice information.

[0134] Among them represents the time window of the j-th slice of the subtask and represents the rectangular fitting coverage area of the j-th slice of the subtask ;

[0135] Among them is the entry time of the j-th time slice of the subtask and is the departure time of the j-th time slice of the subtask ;

[0136] represents the four longitude and latitude coordinate points of the coverage rectangular area of the j-th time slice of the subtask , and the four coordinate points are arranged in counterclockwise order.

[0137] For example, taking one time slice every 10 seconds as an example, one of the time slice information of s 2,1 is [[2024-12-27-04-26-42,2024-12-27-04-26-52],[[104.6,29.7],[104.7,29.5],[105.2,29.9],[105.1,29.1]]], that is, the time slice from 4:26:42 on December 27, 2024 to 4:26:52 on December 27, 2024, and the rectangular range captured in 10 seconds composed of 4 longitude and latitude coordinate points.

[0138] S2. Process various wave position sets that meet the scanning width, and find the corresponding reference wave position.

[0139] S21. Process the transit times of all captured areas of a certain wave position group under a certain left / right view in a certain cycle.

[0140] S21. Obtain the maximum transit range under the continuous wave position numbers that meet the width, and the corresponding reference wave position.

[0141] S211. Construct a four-dimensional table structure T_Range[c][q][m][3], where the number of elements in the first dimension is n_width, the number of elements in the second dimension is 2, the number of elements in the third dimension is n_task, and the number of elements in the fourth dimension is 3. Here, c ∈ {1, 2, …, n_width}, q = {1, 2}, and m ∈ {1, 2, …, n_task}.

[0142] T_Range[c][q][m][1] is used to store a reference wave position base, where base ∈ the set of wave positions in the c-th group {c, …, c + w c -1}, that is, in the m-th observation task with a side view of q, when selecting the reference wave position base in the selected wave position set, it has the largest transit range;

[0143] T_Range[c][q][m][2] is used to store the transit duration of the m-th observation task with a side view of q and a reference wave position base;

[0144] T_Range[c][q][m][3] is used to store the entry time of the m-th observation task with a side view of q and a reference wave position base.

[0145] S212. Traverse Task q,b and Width, and perform data preprocessing. If any wave position in the c-th group of wave positions is used in the m-th observation task and the shooting area intersects with the target area, then

[0146] T_Range[c][q][m][1] = base, that is, the reference wave position belonging to the c-th group of wave positions;

[0147] T_Range[c][q][m][2] = delta m , that is, the transit duration of the observation task m q,base ;

[0148] T_Range[c][q][m][3] = wstart m , that is, the entry time of the observation task m q,base .

[0149] If any wave position in the c-th group of wave positions is used in the m-th observation task and the shooting area has no intersection with the target area, then

[0150] T_Range[c][q][m][1] = 0, T_Range[c][q][m][2] = 0, T_Range[c][q][m][3] = 0.

[0151] For example, taking the 5th set of frequency positions (the 5th set of frequency positions is {5, 6, 7, 8, 9}) and the observation task of looking right as an example, T_Range[5][2][m][1], T_Range[5][2][m][2] and T_Range[5][2][m][3] are as follows:

[0152]

[0153] As can be seen from the above table, when taking the fifth set of frequency positions and looking right, for the first observation task, the reference frequency position is 9, that is, when the frequency position is 9, it has the longest transit duration. Then, in the current particle update operation, if the fifth set of frequency positions is selected, the update iteration of the continuous random variable and the task planning of the current particle are updated iteratively based on the transit duration and the entry time corresponding to the reference frequency position 9; for the 3rd observation task, for any frequency position in the set of frequency positions, there is no intersection between the shooting area and the target area.

[0154] S3. Population initialization.

[0155] S31. Create a population. The population contains 150 particles, and the number of iterations is set to 200 generations.

[0156] Each particle contains the position and velocity values of the discrete and continuous random variables of all observation tasks. The positions and velocities of each component of each particle are randomly initialized within the search space, and the learning factors C1 and C2 are assigned the value of 1.495. The continuous random variables are: t and Δt, and the rest of the variables are discrete random variables.

[0157] The task information position matrix of each particle is:

[0158]

[0159] where t i represents the observation duration of the i-th observation task, Δt i represents the duration from the entry time to the startup time of the i-th observation task, c i represents the number of the selected set of frequency positions, q i represents the side view of the i-th observation task, mode i represents the mode of the i-th observation task (i ∈ [1,..., n_task]).

[0160] The frequency position information position matrix of each particle is:

[0161]

[0162] where Indicates the adoption situation of the g-th wave position in the i-th observation task. A value of 0 means this wave position is not adopted, and a value of 1 means this wave position is adopted.

[0163] The velocity matrix of each particle is:

[0164]

[0165] Where represents the velocity value of the variable t i , represents the velocity value of the variable Δt i .

[0166] S4. Update the set of wave positions that meet the scanning width, and update a single particle to generate the current task plan.

[0167] S41. Update the discrete random variables of a single particle.

[0168] S411. Randomly select left view or right view q i ∈q.

[0169] S412. Randomly select the mode mode i ∈Mode.

[0170] S413. Randomly select a number c i ∈{1,…,n_width}, then the corresponding c-th i group of wave position sets is , and the corresponding reference wave position base i = T_Range[c i [q i [mode i [1]; S414. Traverse the i-th row of the Position_band matrix. For If Then Otherwise For example, if the selected number c i = 9, that is, the 9th group of wave position sets, and w9 = 6 in the Width set, then the corresponding 7th group of wave position sets starts from wave position 9 and ends at wave position 14 (i.e., 9 + 6 - 1), that is, {9, 10, 11, 12, 13, 14};

[0171] The values of the i-th row of the Position_band matrix are: 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0.

[0172] S42. Update the continuous random variables t i and Δt iUpper and lower limits.

[0173] S421, 0 ≤ Δt i ≤ T_Range[c i [q i [i][2], that is, the duration from the entry time to the power-on time should be within the transit range of the reference wave position base in the c i th set of wave position sets and the i i th observation task of the side-looking q, and

[0174] S422. If mode i = 0, then Single_Min ≤ t i ≤ Single_Max, that is, the power-on duration t i of the i

[0175] th observation task satisfies the shortest and maximum power-on durations in the single-track mode, and i If mode i = 1, then Double_Min ≤ t i ≤ Double_Max, that is, the power-on duration t

[0176] of the i

[0177] th observation task satisfies the shortest and maximum power-on durations in the double-track mode, and

[0178]

[0179] The lower limit matrix of the continuous random variable position of the current particle is:

[0180]

[0181] S43. Update the continuous random variables t i and Δt i in the i

[0182] th observation task of a single particle: i is the optimal position of the continuous random variables t i and Δt of the current particle, i is the global optimal position of the continuous random variables t i and Δt

[0183] The update formulas for the velocity and position of continuous random variables are as follows:

[0184]

[0185] where w is the weight, and R1 i and i R2 are random numbers.

[0186] S44. Boundary processing.

[0187] S441. If that is, the transit duration does not exceed the maximum startup duration of the single-rail or double-rail mode, then Δt i = 0, and the payload is powered on throughout the period;

[0188] If and then randomly generate

[0189] If and then

[0190] S442. The duration from the entry time to the shutdown time is: Δt i + t i .

[0191] If then randomly generate

[0192] S45. Update all observation tasks for a single particle.

[0193] Repeat S41 - S44 to obtain:

[0194] The position matrix of the current particle is:

[0195]

[0196] The position matrix of the wave position information of the current particle is:

[0197]

[0198] The velocity matrix of the current particle is:

[0199]

[0200] S46. Plan the payload working plan time for the i-th observation task in the c-th i group of wave position sets.

[0201] For the $i$-th observation task, for different wave positions in the set of adopted wave positions, a random variable $t$ and $\Delta t$ are uniformly maintained because for this task, regardless of which wave positions are used, the payload needs to be powered on and off together.

[0202] The planned payload power-on time is: Start_Sar i = T_Range[c][q][m][3] + $\Delta t$ i (entry time + duration from entry to power-on);

[0203] The payload power-on duration is: $t$ i ;

[0204] The planned payload power-off time is: End_Sar i = Start_Sar i + $t$ i .

[0205] S47. Obtain the effective time range for the sub-task of the $r$-th adopted wave position $b$ in the $i$-th observation task for photographing the target area.

[0206] As Figure 3 shown, let the start time for photographing the target area of the sub-task of the $r$-th adopted wave position $b$ in the $i$-th observation task be ENstart i,r,b , and the end time for photographing the target be ENend i,r,b , then the effective time range is [ENstart i,r,b , ENend i,r,b .

[0207] If start i,r < Start_Sar i , and Start_Sar i < end i,r < End_Sar i , then ENstart i,r,b = Start_Sar i , ENend i,r,b = end i,r ;

[0208] If start i,r > Start_Sar i , and end i,r < End_Sar i , then ENstart i,r,b = start i,r , ENend i,r,b = end i,r ;

[0209] If Start_Sar i <start i,r <end i,r , and End_Sar i <end i,r , then ENstart i,r,b = start i,r , ENend i,r,b = End_Sar i ;

[0210] If start i,r <Start_Sar i , and end i,r > End_Sar i , then ENstart i,r,b = Start_Sar i , ENend i,r,b = End_Sar i ;

[0211] If end i,r <Start_Sar i , then the payload planning does not power on, and flag i = false;

[0212] If start i,r > End_Sar i , then the payload planning does not power on, and flag i = false.

[0213] For example, in the first case, the effective shooting time range for the target area is: [Start_Sar i , end i,r ;

[0214] In the second case, the effective shooting time range for the target area is: [start i,r , end i,r ;

[0215] In the third case, the effective shooting time range for the target area is: [start i,r , End_Sar i ;

[0216] In the fourth case, the effective shooting time range for the target area is: [Start_Sar i , End_Sar i ;

[0217] In the fifth case, when it is not within the power-on time range of the mission plan, the payload plan does not power on.

[0218] In the sixth case, when it is not within the power-on time range of the mission plan, the payload plan does not power on.

[0219] S48. Obtain the subtask of the r-th acquisition wave position b for the i-th observation mission The set of longitude and latitude points taken for the target area.

[0220] If flag i = true, j satisfies That is, the effective shooting start time is within the j-th time slice, and x satisfies That is, the effective shooting end time is within the (j + x)-th time slice, then find the union, Area i,r,b Is the set of longitude and latitude points taken for the r-th acquisition wave position b of the i-th observation mission for the target area shooting.

[0221] If flag i = false, since the mission plan payload does not power on, there is no shooting, so Area i,r,b Is an empty set.

[0222] S49. Respectively obtain the set of longitude and latitude points taken for the target area when all subtasks of all observation missions take all planned wave positions.

[0223] Traverse Position_task and Position_band, and repeat S46, S47, and S48.

[0224] If mode i = 1, flag i = true, Start_Sar i+1 - End_Sar i ≥ T_Circle, that is, in the dual-track mode, the interval time between two adjacent orbits is greater than the time for the satellite to orbit the earth once, meeting the energy constraint, then flag i+1 = true;

[0225] If mode i = 1, flag i = true, Start_Sar i+1 - End_Sar i < T_Circle, that is, in the dual-track mode, the interval time between two adjacent orbits is less than the time for the satellite to orbit the earth once, not meeting the energy constraint, then flag i+1 = false, Area i+1,r,b Is an empty set, and the payload of the (i + 1)-th observation mission does not power on.

[0226] S5. Calculate the objective function and fitness function.

[0227] S51. Let the set of longitude and latitude points be Lon_Lat, and the area function be S(Lon_Lat).

[0228] S(Lon_Lat) is as follows: Traverse the sequence of longitude and latitude points of the polygon, adopt the polygon area calculation method, convert the longitude and latitude coordinates to radians, and combine the characteristics of the earth's curvature to accurately calculate the area of the target observation area.

[0229] S52. Calculate the objective function.

[0230]

[0231] Because there may be overlaps in the shooting areas of adjacent two wave positions for the same subtask, and there may also be overlaps in the shooting areas of different observation tasks, so a simple cumulative operation cannot be performed on the shooting area area: The numerator is, first find the intersection of the shooting area and the target area Target when the subtask r of the i-th observation task adopts the wave position b in the c-th wave position set, and then successively perform coverage merging on the shooting areas for the target area Target when adopting all wave positions in the c-th wave position set, perform coverage merging on the shooting areas for the target area Target of all subtasks of the i-th observation task, perform coverage merging on the shooting areas for the target area Target of all observation tasks, and perform coverage merging on the shooting areas for all target areas of all observation tasks to find the final coverage area for all target areas; i the shooting area and the target area Target when the subtask r of the i-th observation task adopts the wave position b in the c-th wave position set a and then successively i perform coverage merging on the shooting areas for the target area Target when adopting all wave positions in the c-th wave position set a perform coverage merging on the shooting areas for the target area Target of all subtasks of the i-th observation task a perform coverage merging on the shooting areas for the target area Target of all observation tasks a perform coverage merging on the shooting areas for all target areas of all observation tasks to find the final coverage area for all target areas;

[0232] The denominator is the total area of all target areas.

[0233] S53. Calculate the fitness function.

[0234] AF = (Coverage + 1) 5 - 1

[0235] Save the global optimal positions of each variable when AF is the largest, that is and and the corresponding and until

[0236] S6. Perform parallel computing to update all particles.

[0237] S61. Perform parallel computing to update all particles.

[0238] Allocate the particles within the population to multiple threads, and each thread repeats the execution of S45, S49, S52, and S53 in parallel until all particles are updated.

[0239] S7. Complete all iterations to obtain the optimal plan.

[0240] S71. Repeat S51 until the number of iterations is reached.

[0241] S72. Obtain the optimal plan for the i-th observation task.

[0242] If flag i = true, then the plan for the i-th observation task is:

[0243] The power-on time of the payload is:

[0244] The power-on duration of the payload is:

[0245] The planned power-off time of the payload is:

[0246] Wave position:

[0247] Side-looking:

[0248] Mode:

[0249] If flag i = false, then there is no payload power-on plan for the i-th observation task.

[0250] S73. Repeat S62 to obtain the optimal plans for all observation tasks.

[0251] The optimal plans for all the final observation tasks are:

[0252] Let h = n_task,

[0253]

[0254]

[0255] The optimal plans for all the final observation tasks are as Figure 4 shown.

[0256] As Figure 5As shown in the figure, the present invention provides a SAR satellite scanning mode task planning system for maximizing multi-region coverage rate. The system aims to overcome the technical problems faced by the prior art in processing SAR satellite scanning mode task planning, such as complex wave position combination management, inaccurate multi-objective coverage evaluation, difficulty in taking into account various payload constraints, and low planning efficiency, so as to maximize the total coverage rate of multiple specified target regions. In a specific implementation, the physical carrier of the system can be one or more computing devices, such as servers, workstations, or embedded computing platforms. The core hardware architecture of the system includes at least one processor and a memory that interacts with the processor for data. The memory, as a computer-readable medium, can include volatile storage units (such as RAM) and non-volatile storage units (such as hard disks, solid-state drives, or ROM), and its main function is to store a series of program instructions for the processor to execute. The processor can be a computing core such as a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a field programmable gate array (FPGA), or an application specific integrated circuit (ASIC). When the processor loads and executes the program instructions stored in the memory, the system is configured to perform a series of operations to implement the above task planning method. The system includes:

[0257] Data acquisition module M100. The system acquires and processes basic input data through the configured interface or data reading module, which includes: the geospatial information of at least one target region to be observed (for example, a list of vertex coordinates defining the region boundary), the orbital elements or ephemeris data of the SAR satellite, and the key SAR satellite payload parameter information. The payload parameter information here is crucial for the scanning mode planning of the present invention, and it clearly includes multiple sets of continuous wave positions that can be selected in the scanning mode. Each set of wave positions represents a group of continuous wave positions that can work simultaneously to meet the specific scanning swath requirements, which is a key input different from the traditional strip mode planning.

[0258] Initialize module M200. Connect to the data acquisition module M100 and configure it to obtain the geographical information of at least one target area to be observed, the payload parameter information of the SAR satellite, and the orbit information. The system performs an initialization step to establish a framework for solving this complex optimization problem. Specifically, the system initializes a population of an optimization algorithm based on swarm intelligence. The swarm intelligence optimization algorithm here can be an algorithm suitable for handling high-dimensional and complex constraint optimization problems such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), etc. Each individual in the population (for example, a particle in PSO) logically represents a complete and potential mission planning scheme. The parameters (or position vectors) of each individual encode the key decision variables of this scheme. According to the present invention, these parameters need to at least include the set of wave positions (for example, represented by the number of the set) selected for one or more observation tasks involved in the planning, and the corresponding task timing information (for example, indicating when to start and how long it lasts).

[0259] Iterative processing module M300. Connect to the initialization module M200 and configure it to initialize a population of an optimization algorithm based on swarm intelligence. The system enters the core iterative update loop. The processor repeatedly executes the iterative logic of the optimization algorithm, continuously adjusting and improving the mission planning scheme represented by the individuals in the population until the preset iterative termination conditions are met (for example, reaching the maximum number of iterations, the objective function value converges, or reaching the preset planning time limit). During each iteration, the system performs a series of evaluation and update operations on at least one individual (usually all individuals) in the population:

[0260] Based on the current parameters of this individual (the selected set of wave positions, task timing information, etc.), the processor calculates the actual working time period of the payload for one or more observation tasks in the planning. This requires combining the satellite orbit information and the target area transit window to determine the exact turn-on and turn-off times.

[0261] According to the determined actual working time period of the payload and the set of wave positions selected by this individual, the processor simulates or calculates the SAR payload's ground observation process to determine the actual coverage area formed. This calculation needs to consider factors such as satellite attitude, beam pointing, terrain undulation (if the model supports), etc., to obtain the geometric coverage range on the ground.

[0262] The processor performs spatial geometric operations to calculate the intersection area between the obtained actual coverage area and all the input target areas to be observed. This determines the part of the target area that is actually effectively covered by this planning scheme.

[0263] Since planning may involve multiple observation tasks (for different transits of the same target area or for different target areas), and a single task in the scanning mode may also cover parts of multiple target areas, the processor needs to perform a union operation on the intersection areas generated by all these observation tasks. This union operation is crucial as it integrates all effective coverage and naturally and precisely handles the possible coverage overlaps between different observation strips and between different tasks, thus obtaining the total target coverage area that represents the current individual planning scheme for all target areas without duplicate calculations.

[0264] Based on the area (or other coverage metrics) of the obtained total target coverage area, the processor calculates a quantified objective function value, which directly represents the multi-region coverage rate (e.g., the ratio of the total coverage area to the total area of the target area). This objective function is the core basis for driving optimization.

[0265] Finally, based on the objective function value calculated for this individual and following the specific rules of the selected swarm intelligence optimization algorithm (e.g., individual best and global best updates, velocity and position updates in PSO), the processor updates the population so that the population as a whole tends to generate a planning scheme with a higher objective function value.

[0266] Result output module M400. It is connected to the iterative processing module M300 and is configured to output the task planning scheme represented by the individual corresponding to the optimal objective function value after the iteration ends. When the iterative process terminates, the system outputs through the configured output module the task planning scheme represented by the individual in the population (or recorded historically) with the optimal objective function value (i.e., the maximum coverage rate). This scheme includes detailed scheduling information such as the set of wave positions to be selected for each observation task determined to achieve optimal coverage and the specific power-on / power-off times.

[0267] The system of the embodiment of the present invention simplifies the optimization variables by abstracting the wave position selection problem in the scanning mode as the selection of a predefined "wave position set"; constructs the objective function by accurately calculating the intersection and union of the coverage areas, solving the problem of inaccurate coverage evaluation; and utilizes the global search ability of the swarm intelligence optimization algorithm to effectively explore the complex solution space.

[0268] Therefore, the system provided by the embodiments of the present invention can automatically and efficiently generate a SAR satellite scan mode task planning scheme with the maximum multi-region coverage rate, significantly improving the accuracy and optimality of the planning, ensuring that the planning results can fully utilize the coverage advantages of the scan mode, while meeting the actual operation constraints, and improving the utilization efficiency of satellite resources. It should be understood that the above functional units can be implemented in the form of software, hardware or firmware, and can be combined or split according to specific application scenarios. For example, data acquisition, initialization, iterative processing, and result output can be implemented as different software modules running on a processor.

[0269] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A SAR satellite scanning mode mission planning method for maximizing multi-region coverage rate, characterized in that including the following steps, obtaining the geographical information of at least one target area to be observed, the payload parameter information of the SAR satellite, and the orbital information; wherein, the payload parameter information includes a plurality of continuous wave position sets in a scanning mode; initializing a population of an optimization algorithm based on swarm intelligence, wherein each individual in the population represents a mission planning scheme, and the parameters of the individual include the wave position set selected for one or more observation tasks and the mission timing information; iteratively updating the population until a preset iteration termination condition is met. In each iteration, for at least one individual, execute: determining the actual working time period of the payload for the one or more observation tasks based on the parameters of the individual; determining the actual coverage area formed by the earth observation based on the actual working time period of the payload and the selected wave position set; calculating the intersection area between the actual coverage area and the at least one target area; performing a union process on the intersection areas generated by one or more observation tasks to obtain the total target coverage area; calculating the objective function value representing the multi-area coverage rate according to the total target coverage area; updating the population based on the objective function value and the rules of the optimization algorithm; after the iteration ends, output the mission planning scheme represented by the individual corresponding to the optimal objective function value.

2. The SAR satellite scanning mode mission planning method for maximizing multi-region coverage rate according to claim 1, wherein The payload parameter information further includes the reference wave position information corresponding to each wave position set; the reference wave position information includes the maximum effective transit duration calculated on behalf of the set and the corresponding entry time when this wave position set is selected.

3. The SAR satellite scanning mode mission planning method for maximizing multi-region coverage rate according to claim 1, characterized in that The parameters of the individual further include the working mode and the side-looking direction of the observation task.

4. The SAR satellite scanning mode mission planning method for maximizing multi-region coverage rate according to claim 3, wherein The mission timing information includes the payload working duration and the delay duration of the payload startup time relative to the start time of the effective transit time window of the observation task; wherein, the payload working duration and the delay duration are continuous variables; the number of the selected wave position set, the working mode, and the side-looking direction are discrete variables.

5. The SAR satellite scanning mode mission planning method for maximizing multi-region coverage rate according to claim 4, characterized in that The payload parameter information further includes the shortest and longest payload startup duration constraints in the single-track mode and the double-track mode; when updating the population, ensure that the payload working duration parameter meets the shortest and longest startup duration constraints corresponding to the working mode.

6. The SAR satellite scanning mode mission planning method for maximizing multi-region coverage rate according to claim 1, wherein Before initializing the population of the optimization algorithm, it further includes: determining the effective transit time window of the SAR satellite for each target area based on the geographical information, payload parameter information, and orbital information.

7. The SAR satellite scanning mode mission planning method for maximizing multi-region coverage rate according to claim 6, characterized in that After the step of determining the effective transit time window, it further includes: if the effective transit time window of a single observation task is discontinuous, decomposing it into one or more subtasks, and each subtask corresponds to a continuous effective shooting time period.

8. The SAR satellite scanning mode mission planning method for maximizing multi-region coverage rate according to claim 7, characterized in that, After decomposing into one or more subtasks, it further includes: dividing the continuous effective shooting time period of each subtask into multiple time slices, and pre-calculating the ground coverage geometric area corresponding to each time slice.

9. The SAR satellite scanning mode mission planning method for maximizing multi-region coverage rate according to claim 8, wherein After decomposing into one or more subtasks, it further includes: The step of determining the actual coverage area formed by the earth observation specifically includes: For each subtask, identify all the time slices included in the actual working time period of the payload. Perform a union operation on the ground coverage geometric regions corresponding to all the identified time slices, and then perform an intersection operation with the target geometric region to obtain the actual coverage region.

10. A SAR satellite scanning mode mission planning system for maximizing multi-region coverage rate, characterized in that, It includes: A data acquisition module configured to acquire geographic information of at least one target area to be observed, payload parameter information of a SAR satellite, and orbital information; wherein, the payload parameter information includes a set of multiple continuous wave positions in a scanning mode. An initialization module configured to initialize a population of an optimization algorithm based on swarm intelligence, where each individual in the population represents a mission planning scheme, and the parameters of the individual include the set of wave positions selected for one or more observation tasks and mission timing information. An iterative processing module configured to iteratively update the population until a preset iterative termination condition is met. A result output module configured to output the mission planning scheme represented by the individual corresponding to the optimal objective function value after the iteration ends.

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