SAR satellite strip mode task planning method and system oriented to maximum area coverage rate

Abstract

The invention discloses an SAR satellite strip mode task planning method, and aims to maximize the area coverage rate. According to the method, basic data containing time sequence geometric coverage primitives are obtained, and candidate planning schemes containing mixed parameters are initialized. During iterative optimization, an imaging interval is determined according to planning parameters, corresponding geometric primitives are identified, geometric merging calculation is executed to determine a planned coverage area, the area coverage performance is evaluated in combination with load constraint, and the parameters are updated by using an optimization algorithm. And outputting an optimal scheme after iteration. According to the method, through accurate geometric coverage calculation and constraint processing, the problems of inaccurate coverage estimation, incomplete constraint consideration and low planning efficiency are solved, and a feasible planning result with higher coverage rate and better performance can be quickly generated.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite mission planning, and in particular to a SAR satellite strip mode mission planning method and system for maximizing regional coverage. Background Art

[0002] With the development of satellite technology, especially the widespread application of SAR (Synthetic Aperture Radar) satellites in the field of Earth observation, how to efficiently plan satellite missions to meet the growing demand for observation has become an important research topic. Traditional SAR satellite mission planning methods usually have the following problems:

[0003] First, it's difficult to fully consider the actual operational requirements and scenarios of satellite payloads. It's often difficult to abstract complex payload constraints into an effective optimization model, leading to discrepancies between planning results and actual requirements. Existing mission planning algorithms lack effective modeling methods for the various practical constraints of satellite payloads, such as beam position, side view, operating mode, and continuous variables like power-up duration.

[0004] Secondly, when calculating coverage area, traditional methods typically project the target area onto a flat surface, divide it into grids, and then estimate the coverage area based on the grids. This method has limitations in accuracy and cannot accurately represent the corresponding capture area of the capture arc, especially when the target area is large or the satellite attitude is complex.

[0005] Furthermore, traditional serial computation methods are inefficient for planning tasks involving long timeframes and large amounts of data, making them difficult to meet the demands of rapid task planning. This is especially true when the population size and number of iterations are large, as serial execution results in a slow iteration rate, impacting task planning efficiency.

[0006] Therefore, how to provide a SAR satellite mission planning method that can fully consider payload constraints, accurately calculate coverage areas, and quickly generate mission planning solutions is a technical problem that needs to be urgently solved in this field.

[0007] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0008] In view of this, the present invention provides a SAR satellite strip mode mission planning method and system for maximizing regional coverage, so as to solve the problems in the existing satellite mission planning methods that are difficult to fully consider payload constraints, accurately calculate coverage areas, and quickly generate mission planning solutions, thereby improving the efficiency of satellite mission planning and the coverage of target areas.

[0009] The present invention provides a SAR satellite stripe mode mission planning method for maximizing regional coverage, comprising the following steps:

[0010] Obtain the basic data for mission planning, which includes: the definition of the geographic scope of the target area, the operating constraints of the SAR payload, the list of observation tasks to be planned, and the time-series ground cover geometry information pre-calculated for the tasks in the observation task list, associated with the satellite transit time and payload operation options;

[0011] Initialize a set of candidate mission planning schemes. Each candidate mission planning scheme assigns a set of planning parameters containing discrete and continuous values to the tasks in the observation mission list. The planning parameters are used to determine the imaging timing, imaging beam pointing, and working mode of the mission.

[0012] Iteratively optimize a set of candidate task plans. In each iteration, for each candidate task plan, perform:

[0013] Determine the planned imaging time interval for each observation mission based on the planning parameters it contains;

[0014] Based on the planned imaging time interval, identifying one or more ground cover geometric primitives corresponding to the interval from the time-serialized ground cover geometric primitive information;

[0015] Performing geometric merging calculations on the identified one or more ground cover geometric primitives to determine the planned coverage geographical area of the observation mission under this plan;

[0016] Based on the planned coverage geographical areas of all observation missions and combined with the SAR payload operation constraints, calculate the regional coverage performance evaluation value of the candidate mission planning scheme;

[0017] According to the regional coverage performance evaluation value, an optimization algorithm is used to update the planning parameters of the candidate mission planning scheme;

[0018] After the iteration is completed, the candidate mission planning scheme with the optimal area coverage performance evaluation value is output as the mission planning result.

[0019] In some optional embodiments, time-serialized ground cover geometric primitive information is generated for each observation mission, for each optional side-viewing direction of the satellite and each optional wave position combination, by dividing the effective transit time window into multiple time slices; each time slice contains a time sub-window and a set of geographic coordinate points defining a rectangular area on the ground.

[0020] In some optional embodiments, before initializing a set of candidate task planning solutions, a data preprocessing step is further included:

[0021] Analyze the time-series ground cover geometry information pre-calculated for each observation task and for different load working options in the basic data;

[0022] Based on the analysis of the presence or properties of geometric primitive information, a left-right view validity status value is determined for each observation task and each optional wave position combination to indicate whether only the left view, only the right view, both the left and right views can form effective ground coverage under this combination, or neither view can form effective ground coverage.

[0023] All determined left and right view validity status values are stored in a preset two-dimensional search structure.

[0024] In some optional embodiments, the step of calculating the area coverage performance evaluation value includes:

[0025] Identify all tasks in the candidate task planning schemes whose selected working mode is a dual-track mode;

[0026] For two temporally adjacent tasks selected for dual-orbit mode, determine whether the time difference between their planned shutdown and startup times meets the minimum orbital period time interval requirement;

[0027] Only dual-orbit mode missions that meet the minimum orbital period time interval requirement and the planned coverage geographical areas of all missions selected as non-dual-orbit mode missions are included in the calculation of the performance evaluation value.

[0028] In some optional embodiments, the regional coverage performance evaluation value is calculated based on the ratio of the total area of all planned coverage geographic areas included in the calculation to the area defined by the geographic scope of the target area.

[0029] In some optional embodiments, the discrete type values include: a selected wave position value, a selected side view direction value, and a selected working mode value; the continuous type values specifically include: a selected imaging duration value and a selected power-on time offset value.

[0030] In some optional embodiments, updating the planning parameters of the candidate mission planning solution using the optimization algorithm includes the step of updating the selected side view direction value, including:

[0031] Use the current task and its selected wave position value to query the two-dimensional search structure and obtain the corresponding left and right view validity status values;

[0032] Based on the obtained left and right view validity status values, constrain or guide the update of the selected side view direction value: if the status value is only valid on one side, update the side view direction value to the valid side; if the status value is valid on both sides, determine the updated side view direction value according to the optimization algorithm rules.

[0033] In some optional embodiments, updating the planning parameters of the candidate mission planning solution using the optimization algorithm includes updating the selected imaging duration value and the selected power-on time offset value, and also includes performing boundary checking and adjustment steps:

[0034] According to the working mode value selected for the current mission, the minimum and maximum allowable imaging time are obtained from the SAR payload operation constraints;

[0035] Update the selected imaging duration value and the selected power-on time offset value according to the transit duration corresponding to the current task;

[0036] Check whether the updated imaging duration value is between the minimum and maximum allowed imaging durations, check whether the updated power-on time offset value is non-negative, and check whether the sum of the updated imaging duration value and the power-on time offset value exceeds the transit time;

[0037] If the above conditions are not met, the updated imaging duration value and / or power-on time offset value is modified according to a predefined adjustment rule.

[0038] In some optional embodiments, the predefined adjustment rules include:

[0039] If the transit time is less than or equal to the maximum allowed imaging time, and the sum of the imaging time and the power-on time offset exceeds the transit time, the imaging time value is set to the transit time, and the power-on time offset value is set to 0;

[0040] If the imaging duration value is less than the minimum allowed imaging duration, it is set to the minimum allowed imaging duration;

[0041] If the imaging duration value is greater than the maximum allowable imaging duration, it is set to the maximum allowable imaging duration;

[0042] If the sum of the imaging time and the power-on time offset exceeds the transit time and the first rule condition is not met, then the power-on time offset value shall be re-determined while keeping the imaging time value not less than the minimum allowed imaging time so that the sum of the two does not exceed the transit time.

[0043] A SAR satellite stripe pattern mission planning system for maximum regional coverage, comprising:

[0044] The data interface module is used to obtain the basic data for mission planning. The basic data includes: the definition of the geographical scope of the target area, the operating constraints of the SAR payload, the list of observation tasks to be planned, and the time-series ground cover geometric primitive information pre-calculated for the tasks in the observation task list and associated with the satellite transit time and payload working options;

[0045] An initialization module, connected to the data interface module, is used to generate a set of candidate task planning schemes based on the task list. Each candidate task planning scheme assigns a set of planning parameters containing discrete and continuous values to the tasks in the observation task list. The planning parameters are used to determine the imaging timing, imaging beam pointing, and working mode of the task.

[0046] The iterative optimization processing module is connected to the initialization module and is used to iteratively optimize a set of candidate task planning solutions. The iterative optimization processing module is configured to perform the following operations for each candidate task planning solution in each iteration:

[0047] Determining a planned imaging time interval for each observation mission based on planning parameters included in the candidate mission planning scheme;

[0048] Based on the determined planned imaging time interval, identifying one or more ground cover geometric primitives corresponding to the interval from the time-serialized ground cover geometric primitive information;

[0049] Performing geometric merging calculations on the identified one or more ground cover geometric primitives to determine the planned coverage geographical area of the observation mission under this plan;

[0050] Based on the planned coverage geographical areas of all observation missions and combined with the SAR payload operation constraints, calculate the regional coverage performance evaluation value of the candidate mission planning scheme;

[0051] Based on the calculated area coverage performance evaluation value, the preset optimization algorithm rules are used to update the planning parameters of the candidate mission planning scheme;

[0052] The result output module is connected to the iterative optimization processing module and is used to output a candidate task planning scheme with the optimal area coverage performance evaluation value as the task planning result after the iteration is completed.

[0053] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

[0054] The SAR satellite stripe mode mission planning method and system for maximizing regional coverage of the present invention have the following beneficial effects:

[0055] The mission planning problem addressed by this invention essentially involves finding the optimal mission execution solution to maximize coverage of the target area while satisfying multiple constraints. This problem can be modeled as a combinatorial optimization problem, in which the mission execution parameters (such as imaging time, beam pointing, operating mode, etc.) constitute the decision variables, and coverage constitutes the objective function. Optimization algorithms, such as the particle swarm optimization (PSO), can effectively find solutions close to the optimal solution by simulating the search behavior of particles in the solution space. The PSO possesses global search capabilities and dynamic adaptability, enabling it to handle optimization problems in complex multi-constraint scenarios. Time-series ground coverage geometric primitives are used to characterize the ground coverage of the satellite at different times and under different payload operating options. This information is the basis for accurate coverage calculation. By combining these geometric primitives, the planned geographic coverage area of each mission under this plan can be determined. By comprehensively considering the planned coverage areas of all missions and incorporating the payload operating constraints, the coverage performance evaluation value of the candidate mission planning solutions can be calculated. Through continuous iterative optimization, the mission planning solution with the optimal coverage performance evaluation value can be found. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Other features, objects and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0057] Figure 1 This is a flowchart of a SAR satellite strip mode mission planning method for maximizing regional coverage according to an embodiment of the present invention;

[0058] Figure 2 This is a flow chart of a SAR satellite strip mode mission planning method for maximizing regional coverage according to another embodiment of the present invention;

[0059] Figure 3 It is a structural diagram of a SAR satellite strip mode mission planning system for maximizing regional coverage according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many 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 thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0061] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0062] The flowcharts shown in the accompanying drawings are merely exemplary and do not necessarily include all steps. For example, some steps may be decomposed, while some steps may be combined or partially combined. Therefore, the actual execution order may change according to actual circumstances.

[0063] The present invention provides a SAR satellite strip mode mission planning method and system for maximizing regional coverage. By comprehensively considering the various actual constraints of the satellite payload, mapping them into a combinatorial optimization problem of discrete random variables and continuous random variables, and constructing a multi-dimensional variable joint framework, the method and system can more effectively handle mission planning problems in complex multi-constraint scenarios. By adopting the time slicing method, the area of the shooting area corresponding to each mission arc segment can be calculated more accurately, thereby obtaining a more optimal mission planning solution. In addition, through parallel computing, the iteration speed can be effectively accelerated, and the efficiency of mission planning can be improved. Compared with the existing technology, the present invention can generate satellite mission planning solutions that are more in line with actual needs and have higher coverage, and improve the efficiency of mission planning.

[0064] like Figure 1 As shown, an embodiment of the present invention provides a SAR satellite strip mode mission planning method for maximizing regional coverage, the method comprising the following steps:

[0065] S100. Obtain basic mission planning data. This basic data includes the definition of the target area's geographic extent, SAR payload operational constraints, a list of observation missions to be planned, and pre-calculated, time-series ground coverage geometry information for the missions in the observation mission list, associated with the satellite transit time and payload operating options. The definition of the target area's geographic extent refers to defining the geographic boundaries of the target area to be covered by SAR imaging. For example, a series of longitude and latitude coordinates can be used to describe the scope of the target area. SAR payload operational constraints refer to various restrictions imposed on a SAR satellite when performing an imaging mission, such as minimum and maximum imaging durations and energy constraints. The list of observation missions to be planned refers to a series of SAR imaging missions to be performed in the target area. Time-series ground coverage geometry information refers to pre-calculated ground coverage information associated with the satellite transit time and payload operating options (such as side-view direction and beam position). This information is organized in a time-series manner for quick query and use during mission planning. Specifically, the ground coverage geometry can be a rectangle, polygon, or other geometric shape, and is used to approximate the instantaneous coverage of the SAR payload at a specific time and under a specific operating option. Pre-calculating and using time-serialized ground cover geometry information avoids repeated complex satellite-ground geometry calculations during mission planning, thereby improving mission planning efficiency.

[0066] S200, initialize a group of candidate task planning schemes, each candidate task planning scheme assigns a group of planning parameters containing discrete type values and continuous type values to the tasks in the observation task list, and the planning parameters are used to determine the imaging timing, imaging beam pointing and working mode of the task. Among them, the candidate task planning scheme refers to a group of possible task execution schemes, and each scheme defines the specific execution parameters of each observation task. Planning parameters refer to parameters used to determine the task execution method, including discrete type values (such as selected wave position, side viewing direction and working mode) and continuous type values (such as selected imaging duration and power-on time offset). By assigning a group of planning parameters to each task, a candidate task planning scheme can be fully described. One initialization method is: for each task, randomly select a feasible wave position, side viewing direction and working mode, and randomly generate imaging duration and power-on time offset within the allowed range. Other initialization methods can also be used, such as initialization based on historical data or prior knowledge to accelerate the optimization process.

[0067] S300, iteratively optimize a set of candidate task planning solutions. In each iteration, for each candidate task planning solution, perform the following operations:

[0068] S310. Determine a planned imaging time interval for each observation mission based on the included planning parameters. The planned imaging time interval refers to the time period during which the SAR payload will actually perform imaging, determined based on the selected power-up time offset and imaging duration. This time interval serves as the basis for identifying ground cover geometry in subsequent steps. For example, if a mission has a power-up time offset of 10 seconds and an imaging duration of 100 seconds, its planned imaging time interval is the entry time + 10 seconds and the departure time + 110 seconds.

[0069] S320: Based on the planned imaging time interval, identify one or more ground cover geometric primitives corresponding to the time interval from the time-series ground cover geometric primitive information. Specifically, the time-series ground cover geometric primitive information can be searched based on the start and end times of the planned imaging time interval to find all geometric primitives whose time windows overlap with the time interval. These geometric primitives represent the ground coverage of the SAR payload during the time interval.

[0070] S330. Perform a geometric merging calculation on the one or more identified ground coverage geometric primitives to determine the planned coverage geographic area of the observation task under this plan. Geometric merging calculation refers to merging multiple geometric primitives to obtain a new geometric primitive that can represent the overall coverage range. For example, multiple rectangular areas can be combined to obtain a polygonal area, which is the planned coverage geographic area. Other geometric operation methods, such as calculating intersection and difference, can also be used to meet different coverage requirements.

[0071] S340. Based on the planned coverage geographical areas of all observation tasks and in combination with the SAR payload operation constraints, calculate the regional coverage performance evaluation value of the candidate mission planning scheme. Among them, the regional coverage performance evaluation value is used to quantitatively evaluate the pros and cons of a candidate mission planning scheme. A commonly used evaluation method is to calculate the total area of the planned coverage geographical areas of all tasks, and compare it with the total area of the target area to obtain a coverage index. It is also possible to combine the SAR payload operation constraints to penalize mission planning schemes that do not meet the constraints to ensure the feasibility of the generated mission planning scheme. For example, if the interval time of the dual-track mode mission does not meet the minimum orbital period requirement, the evaluation value of the scheme is reduced. The calculation method of the evaluation value can be adjusted according to the specific application scenario, such as considering the priority of different areas, the weight of different tasks, etc.

[0072] S350: Based on the regional coverage performance evaluation value, an optimization algorithm is used to update the planning parameters of the candidate task planning scheme. An optimization algorithm refers to an algorithm used to find the optimal solution in the solution space. In the present invention, a variety of optimization algorithms can be used, such as particle swarm optimization, genetic algorithm, simulated annealing algorithm, etc. The particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence. It searches for the optimal solution in the solution space by simulating the foraging behavior of flocks of birds or schools of fish. In each iteration, the particles update their own speed and position based on their own historical optimal position and the historical optimal position of the group, thereby continuously approaching the optimal solution.

[0073] S400: After the iteration is completed, the candidate mission planning scheme with the optimal area coverage performance evaluation value is output as the mission planning result. The mission planning result is the optimal mission execution scheme sought by the present invention, which can maximize the coverage of the target area and meet the operation constraints of the SAR payload.

[0074] The method provided in this embodiment, by acquiring basic data containing time-series ground coverage geometric primitive information, can effectively avoid repeated satellite-ground geometry calculations during mission planning, thereby improving planning efficiency. By iteratively optimizing candidate mission planning schemes and selecting them based on regional coverage performance evaluation values, a mission planning scheme that can maximize coverage of the target area can be effectively generated. In addition, because the operational constraints of the SAR payload are taken into account during the evaluation process, the generated mission planning scheme has a high degree of feasibility. Therefore, the use of this technical means can improve the efficiency of satellite mission planning and the coverage rate of the target area.

[0075] In some embodiments, time-series ground coverage geometric primitive information is generated for each observation mission, for each optional side-looking direction and each optional beam position combination of the satellite, by dividing the effective transit time window into multiple time slices. Each time slice contains a time sub-window and a set of geographic coordinate points defining a rectangular ground area. The time-series ground coverage geometric primitive information is used to characterize the satellite's ground coverage at different times and under different payload operating options. For each optional side-looking direction and each optional beam position combination of the satellite, a more comprehensive description of the satellite's coverage capability can be provided. The effective transit time window refers to the time range during which the satellite can effectively observe the target area. The start and end times of this time window are affected by factors such as the satellite's orbit, the target area's location, and the payload operating options. Time slicing involves dividing the effective transit time window into multiple smaller time periods, each corresponding to a time sub-window and a set of geographic coordinate points defining a rectangular ground area. Specifically, the length of the time slice can be adjusted based on actual needs, for example, to 1 second, 10 seconds, or longer. Shorter time slices provide a more accurate approximation of the ground coverage, but also increase the computational complexity. One method of generating time slices is: first determine the start time and end time of the valid transit time window, and then divide the time window into multiple time slices, where the time sub-window of each time slice is the start time and end time of the time slice. Then, for each time slice, calculate the ground coverage of the SAR payload within the time sub-window, and use a set of geographic coordinate points that define a rectangular area on the ground to approximate the coverage. The coordinates of the four corner points of the rectangular area can be obtained through satellite-ground geometry calculations. Other geometric shapes can also be used to approximate the ground coverage, such as polygons, circles, etc. This step reduces the computational complexity and facilitates subsequent geometric merging calculations by dividing the valid transit time window into multiple time slices and using simple geometric shapes to approximate the ground coverage of each time slice.

[0076] This embodiment uses time slicing to decompose complex ground coverage calculations into multiple simple geometric calculations, reducing computational complexity and improving mission planning efficiency. Furthermore, generating time slices for each combination of optional side-view directions and optional beam positions provides a more comprehensive description of the satellite's coverage capabilities, providing more options for mission planning and improving coverage of the target area.

[0077] In some embodiments, before initializing a set of candidate mission planning solutions, a data preprocessing step is further included:

[0078] Analyze the time-series ground cover geometry information pre-calculated for each observation task and for different load working options in the basic data;

[0079] Based on the analysis of the presence or properties of geometric primitive information, a left-right view validity status value is determined for each observation task and each optional wave position combination to indicate whether only the left view, only the right view, both the left and right views can form effective ground coverage under this combination, or neither view can form effective ground coverage.

[0080] All determined left and right view validity status values are stored in a preset two-dimensional search structure.

[0081] The data preprocessing step analyzes and processes the basic data to provide more effective information for subsequent mission planning. Analyzing the time-series ground cover geometry precomputed for each observation mission and for different payload options in the basic data involves checking the precomputed geometry, for example, to determine whether valid geometry exists or whether its properties meet requirements. The left-right view validity status value indicates whether, for a specific observation mission and wave position combination, only the left side view, only the right side view, both the left and right views, or neither view can provide valid ground coverage. This status value helps the mission planning algorithm quickly determine the feasibility of the side view direction, thereby reducing unnecessary computation. For example, if the status value is "only left side view valid," the mission planning algorithm can directly select the left side view without attempting the right side view. A two-dimensional lookup structure refers to a data structure, such as a two-dimensional array or hash table, used to store the left-right view validity status values. Storing the status values in a two-dimensional lookup structure allows for convenient and fast querying. Specifically, a two-dimensional array indexed by the observation mission ID and wave position ID can be constructed, with each element in the array storing the corresponding left-right view validity status value. The preprocessing step can effectively reduce the search space and eliminate invalid solutions.

[0082] This embodiment uses a preprocessing step to quickly determine the feasibility of side-view directions, thereby reducing unnecessary calculations and improving mission planning efficiency. By storing the left and right view validity status values in a two-dimensional lookup structure, rapid queries can be performed, further improving mission planning efficiency. Furthermore, the preprocessing step can help eliminate invalid solutions, thereby improving mission planning quality.

[0083] In some embodiments, the step of calculating the area coverage performance evaluation value includes:

[0084] Identify all tasks in the candidate task planning schemes whose selected working mode is a dual-track mode;

[0085] For two temporally adjacent tasks selected for dual-orbit mode, determine whether the time difference between their planned shutdown and startup times meets the minimum orbital period time interval requirement;

[0086] Only dual-orbit mode missions that meet the minimum orbital period time interval requirement and the planned coverage geographical areas of all missions selected as non-dual-orbit mode missions are included in the calculation of the performance evaluation value.

[0087] Dual-orbit mode refers to an operating mode in which a SAR satellite is powered on at most once during two orbits of the Earth. The minimum orbital interval requirement is the minimum time interval that must be met between two consecutive dual-orbit mode missions to ensure the energy constraints of the satellite payload. This minimum orbital interval requirement can be calculated based on the specific satellite parameters and the payload's energy consumption, for example, setting it to the time it takes for the satellite to orbit the Earth once.

[0088] Specifically, the method for calculating the regional coverage performance evaluation value is as follows: first, identify all tasks in the candidate task planning scheme whose selected working mode is the dual-track mode, and then, for two temporally adjacent dual-track mode tasks, calculate the time difference between their planned shutdown time and startup time. If the time difference is less than the minimum orbital cycle time interval requirement, the scheme is considered to not meet the energy constraint and is penalized, such as reducing its evaluation value. Finally, only the dual-track mode tasks that meet the minimum orbital cycle time interval requirement and all planned coverage geographical areas selected as non-dual-track mode tasks are included in the calculation of the performance evaluation value. Other methods can also be used to deal with the energy constraints of dual-track mode tasks, such as directly constraining the interval time of dual-track mode tasks in the optimization algorithm, or avoiding generating schemes that do not meet the energy constraints when generating candidate task planning schemes.

[0089] This embodiment, by considering the energy constraints of dual-track tasks, ensures the feasibility of the generated task planning solutions and avoids task execution failures due to insufficient energy. By penalizing solutions that do not meet the energy constraints, the optimization algorithm can be guided to find a more optimal task planning solution.

[0090] In some embodiments, the regional coverage performance evaluation value is calculated based on the ratio of the total area of all planned geographic coverage areas included in the calculation to the area defined by the target area's geographic scope. The regional coverage performance evaluation value is used to quantitatively assess the quality of a candidate mission planning solution. The total area of the planned geographic coverage area refers to the sum of the areas of the planned geographic coverage areas for all selected tasks, namely, dual-track mode tasks that meet energy constraints, and all non-dual-track mode tasks. The area defined by the target area's geographic scope refers to the total area of the target area to be covered by SAR imaging. By calculating the ratio of the total area of the planned geographic coverage area to the area defined by the target area's geographic scope, a coverage ratio indicator can be obtained, which can intuitively reflect the coverage effectiveness of the mission planning solution. For example, if the ratio of the total area of the planned geographic coverage area to the area defined by the target area's geographic scope is 0.9, it means that the mission planning solution can cover 90% of the target area. Specifically, the regional coverage performance evaluation value is calculated as follows: first, the union of each planned geographic coverage area is calculated, and the intersection of the result with the target area is calculated to obtain the total area of all planned geographic coverage areas within the target area. Then, the total area of the planned geographic coverage area is calculated using a polygon area calculation method. Finally, the total area of the planned geographic coverage is compared with the area defined by the target region's geographic scope to obtain a coverage ratio indicator, which is used as the regional coverage performance evaluation value. Polygonal area calculation methods can utilize a variety of existing techniques, such as the shoelace formula and Green's formula. If the priority of different regions is taken into account, the coverage areas of different regions can be weighted and then accumulated to obtain the weighted total area of the planned geographic coverage area. The evaluation calculation method can be adjusted according to the specific application scenario, such as taking into account the weighting of different tasks.

[0091] This embodiment uses the coverage ratio metric as the regional coverage performance evaluation value, which can intuitively reflect the coverage effect of the mission planning solution and facilitate the selection of the optimization algorithm. By combining it with the polygon area calculation method, the area of the planned geographical coverage area can be accurately calculated, thereby improving the accuracy of the evaluation. In addition, this embodiment can flexibly adjust the calculation method of the evaluation value to meet different application requirements.

[0092] In some embodiments, discrete values include: a selected beam position value, a selected side view direction value, and a selected operating mode value; continuous values specifically include: a selected imaging duration value and a selected power-on time offset value. Discrete and continuous values are two types of planning parameters. Discrete values refer to parameters that can only take on a finite number of discrete values. For example, beam position values can only be selected from a preset beam position set, side view directions can only select left or right view, and operating modes can only select single-track mode or dual-track mode. Continuous values refer to parameters whose values can continuously vary within a certain range. For example, imaging duration values can continuously vary between the minimum and maximum allowable imaging durations, and power-on time offset values can continuously vary between 0 and the transit time. By classifying planning parameters into discrete and continuous values, the SAR satellite mission planning problem can be more effectively modeled. Discrete values can be optimized using discrete optimization algorithms, such as genetic algorithms and simulated annealing algorithms. Continuous values can be optimized using continuous optimization algorithms, such as gradient descent and Newton's method. Hybrid optimization algorithms can also be used to optimize both discrete and continuous values. For example, in a particle swarm optimization algorithm, a discrete update rule can be used for discrete values, and a continuous update rule can be used for continuous values.

[0093] For example, the beam position value can be selected from the preset beam position set {1, 2, 3, 4}, the side view direction value can be selected from the set {left view, right view}, and the operating mode value can be selected from the set {single track mode, dual track mode}. The imaging duration value can continuously vary between the minimum allowable imaging duration and the maximum allowable imaging duration, for example, [100 seconds, 200 seconds]. The power-on time offset value can continuously vary between 0 and the transit duration, for example, [0 seconds, 300 seconds].

[0094] By classifying planning parameters into discrete and continuous values, this embodiment allows for more efficient modeling of SAR satellite mission planning problems and facilitates the use of appropriate optimization algorithms. By properly selecting the ranges of discrete and continuous values, the size of the search space can be effectively controlled, improving mission planning efficiency.

[0095] In some embodiments, updating the planning parameters of the candidate mission planning solution using the optimization algorithm includes the step of updating the selected side view direction value, including:

[0096] Use the current task and its selected wave position value to query the two-dimensional search structure and obtain the corresponding left and right view validity status values;

[0097] Based on the obtained left and right view validity status values, constrain or guide the update of the selected side view direction value:

[0098] If the status value is that only one side is valid, the side view direction value is updated to the valid side;

[0099] If the status value is valid on both sides, the updated side view direction value is determined according to the optimization algorithm rules.

[0100] Among them, the two-dimensional search structure refers to a data structure for storing the left and right view validity status values, such as a two-dimensional array, a hash table, etc. The left and right view validity status values are used to characterize whether only the left view, only the right view, both the left and right views can form effective ground coverage, or both views cannot form effective ground coverage under a specific observation task and wave position combination. If the status value is "only one side valid", it means that only by selecting this side view direction can effective ground coverage be obtained, so the side view direction value needs to be updated to the valid side. If the status value is "both sides valid", it means that either the left view or the right view can be selected to obtain effective ground coverage. At this time, the updated side view direction value can be determined according to the optimization algorithm rules (such as random selection, or selection based on historical experience). This step can effectively constrain or guide the update of the side view direction value by utilizing the pre-calculated left and right view validity status values, thereby reducing unnecessary calculations and improving the efficiency of task planning.

[0101] For example, if the left and right view validity status for the current mission and its selected wave position is "Left view only valid," the side view direction value is updated to "Left view." If the status value is "Both sides valid," the optimization algorithm can be used to randomly select either the left or right view, or to select the side view direction with higher coverage based on historical experience.

[0102] This embodiment utilizes pre-calculated left and right view validity status values to effectively constrain or guide the update of the side view direction value, thereby reducing unnecessary calculations and improving mission planning efficiency. This method also ensures the feasibility of the generated mission planning solution and improves coverage of the target area.

[0103] In some embodiments, updating the planning parameters of the candidate mission planning solution using the optimization algorithm includes updating a selected imaging duration value and a selected power-on time offset value, and also includes performing boundary checking and adjustment steps:

[0104] According to the working mode value selected for the current mission, the minimum and maximum allowable imaging time are obtained from the SAR payload operation constraints;

[0105] According to the transit time corresponding to the current task, the selected imaging time value and the selected power-on time offset value are changed;

[0106] Check whether the updated imaging duration value is between the minimum and maximum allowed imaging durations, check whether the updated power-on time offset value is non-negative, and check whether the sum of the updated imaging duration value and the power-on time offset value exceeds the transit time;

[0107] If the above conditions are not met, the updated imaging duration value and / or power-on time offset value is modified according to a predefined adjustment rule.

[0108] Among them, the boundary check and adjustment steps are used to ensure that the updated imaging duration value and the power-on time offset value meet the operating constraints of the SAR payload. The minimum and maximum allowed imaging durations refer to the minimum and maximum imaging times allowed for the SAR payload in single-track mode or dual-track mode. The transit time refers to the length of time the satellite conducts effective observations of the target area. Boundary checking refers to checking the updated imaging duration value and the power-on time offset value to determine whether they meet the following conditions: the imaging duration value is between the minimum and maximum allowed imaging durations, the power-on time offset value is non-negative, and the sum of the imaging duration value and the power-on time offset value does not exceed the transit time. Adjustment rules refer to rules for modifying the updated imaging duration value and the power-on time offset value when they do not meet the above conditions. Specifically, the adjustment rule is as follows: if the transit time is less than or equal to the maximum allowable imaging time, and the sum of the imaging time and the power-on time offset exceeds the transit time, the imaging time value is set to the transit time, and the power-on time offset value is set to 0; if the imaging time value is less than the minimum allowable imaging time, it is set to the minimum allowable imaging time; if the imaging time value is greater than the maximum allowable imaging time, it is set to the maximum allowable imaging time; if the sum of the imaging time and the power-on time offset exceeds the transit time and the first rule condition is not met, then, while maintaining the imaging time value at or above the minimum allowable imaging time, the power-on time offset value is re-determined so that the sum of the two does not exceed the transit time. Other adjustment rules can also be used, such as adjustments based on historical experience or more complex optimization algorithms.

[0109] This embodiment, by performing boundary checking and adjustment steps, ensures that the updated imaging duration and power-on time offset values meet the operational constraints of the SAR payload, thereby guaranteeing the feasibility of the generated mission plan. By employing reasonable adjustment rules, mission failures due to parameter out-of-bounds can be avoided and coverage of the target area can be improved.

[0110] In some embodiments, the predefined adjustment rules include:

[0111] If the transit time is less than or equal to the maximum allowed imaging time, and the sum of the imaging time and the power-on time offset exceeds the transit time, the imaging time value is set to the transit time, and the power-on time offset value is set to 0;

[0112] If the imaging duration value is less than the minimum allowed imaging duration, it is set to the minimum allowed imaging duration;

[0113] If the imaging duration value is greater than the maximum allowable imaging duration, it is set to the maximum allowable imaging duration;

[0114] If the sum of the imaging time and the power-on time offset exceeds the transit time and the first rule condition is not met, then the power-on time offset value shall be re-determined while keeping the imaging time value not less than the minimum allowed imaging time so that the sum of the two does not exceed the transit time.

[0115] These predefined adjustment rules are designed to ensure that the imaging duration and power-on time offset are both physically and technically feasible. Specifically, the first rule, "If the transit time is less than or equal to the maximum allowable imaging time, and the sum of the imaging time and the power-on time offset exceeds the transit time, then the imaging duration value is set to the transit time, and the power-on time offset value is set to 0," means that even if the payload starts working from the moment of entry until the moment of departure, if the power-on time is still less than the maximum allowable power-on time for the payload, then the payload should continue working until the moment of departure to fully utilize the transit opportunity, and the power-on time offset is set to 0, indicating that it will start immediately from the moment of entry. The second rule, "If the imaging duration value is less than the minimum allowable imaging duration, then set it to the minimum allowable imaging duration," ensures that there will be no unfinished imaging missions that are too short in the mission planning plan. The third rule, "If the imaging duration value is greater than the maximum allowable imaging duration, then set it to the maximum allowable imaging duration," ensures that the imaging time does not exceed the maximum time allowed by the payload itself. The fourth rule, "If the sum of the imaging duration and the power-on time offset exceeds the transit time and the conditions of the first rule are not met, then, while maintaining the imaging duration at least as long as the minimum allowable imaging duration, the power-on time offset is re-determined so that the sum of the two does not exceed the transit time," adjusts parameters in more complex situations. First, ensure that the imaging duration is no less than the minimum allowable imaging duration to maximize the completion of meaningful imaging tasks. Then, adjust the power-on time to ensure that the entire imaging process can be completed within the transit time. In actual applications, these adjustment rules can be modified or supplemented based on the specific characteristics of the SAR payload and mission requirements.

[0116] This embodiment uses predefined adjustment rules to ensure the rationality of parameters such as imaging duration and power-on time offset, thereby ensuring the feasibility of the mission planning solution and improving the coverage of the target area. By adopting simple and effective adjustment rules, the computational complexity can be reduced and the efficiency of mission planning can be improved.

[0117] like Figure 2FIG. 1 is an implementation method of a SAR satellite stripe mode mission planning method for maximizing regional coverage provided by another embodiment of the present invention, comprising the following steps:

[0118] S1. Obtain the target observation area information set, payload information set, observation mission information set and the corresponding orbit recursion information set, and the time slice information set of each mission.

[0119] S11. Obtain target observation area information.

[0120] Target={P1,…,P z ,…,P n_point}, n_point is the number of longitude and latitude coordinate points;

[0121] P z Indicates the zth longitude and latitude coordinate point, P z =[Lon z ,Lat z ], Lon z is the longitude value of the z-th coordinate point, Lat z The latitude value of the z-th coordinate point.

[0122] For example, Target = {[88.9, 20.4], [88.9, 40.0], [114.7, 40.0], [114.7, 20.4]}.

[0123] S12. Obtain load information.

[0124] The side view set q = {1, 2}, 1 means the satellite takes the left view, and 2 means the satellite takes the right view.

[0125] Band = {1,…,b,…,n_band}, where n_band is the total number of bands;

[0126] The time it takes for a satellite to orbit the earth is T_Circle seconds;

[0127] Mode set Mode = {0, 1}, subject to the energy constraints of the payload. 0 is single-orbit mode, in which the payload can be powered on once within T_Circle seconds, with the minimum power-on time being Single_Min seconds and the maximum power-on time being Single_Max seconds. 1 is dual-orbit mode, in which the payload can be powered on at most once within 2*T_Circle seconds, the time it takes the satellite to orbit the earth twice, with the maximum power-on time being Double_Min seconds and Double_Max seconds.

[0128] In dual-track mode, if the interval between the shutdown time of the current cycle and the startup time of the next cycle is less than T_Circle seconds, the energy constraint of the load is not met and the next cycle cannot be started.

[0129] For example, q = {1, 2}, Band = {1, 2, 3, 4}, the satellite orbit time is 5400 seconds, Mode = {0, 1}, in single-orbit mode, the minimum power-on time for the payload is 100 seconds, and the maximum power-on time is 200 seconds; in dual-orbit mode, the maximum power-on time for the payload is 200 seconds, and the maximum power-on time is 300 seconds.

[0130] S13. Obtain observation mission information.

[0131] Observation task set Task qb ={1,..,i qb ...,n_task}, where i qb is the i-th observation task, which takes the side view q and wave position b, where n_task is the total number of observation tasks. qb ={1,2,3,4,5,6,7}.

[0132] S14. Obtain the mission orbit recursion information of the observation mission i_qb.

[0133] i qb =[flag i ,tw i ,d i ,s i ] represents observation task i qb Track recursion information, where flag i For observation task i qb Is it a sign of effective transit when looking sideways at q and wave position b? i For observation task i qb The transit time window, d i For observation task i qb The transit time, s i For observation task i qb Time slice information;

[0134] Border crossing sign i = [true, false], true means valid transit, which intersects with the target area, false means invalid transit, which does not intersect with the target area;

[0135] Time window tw i =[start i ,end i ],start i For observation task i qb Entry time, endi For observation task i qb departure time;

[0136] Transit time i =start i -end i ;

[0137] Time Slice Collection in, For observation task i qb The jth time slice, k is the observation task i qb The number of time slices.

[0138] For example, when seven tasks adopt left or right viewing and adopt orbit recursion information at wave position 1, 2, 3, or 4, each observation task has a total of 2*4=8 combinations, and all observation tasks have a total of 7*8=56 combinations. Taking one of the combinations as an example, for example, when the second transit task adopts right viewing and wave position 3, the orbit recursion information is [true, [2024-8-16-03-13-43, 2024-08-16-03-20-27], 404.33, s2]. For an example of time slice s2, see S15.

[0139] S15 Get observation task i qb The j-th time slice information

[0140] in represents observation task i qb The time window of the j-th slice, represents observation task i qb The coverage area of the j-th slice;

[0141] in For observation task i qb The entry moment of the j-th time slice, For observation task i qb The departure time of the j-th time slice;

[0142] represents observation task i qb The four longitude and latitude coordinate points of the covering rectangular area of the j-th time slice are arranged in a counterclockwise direction.

[0143] For example, if a time slice is created every 10 seconds, the information for one time slice is [[2024-08-16-03-15-33,2024-08-16-03-15-43],[[106.7,30.8],[106.87,30.6],[107.3,31.0],[107.2,31.2]]], which corresponds to the four longitude and latitude coordinates of the shooting rectangle range for the time slice from 3:15:33 to 3:15:43 on August 16, 2024. Multiple time slices constitute the time slice set s2 in S14.

[0144] S2 data preprocessing and population initialization.

[0145] S21 processes the transit time of a certain wave position in a certain circle under a certain left and right view

[0146] S211 constructs a three-dimensional table structure T[q][k][m], where the number of the first dimension is 2, the number of the second dimension is n_band, and the number of the third dimension is n_task, where q = {1, 2}, k∈{1, 2,…, n_band}, and m∈{1, 2,…, n_task}.

[0147] Each cell T[q][k][m] of the three-dimensional table structure is used to store the transit time of the mth observation task taking the wave position k when the side view is q.

[0148] S212 Traversal Collection Task qb , for observation task i qb have,

[0149] If i 1b If flagi=false, then T[1][k][m]=-1, which means there is no intersection with the target area, the load is not turned on, and it is an invalid value;

[0150] If i 2b If flagi=false, then T[2][k][m]=-1, which means there is no intersection with the target area, the load is not turned on, and it is an invalid value;

[0151] If i 1b If flagi=true, then T[1][k][m]=d i ;

[0152] If i 2b If flagi=true, then T[2][k][m]=d i .

[0153] For example

[0154] Take the left view T[1][k][m] (keep 1 decimal place):

[0155]

[0156] For example:

[0157] T[1][2][3] means the third round with left view and wave position 2, which has no intersection with the target area and the payload is not powered on;

[0158] T[1][4][6] indicates the observation mission of the 6th round with left view and wave position 4, and the transit time is 465.4 seconds.

[0159] When taking right view, T[2][k][m] (keep 1 decimal place):

[0160]

[0161]

[0162] For example:

[0163] T[2][2][4] means taking the right view, the fourth round of the wave position is 2, there is no intersection with the target area, and the payload is not turned on;

[0164] T[2][1][5] indicates the observation mission of the 5th round with right view and wave position 1, and the transit time is 352.3 seconds.

[0165] The summary is as follows: take full account of the actual situation, adopt different side views and different wave positions, and the transit situation may be different.

[0166] When left-view is adopted, the wave positions of the 3rd and 4th circles have no intersection with the target area, so they are all recorded as -1, and the remaining circles and wave positions are all in transit;

[0167] When right viewing is adopted, the wave positions 1 and 2 in the 4th round have no intersection with the target area, so they are both recorded as -1. The wave positions in the 7th round have no intersection with the target area, so they are all recorded as -1.

[0168] S22 determines the left and right viewing situation of a certain wave position in a certain circle

[0169] S221 constructs a two-dimensional table structure Tilt[k][m], the number of the first dimension is n_band, the number of the second dimension is n_task, where k∈{1,2,…,n_band}, m∈{1,2,…,n_task}.

[0170] Each cell Tilt[k][m] of the two-dimensional table structure is used to store the transit status of the m-th observation task taking wave position k.

[0171] S222 Traversing the collection Task qb , judge the observation task i qb Under the wave position b, take the transit situation of different side views q,

[0172] If i 1b flag i =false, and i 2b flag i =false, then Tilt[k][m]=3, that is, the left view and the right view have no intersection with the target area;

[0173] If i 1b flag i =false, and i 2b flag i =true, then Tilt[k][m]=2, that is, the left view has no intersection with the target area, but the right view has an intersection;

[0174] If i 1b flag i =true, and i 2b flag i =false, then Tilt[k][m]=1, that is, the left view has an intersection with the target area, but the right view has no intersection;

[0175] If i 1b flag i =true, and i 2b flag i =true, then Tilt[k][m]=0, that is, both the left view and the right view intersect with the target area.

[0176] Left and right view of the border crossing:

[0177]

[0178] For example:

[0179] Tilt[1][2] indicates the observation task of the second round with the wave position of 1. When looking left or right, it intersects with the target area.

[0180] Tilt[3][3] indicates the observation task of the third round with wave position 3, where the left view has no intersection with the target area, but the right view has an intersection;

[0181] Tilt[2][7] indicates the observation task of the 7th round with the wave position of 2, where the left view intersects with the target area, while the right view does not intersect.

[0182] Combined with S21, it is not difficult to conclude that

[0183] For rounds 1, 2, 5, and 6, any side view and any wave position intersects with the target area;

[0184] For round 3, only when the right view is taken, each wave position intersects with the target area;

[0185] For round 4, when wave positions 1 and 2 are adopted, both the left view and the right view have no intersection with the target area. When wave positions 3 and 4 are adopted, only the right view has an intersection with the target area.

[0186] For round 7, only when the left view is taken, the various wave positions intersect with the target area.

[0187] S23. Population initialization.

[0188] S231. Create a population containing 100 particles and set the number of iterations to 200 generations.

[0189] Each particle contains the position and velocity values of discrete random variables and continuous random variables of all observation tasks. The position and velocity of each component of each particle are randomly initialized in the search space, and the learning factors C1 and C2 are assigned a value of 1.49.

[0190] The position matrix of each particle is:

[0191]

[0192] where t i represents the observation time of the i-th observation task, Δt i represents the duration of the i-th observation task from the entry time to the start time, b i represents the wave position of the i-th observation task, q i Indicates the side view of the i-th observation task, mode i represents the pattern of the i-th observation task (i∈[1,…,n_task]).

[0193] The velocity matrix of each particle is:

[0194]

[0195] in Represents the variable t i The speed value, Represents the variable Δt i Speed value.

[0196] S3. Single particle update.

[0197] S31. Update of discrete random variables in the i-th observation task for a single particle (iterative wave position, side view and mode).

[0198] S311, randomly select a wave position b i ∈{1,2,3,4}.

[0199] S312, in order to speed up the convergence speed, the wave position b of the i-th task i Make a judgment,

[0200] If Tilt[b i ][i]=0, then randomly select left view or right view q i ∈{1,2};

[0201] If Tilt[b i ][i]=1, then q i =1, that is, the left view is selected first;

[0202] If Tilt[b i ][i]=2, then q i =2, that is, the right view is selected first.

[0203] S313, random selection mode mode i ∈{0,1}.

[0204] S32, update the continuous random variable t in the i-th observation task of a single particle i and Δt i upper and lower limits.

[0205] S321 0≤Δt i ≤T[q i ][b i ][i], that is, the time from the entry time to the start time must be within the transit range of the i-th observation mission, and That is, side view q i and wave position b i The transit time of the i-th observation mission.

[0206] S322 if mode i =0, then 100≤t i ≤200, that is, the startup time t of the i-th observation task i Meet the minimum and maximum power-on times in single-track mode, and

[0207]

[0208] If mode i =1, then 200≤t i≤300, that is, the startup time t of the i-th observation task i Meet the minimum boot time and maximum boot time in dual-track mode, and S323, traverse Position, repeat S321 and S322, and get:

[0209] The position lower limit matrix of the continuous random variable of the current particle is:

[0210]

[0211] The position upper limit matrix of the continuous random variable of the current particle is:

[0212]

[0213] S33 updates the continuous random variable t in the i-th observation task of a single particle i and Δt i : is the continuous random variable t of the current particle i and Δt i The optimal position, is the continuous random variable t of the particle swarm i and Δt i The global optimal position of .

[0214] The update formula for continuous random variables velocity and position is:

[0215]

[0216] Where w is the weight, R1 i 、R2 i is a random number.

[0217] S34, boundary processing.

[0218] S341, if That is, the transit time does not exceed the maximum power-on time of the single-track or dual-track mode. Δt i =0, the load is on all the time;

[0219] like and Then randomly generate like and but

[0220] S342. The duration from the time of entry to the time of shutdown is: Δt i +t i .

[0221] like Then randomly generate S35 updates all observation tasks for a single particle

[0222] Repeat S31 to S34 to obtain:

[0223] The current particle position matrix is:

[0224]

[0225] The velocity matrix of the current particle is:

[0226]

[0227] S36. Plan the payload work time for the i-th observation mission.

[0228] The planned load start time is: Start_Sar i =start i +Δt i ;

[0229] Load startup time: t i ;

[0230] The planned load shutdown time is: End_Sar i =start i +Δt i +t i .

[0231] S37. Store the latitude and longitude point set of the shooting area of the i-th observation mission.

[0232] S371. Traverse the time slice set And for the empty set temp at each iteration, if That is, if the boot time is greater than or equal to the start time of the jth time slice, then temp starts to be put into

[0233] until That is, if the shutdown time is within the starting time range of the j+xth time slice, then temp is last put into Therefore, the point set of the shooting area is:

[0234] S372. Union the longitude and latitude points in temp to obtain the longitude and latitude point set of the i-th observation task. i ,in A set of longitude and latitude points arranged counterclockwise.

[0235] S38. Calculate the union of the longitude and latitude sets of all mission shooting areas.

[0236] S381. Repeat S36 and S37 to obtain the payload working planning time for each observation task of the current particle and the set of longitude and latitude points of the corresponding shooting areas.

[0237] S382. The union of the longitude and latitude sets of all task shooting areas is Union, which is initially an empty set.

[0238] Traverse the position matrix Position of the current particle.

[0239] If mode i = 0 and flag i = true, then add Union i to Union and find the union of Union;

[0240] If mode i = 1 and flag i = true, and Start_Sar i+1 - End_Sar i ≥ T_Circle, that is, in the dual-track mode, the time interval between adjacent orbits is greater than the time for the satellite to orbit the earth once, meeting the energy constraint, then flag i+1 = true, add Union i to Union and find the union of Union;

[0241] If mode i = 1 and flag i = true, and Start_Sar i+1 - End_Sar i < T_Circle, that is, in the dual-track mode, the time interval between 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, and the payload of the (i + 1)-th observation task will not be powered on.

[0242] After the traversal, obtain the union of the longitude and latitude sets of all task shooting areas, that is, Union = [Union1, Union2,..., Union y , and the number of elements in the set is y.

[0243] S4. Calculate the objective function and fitness function.

[0244] S41. Let the set of longitude and latitude points be Lon_Lat, and the area function be S(Lon_Lat).

[0245] S(Lon_Lat) is: traversing the polygon longitude and latitude point sequence, using the polygon area calculation method, by converting the longitude and latitude coordinates into radians, and combining the earth's curvature characteristics, accurately calculate the area of the target observation area.

[0246] S42. Calculate the objective function.

[0247]

[0248] S43. Calculate the fitness function.

[0249] AF=(Coverage+1) 3 -1

[0250] Save the global optimal position of each variable when AF is maximized, that is, and and corresponding

[0251] S5. Parallel calculation to update all particles.

[0252] Assign the 100 particles in the population to 10 threads. Each thread repeatedly executes S35, S38, S42, and S43. The 10 threads execute in parallel until all particles are updated.

[0253] S6. Complete all iterations and obtain the optimal plan.

[0254] S61. Repeat S51 until the number of iterations is reached.

[0255] S62. Obtain the optimal plan for the i-th observation mission.

[0256] If flag i = true, then the i-th observation task planning is:

[0257] The planned load startup time is:

[0258] Load startup time:

[0259] The planned load shutdown time is:

[0260] Wave position:

[0261] Side view:

[0262] model:

[0263] If flag i=false, then the i-th observation mission will be started without load planning.

[0264] S63. Repeat S62 to obtain the optimal plan for all observation tasks.

[0265] The process of obtaining the optimal planning of all observation tasks by executing the above method through a computer program is as follows: First transit: Entry time: 2024-08-16 01:37:30, Exit time: 2024-08-16 01:41:52, Transit duration: 261.83s.

[0266] The first transit mission plan: Start time: 2024-08-16 01:37:30, shutdown time: 2024-08-16 01:41:52, start time: 261.83s, wave position: 3, left / right view: left view, single / dual track: dual track.

[0267] Second transit: Entry time: 2024-08-16 03:13:43, Exit time: 2024-08-16 03:20:27, transit duration: 404.33s.

[0268] The second transit mission plan: Start time: 2024-08-16 03:17:13, shutdown time: 2024-08-16 03:19:30, start time: 136.90s, wave position: 3, left / right view: right view, single / dual track: single track.

[0269] 3rd border crossing: Entry time: 2024-08-16 04:53:05, Exit time: 2024-08-16 04:56:12, Border crossing duration: 186.76s.

[0270] The third transit mission plan: Start time: 2024-08-16 04:53:05, shutdown time: 2024-08-16 04:55:05, start time: 120.00s, wave position: 1, left / right view: right view, single / dual track: single track.

[0271] The fourth transit was not powered on and there was no mission planning.

[0272] 5th border crossing: Entry time: 2024-08-16 08:09:33, Exit time: 2024-08-16 08:15:30, Border crossing duration: 356.19s.

[0273] The fifth transit mission plan: Start time: 2024-08-16 08:09:43, shutdown time: 2024-08-16 08:14:33, start time: 290.00s, wave position: 2, left / right view: right view, single / dual track: dual track.

[0274] 6th border crossing: Entry time: 2024-08-16 09:47:24, Exit time: 2024-08-16 09:55:09, Border crossing duration: 465.44s.

[0275] The sixth transit mission plan: Start time: 2024-08-16 09:49:46, shutdown time: 2024-08-16 09:54:46, start time: 300.00s, wave position: 4, left / right view: left view, single / dual track: dual track.

[0276] 7th border crossing: Entry time: 2024-08-16 11:27:27, Exit time: 2024-08-16 11:28:29, Border crossing duration: 62.00s.

[0277] The 7th transit was not powered on and there was no mission planned.

[0278] like Figure 3 As shown, an embodiment of the present invention further provides a SAR satellite strip mode mission planning system for maximizing regional coverage, comprising:

[0279] Data interface module M100 is used to obtain basic data for mission planning. This basic data includes: the definition of the geographic scope of the target area, SAR payload operational constraints, a list of observation tasks to be planned, and pre-calculated time-series ground coverage geometry information associated with satellite transit times and payload operating options for the tasks in the observation task list. Data interface module M100 is responsible for obtaining the basic data required for mission planning from external systems. The definition of the geographic scope of the target area refers to the definition of the geographic boundaries of the target area to be covered by SAR imaging. For example, the scope of the target area can be described using a series of longitude and latitude coordinates or defined using a standard Geographic Information System (GIS) format file. SAR payload operational constraints refer to the various restrictions imposed on the SAR satellite when performing imaging missions, such as minimum and maximum imaging durations, energy constraints, and attitude maneuverability constraints. The list of observation tasks to be planned is a series of SAR imaging tasks to be performed in the target area. Each task can include information such as the task ID, priority, and desired imaging time window. Time-series ground cover geometry information refers to pre-calculated ground cover information associated with satellite transit times and payload operating options (such as side-view direction and beam position). This information is organized in a time-series format for quick query and use during mission planning. The data interface module M100 can obtain this basic data in a variety of ways, such as from a database, from a file, or from other systems via a network interface. To ensure data integrity and accuracy, the data interface module M100 can also perform verification and cleansing on the acquired data.

[0280] Initialization module M200, connected to data interface module M100, is responsible for generating a set of candidate mission planning schemes based on the task list. Each candidate mission planning scheme assigns a set of planning parameters, including both discrete and continuous values, to each task in the observation task list. These planning parameters are used to determine the imaging timing, imaging beam pointing, and operating mode of the task. Initialization module M200 is responsible for generating an initial set of candidate mission planning schemes based on the acquired observation task list. A candidate mission planning scheme is a set of possible mission execution scenarios, each defining the specific execution parameters for each observation task. Planning parameters are parameters used to determine the execution method of the task and include both discrete values (such as the selected wavefront, side-view direction, and operating mode) and continuous values (such as the selected imaging duration and power-on time offset). Initialization module M200 can generate the initial candidate mission planning schemes using a variety of methods, such as random generation, generation based on historical data, or generation based on heuristic rules. A commonly used initialization method is to randomly select a feasible wavefront, side-view direction, and operating mode for each task, and then randomly generate the imaging duration and power-on time offset within an allowable range. In order to improve the efficiency of task planning, the initialization module M200 can generate multiple candidate task planning solutions in parallel.

[0281] The iterative optimization processing module M300 is connected to the initialization module M200 and is used to iteratively optimize a set of candidate mission planning schemes. In each iteration, the iterative optimization processing module M300 is configured to perform the following operations for each candidate mission planning scheme: determine a planned imaging time interval for each observation mission based on the planning parameters contained in the candidate mission planning scheme; identify one or more ground cover geometric primitives corresponding to the interval from the time-series ground cover geometric primitive information based on the determined planned imaging time interval; perform a geometric merging calculation on the identified one or more ground cover geometric primitives to determine the planned coverage geographic area of the observation mission under this plan; calculate the regional coverage performance evaluation value of the candidate mission planning scheme based on the planned coverage geographic areas of all observation missions and in combination with the SAR payload operation constraints; and update the planning parameters of the candidate mission planning scheme using a preset optimization algorithm based on the calculated regional coverage performance evaluation value. The iterative optimization processing module M300 is the core module of the mission planning system and is responsible for iteratively optimizing the initial candidate mission planning schemes to find the optimal mission execution plan. This module first determines a planned imaging time interval for each observation mission based on the planning parameters of each candidate mission plan. Then, based on the determined planned imaging time interval, it identifies one or more ground cover geometric primitives corresponding to that interval from pre-computed time-series ground cover geometric primitive information. Next, the module performs a geometric merging calculation on the identified ground cover geometric primitives to determine the planned geographic area for the observation mission under this plan. Next, based on the planned geographic areas of all observation missions and incorporating the SAR payload operational constraints, the module calculates a regional coverage performance evaluation value for the candidate mission plan. This evaluation value is used to quantitatively assess the quality of the candidate mission plan. Finally, based on the calculated regional coverage performance evaluation value, the module applies a pre-set optimization algorithm to update the planning parameters of the candidate mission plan. By continuously iterating this process, the iterative optimization processing module M300 can gradually improve the quality of the candidate mission plans and ultimately identify the optimal mission execution plan. Common optimization algorithms include particle swarm optimization, genetic algorithm, and simulated annealing. To improve optimization efficiency, the iterative optimization processing module M300 can process multiple candidate mission plans in parallel.

[0282] The result output module M400 is connected to the iterative optimization processing module M300, and is used to output the candidate task planning scheme with the optimal area coverage performance evaluation value as the task planning result after the iteration is completed. The result output module M400 is responsible for selecting the scheme with the optimal area coverage performance evaluation value from all candidate task planning schemes after the iteration is completed, and outputting the scheme as the final task planning result. The output task planning results may include information such as the power-on time, power-off time, wave position, side viewing direction, working mode, etc. of each task. The result output module M400 can output the task planning results in a variety of formats, such as text files, XML files, or database records. In addition, the result output module M400 can also display the task planning results in a visual manner, such as displaying the coverage area of each task on a map.

[0283] The system provided in this embodiment acquires basic mission planning data through a data interface module M100, ensuring data integrity and accuracy. An initialization module M200 generates a set of candidate mission planning solutions, providing a starting point for subsequent optimization. An iterative optimization processing module M300 iteratively optimizes these candidate mission planning solutions, effectively improving mission planning quality. Finally, a result output module M400 outputs the final mission planning results for user convenience. Therefore, this technical approach can effectively improve the efficiency of satellite mission planning and target area coverage.

[0284] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A SAR satellite stripe pattern mission planning method for maximizing regional coverage, characterized in that: The following steps are included: Obtaining basic mission planning data, the basic data including: a definition of the geographic extent of the target area, SAR payload operation constraints, a list of observation tasks to be planned, and pre-calculated time-series ground cover geometry information for the tasks in the observation task list, associated with satellite transit times and payload operation options; Initializing a set of candidate task planning schemes, each of the candidate task planning schemes assigning a set of planning parameters including discrete and continuous values to a task in the observation task list, wherein the planning parameters are used to determine the imaging timing, imaging beam pointing, and operating mode of the task; Iteratively optimize the candidate task planning solutions. In each iteration, for each candidate task planning solution, perform: Determining a planned imaging time interval for each observation task based on the planning parameters contained therein; Based on the planned imaging time interval, identifying one or more ground cover geometric primitives corresponding to the interval from the time-serialized ground cover geometric primitive information; Performing a geometric merging calculation on the identified one or more ground cover geometric primitives to determine a planned coverage geographical area of the observation mission under the plan; Calculate the regional coverage performance evaluation value of the candidate mission planning scheme based on the planned coverage geographical areas of all observation missions and in combination with the SAR payload operation constraints; According to the area coverage performance evaluation value, an optimization algorithm is used to update the planning parameters of the candidate mission planning scheme; After the iteration is completed, the candidate mission planning scheme with the optimal area coverage performance evaluation value is output as the mission planning result.

2. The SAR satellite stripe pattern mission planning method for maximizing regional coverage according to claim 1 is characterized in that: The time-serialized ground cover geometric primitive information is generated for each observation mission, for each optional side-view direction of the satellite and each optional wave position combination, by dividing the effective transit time window into multiple time slices; each time slice contains a time sub-window and a set of geographic coordinate points that define a rectangular area on the ground.

3. The SAR satellite stripe pattern mission planning method for maximizing regional coverage according to claim 1 is characterized in that: Before initializing a set of candidate task planning solutions, a data preprocessing step is also included: Analyzing the time-series ground cover geometry information pre-calculated for each observation task and for different load working options in the basic data; Based on the analysis of the presence or properties of the geometric primitive information, a left-right view validity state value is determined for each combination of the observation task and each optional wave position, to indicate whether only the left view, only the right view, both the left and right views can form effective ground coverage under the combination, or neither view can form effective ground coverage; All the determined left-right view validity status values are stored in a preset two-dimensional search structure.

4. The SAR satellite stripe pattern mission planning method for maximizing regional coverage according to claim 1 is characterized in that: The step of calculating the area coverage performance evaluation value comprises: Identify all tasks in the candidate task planning schemes whose selected working mode is a dual-track mode; For two temporally adjacent tasks selected for dual-orbit mode, determine whether the time difference between their planned shutdown and startup times meets the minimum orbital period time interval requirement; Only dual-orbit mode missions that meet the minimum orbital period time interval requirement and the planned coverage geographical areas of all missions selected as non-dual-orbit mode missions are included in the calculation of the performance evaluation value.

5. The SAR satellite stripe pattern mission planning method for maximizing regional coverage according to claim 1 is characterized in that: The regional coverage performance evaluation value is calculated based on the ratio of the total area of all planned coverage geographical areas included in the calculation to the area defined by the geographical scope of the target area.

6. The SAR satellite stripe pattern mission planning method for maximizing regional coverage according to claim 1 is characterized in that: The discrete type values include: a selected wave position value, a selected side view direction value, and a selected working mode value; the continuous type values specifically include: a selected imaging duration value and a selected power-on time offset value.

7. The SAR satellite stripe pattern mission planning method for maximizing regional coverage according to claim 6, characterized in that: Updating the planning parameters of the candidate mission planning solution using an optimization algorithm includes the step of updating the selected side view direction value, including: Using the current task and its selected wave position value to query the two-dimensional search structure, obtain the corresponding left and right view validity state values; Based on the obtained left and right view validity status values, constrain or guide the update of the selected side view direction value: if the status value is valid for only one side, update the side view direction value to the valid side; if the status value is valid for both sides, determine the updated side view direction value according to the optimization algorithm rules.

8. The SAR satellite stripe pattern mission planning method for maximizing regional coverage according to claim 6, characterized in that: Updating the planning parameters of the candidate task planning scheme using the optimization algorithm includes updating the selected imaging duration value and the selected power-on time offset value, and also includes performing boundary checking and adjustment steps: According to the working mode value selected for the current mission, the minimum and maximum allowable imaging durations are obtained from the SAR payload operating constraints; updating the selected imaging duration value and the selected power-on time offset value according to the transit duration corresponding to the current task; checking whether the updated imaging duration value is between the minimum and maximum allowed imaging durations, checking whether the updated power-on time offset value is non-negative, and checking whether the sum of the updated imaging duration value and the power-on time offset value exceeds the transit duration; If the above conditions are not met, the updated imaging duration value and / or power-on time offset value is modified according to a predefined adjustment rule.

9. The SAR satellite stripe pattern mission planning method for maximizing regional coverage according to claim 8, characterized in that: The predefined adjustment rules include: If the transit time is less than or equal to the maximum allowed imaging time, and the sum of the imaging time and the power-on time offset exceeds the transit time, the imaging time value is set to the transit time, and the power-on time offset value is set to 0; If the imaging duration value is less than the minimum allowed imaging duration, it is set to the minimum allowed imaging duration; If the imaging duration value is greater than the maximum allowable imaging duration, it is set to the maximum allowable imaging duration; If the sum of the imaging time and the power-on time offset exceeds the transit time and the first rule condition is not met, then the power-on time offset value shall be re-determined while keeping the imaging time value not less than the minimum allowed imaging time so that the sum of the two does not exceed the transit time.

10. A SAR satellite strip mode mission planning system for maximum regional coverage, characterized by: include, A data interface module is configured to obtain basic mission planning data, including: a definition of the geographic extent of the target area, SAR payload operation constraints, a list of observation tasks to be planned, and time-series ground cover geometric primitive information pre-calculated for the tasks in the observation task list and associated with satellite transit times and payload operation options; an initialization module connected to the data interface module, and configured to generate a set of candidate task planning schemes based on the task list, each of the candidate task planning schemes assigning a set of planning parameters comprising discrete type values and continuous type values to a task in the observation task list, wherein the planning parameters are used to determine the imaging timing, imaging beam pointing, and working mode of the task; An iterative optimization processing module is connected to the initialization module and is used to iteratively optimize the set of candidate task planning solutions. The iterative optimization processing module is configured to perform the following operations for each candidate task planning solution in each iteration: Determining a planned imaging time interval for each observation task based on the planning parameters included in the candidate task planning scheme; Based on the determined planned imaging time interval, identifying one or more ground cover geometric primitives corresponding to the interval from the time-serialized ground cover geometric primitive information; Performing a geometric merging calculation on the identified one or more ground cover geometric primitives to determine a planned coverage geographical area of the observation mission under the plan; Calculate the regional coverage performance evaluation value of the candidate mission planning scheme based on the planned coverage geographical areas of all observation missions and in combination with the SAR payload operation constraints; According to the calculated area coverage performance evaluation value, the planning parameters of the candidate task planning scheme are updated using a preset optimization algorithm rule; The result output module is connected to the iterative optimization processing module and is used to output a candidate task planning scheme with an optimal area coverage performance evaluation value as a task planning result after the iteration is completed.

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