A heterogeneous sar constellation cooperative multi-angle observation task planning method and device

Abstract

This invention provides a method and apparatus for planning multi-angle observation missions in a heterogeneous SAR constellation, comprising: acquiring the ground trajectory centerlines of each SAR satellite in the heterogeneous SAR constellation during the planning period, and determining the intersection points of the ground trajectory centerlines; filtering the intersection points to obtain candidate cooperative points that satisfy geometric constraints; generating a local connectivity graph based on the candidate cooperative points, and performing a heuristic search on the local connectivity graph according to preset rules to obtain the cooperative observation area; and performing multi-objective optimization of the cooperative observation area based on cooperative constraint requirements and the task to be observed to obtain the target observation area. This scheme achieves efficient utilization of heterogeneous SAR resources, and can actively search for effective multi-angle observation areas given satellite resources, thereby improving planning efficiency and observation quality.

Description

Technical Field

[0001] This invention relates to the field of satellite remote sensing mission planning technology, and in particular to a method and apparatus for planning multi-angle observation missions in a heterogeneous SAR constellation. Background Technology

[0002] Synthetic Aperture Radar (SAR) has become an indispensable means of Earth observation due to its all-weather, all-day imaging capabilities. With the large-scale deployment and commercial application of global SAR constellations (such as ICEYE and Capella), utilizing multiple heterogeneous SAR constellations to conduct multi-angle observations of the same target within a short time baseline can significantly improve the accuracy of ground feature classification, 3D reconstruction, and target identification.

[0003] Heterogeneous constellations composed of satellites from different SAR satellite constellations exhibit significant diversity in orbital inclination, sensor performance, and transit time. However, systematically transforming this diversity of globally available resources into high-quality, multi-angle observation opportunities remains a key challenge in current mission planning. Traditional mission planning methods largely follow a demand-driven, passive scheduling model, validating requests for specific geographic areas. This model is not only inefficient but also fails to proactively assess global observation potential. Furthermore, when dealing with large-scale spatiotemporal searches and multi-target conflict optimization problems, it often struggles to balance satellite cost, execution efficiency, and observational geometric quality.

[0004] Therefore, there is an urgent need for a method and device for planning multi-angle observation missions in heterogeneous SAR constellations. Summary of the Invention

[0005] This invention provides a method and apparatus for planning multi-angle observation missions in heterogeneous SAR constellations, which solves the problems of low efficiency of passive scheduling and difficulty in finding the optimal trade-off solution among multiple conflicting targets in the planning of heterogeneous constellations for collaborative observation.

[0006] In a first aspect, embodiments of the present invention provide a method for planning heterogeneous SAR constellation collaborative multi-angle observation missions, including: Obtain the ground trajectory centerline of each SAR satellite in the heterogeneous SAR constellation within the planning period, and determine the intersection of the ground trajectory centerlines; The intersection points are filtered to obtain candidate cooperative points that satisfy the geometric constraints; A local connectivity graph is generated based on the candidate cooperating points, and a heuristic search is performed on the local connectivity graph according to preset rules to obtain the cooperating observation area; Based on the collaborative constraint requirements and the task to be observed, the collaborative observation area is optimized in multiple objectives to obtain the target observation area.

[0007] Secondly, embodiments of the present invention also provide a heterogeneous SAR constellation collaborative multi-angle observation mission planning device, comprising: The acquisition module is used to acquire the ground trajectory centerline of each SAR satellite in the heterogeneous SAR constellation within the planning period, and to determine the intersection of the ground trajectory centerlines; An opportunity identification module is used to filter the intersection points to obtain candidate cooperative points that meet the geometric constraints; and to generate a local connectivity graph based on the candidate cooperative points, and to perform a heuristic search on the local connectivity graph according to preset rules to obtain the cooperative observation area; The multi-objective optimization module is used to perform multi-objective optimization on the collaborative observation area based on collaborative constraint requirements and the task to be observed, so as to obtain the target observation area.

[0008] Thirdly, embodiments of the present invention also provide a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the heterogeneous SAR constellation collaborative multi-angle observation mission planning method described in any of the above claims.

[0009] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon, which, when executed in a computer, causes the computer to execute the heterogeneous SAR constellation collaborative multi-angle observation mission planning method described in any of the above claims.

[0010] Fifthly, embodiments of the present invention also provide a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the method described in any of the first aspects of this specification.

[0011] This invention provides a method and apparatus for planning multi-angle observation missions in a heterogeneous SAR constellation. It obtains the intersection points of the ground trajectory centerlines of several SAR satellites within a planning period, then determines candidate collaborative points based on geometric constraints, generates a local connectivity graph based on these candidate points, and performs a heuristic search according to preset rules to connect candidate collaborative points on valid paths to obtain a closed collaborative observation area. Finally, based on preset collaborative requirements and the tasks to be observed, the collaborative observation area is optimized using multiple objectives to obtain the final target observation area, thus completing the planning of the multi-angle observation mission. In this way, this invention successfully solves the nondeterministic polynomial problem in multi-satellite collaborative planning by using continuous search discretization and introducing multi-stage screening, significantly improving planning efficiency. It also proactively assesses global observation potential and automatically balances satellite scheduling costs and the azimuth diversity of observation geometry through multi-objective optimization, enabling the target observation area to maximize information acquisition dimensions while also considering economy and timeliness. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a heterogeneous SAR constellation collaborative multi-angle observation mission planning method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the geometric definition of a candidate cooperative point provided in an embodiment of the present invention; Figure 3 , Figure 4 This is a schematic diagram of a growth process of obtaining a polygonal region from candidate cooperating points through trajectory growth, according to an embodiment of the present invention. Figure 5 This is an embodiment of the present invention that provides a precise observable area obtained by using a heterogeneous SAR constellation collaborative multi-angle observation mission planning method; Figure 6 This is a diagram illustrating the actual data collection situation provided in an embodiment of the present invention; Figure 7 yes Figure 5 Images of different land cover categories acquired by the Haishao-1 satellite corresponding to the precisely observable area; Figure 8 yes Figure 5 Images of different land cover categories acquired by the Haishao-2 satellite corresponding to the precisely observable area; Figure 9 yes Figure 5 Images of different land cover categories acquired by the Tianyi-41 satellite corresponding to the precisely observable area; Figure 10 This is a hardware architecture diagram of a computing device provided in an embodiment of the present invention; Figure 11 This is a structural diagram of a heterogeneous SAR constellation collaborative multi-angle observation mission planning device provided in an embodiment of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0015] The following is the concept of the present invention, such as Figure 1 As shown, this embodiment of the invention provides a method for planning heterogeneous SAR constellation collaborative multi-angle observation missions, the method comprising: Step 100: Obtain the ground trajectory centerline of each SAR satellite in the heterogeneous SAR constellation within the planning period, and determine the intersection of the ground trajectory centerlines; Step 102: Filter the intersection points to obtain candidate cooperating points that satisfy the geometric constraints; Step 104: Generate a local connectivity graph based on candidate cooperating points, and perform a heuristic search on the local connectivity graph according to preset rules to obtain the cooperating observation area; Step 106: Based on the collaborative constraint requirements and the task to be observed, perform multi-objective optimization on the collaborative observation area to obtain the target observation area.

[0016] In this embodiment of the invention, the intersection points of the ground trajectory centerlines of several SAR satellites within the planning period are obtained. Candidate collaborative points are then determined based on geometric constraints, and a local connectivity map is generated based on these candidate collaborative points. A heuristic search is then performed according to preset rules to connect the candidate collaborative points on the effective paths to obtain a closed collaborative observation area. Finally, based on preset collaborative requirements and the tasks to be observed, the collaborative observation area is optimized using multiple objectives to obtain the final target observation area, thus completing the planning of the multi-angle observation task. In this way, the invention successfully solves the nondeterministic polynomial problem in multi-satellite collaborative planning by using continuous search discretization and introducing multi-stage screening, significantly improving planning efficiency. Simultaneously, it proactively assesses the observation potential globally and automatically balances satellite scheduling costs and the azimuth diversity of observation geometry through multi-objective optimization, enabling the target observation area to maximize the information acquisition dimensions while also considering economy and timeliness.

[0017] The following description Figure 1 The execution method of each step is shown.

[0018] In step 100, the ground trajectory centerline is the line pointing from the beam center to the ground trajectory, i.e., the centerline of the area where the satellite beam sweeps across the ground, equivalent to the satellite's field of view. The SAR satellites are any deployed satellites, without constraints on orbit type or downward viewing angle; multiple SAR satellites form a constellation. The intersection point here is the intersection of any two ground trajectory centerlines.

[0019] For step 102, the intersection points are screened to obtain candidate cooperative points that satisfy the geometric constraints, including: On the center line of the ground trajectory, establish an edge whose length is no greater than the preset neighborhood radius, obtained by connecting any two intersection points; For each intersection point, find the first intersection point adjacent to the intersection point on the intersecting trajectory of the center line of the ground trajectory; and determine the first intersection point that forms the edge by connecting with the intersection point as the neighborhood intersection point; Based on the neighborhood intersection and the intersection, an interior angle is formed with the intersection as the center point; Determine whether the interior angle is within the preset angle range; If the judgment result is yes, then the neighborhood intersection point that constitutes the interior angle and the intersection point are determined as candidate cooperative points.

[0020] In a preferred embodiment, the geometric constraint condition includes that the side length of the edge is not greater than a preset neighborhood radius; The preset neighborhood radius is determined by the following formula: in, The preset neighborhood radius; For the first i The strip width of each SAR satellite; This is the maximum azimuth interval threshold.

[0021] It should be noted that `min()` is a function that takes the minimum value; in the formula above, it refers to taking the minimum strip width among all SAR satellites. The maximum azimuth interval threshold is determined based on the user's acquisition requirements. The above formula is a sufficient condition for connecting the side length and azimuth diversity.

[0022] In this embodiment of the invention, the set of all intersection points is taken as a vertex set. For each intersection point in the vertex set, an edge is established only if the intersection points satisfy the following conditions: they are physically located on the same ground trajectory centerline and the spherical distance is less than a preset neighborhood radius. That is, the edge is located on the ground trajectory centerline, resulting in a sparse local connectivity graph. In this way, redundant long-distance connections are removed through this geometric constraint, and invalid combinations are filtered out in the initial stage, which greatly reduces the complexity of subsequent searches to determine candidate cooperative points and polygonal regions, and greatly shortens the time for discovering observation opportunities.

[0023] In a preferred embodiment, the preset angle range is a supplementary angle greater than the maximum azimuth angle interval threshold and less than π.

[0024] Specifically, the interior angle formed by the center point and the intersection of its two neighboring points needs to satisfy the following condition: θ ∈( θ min ,π), where, At this point, the interior angle will be determined as a valid convex interior angle. The intersection point constituting the interior angle, along with the intersection points of its two neighboring areas and related information, will be encapsulated to obtain candidate cooperative points. If the interior angle obtained with this intersection point as the center does not satisfy... θ ∈( θ min If the intersection point is π, then the above judgment should be performed on the remaining intersection points until the judgment of the interior angles obtained with all intersection points as the center point is completed, and the final candidate cooperating points are obtained. Therefore, the number of candidate cooperating points is less than the number of intersection points.

[0025] In a preferred embodiment, the candidate cooperating point includes information about the central intersection, interior angles, information about neighboring intersections, and the turning direction constituting the interior angles; the information about the central intersection includes a unique identifier, geographic coordinates, the IDs of the two trajectories forming the intersection, SAR satellite information, and the time each trajectory passes through the central intersection. The interior angle is formed by connecting the first neighborhood intersection point, the center intersection point, and the second neighborhood intersection point in sequence.

[0026] Specifically, such as Figure 2 As shown, for example, regarding the intersection point V center On the center line of the ground track a track b The above determines the V adjacent to this intersection point. prev V next IP i IP j IP k The information obtained from step 102 is located in track. a track b The upper edge has V prev V center IP k V prev V center V next V next IP i Therefore, with V center Connecting the edges yields V, the neighborhood intersections of which are obtained. prev V next Therefore, V center Connect V sequentially with the center point. prev Vcenter V next The interior angles can be obtained. θ It was determined that the interior angle satisfies... θ ∈( θ min ,π), at this time determine V prev V center V next This is a candidate collaborative point. The data structure of this candidate collaborative point includes V. center Information, interior angle values, V prev (Front vertex) and V next ID and turning direction of the (rear vertex); V center The information of the intersection includes a unique identifier (ID), geographic coordinates (i.e., latitude and longitude coordinates), and the track that formed the intersection. a track b The IDs of the two SAR satellites forming the intersection, the sensor IDs, the orbit IDs, and the track IDs. a track b The time it takes for the trajectory to pass through the intersection point. In this embodiment, an interior angle formed in a counter-clockwise direction can be defined as a left turn, marked with a turning direction of +1; an interior angle formed in a clockwise direction can be defined as a right turn, marked with a turning direction of -1. It should be noted that different intersection points have different IDs; different ground trajectory centerlines also have different IDs.

[0027] In step 104, a local connectivity graph is generated based on the candidate cooperative points, and a heuristic search is performed on the local connectivity graph according to preset rules to obtain the cooperative observation region, including: For each candidate cooperative point, the following steps are performed: each intersection point in the candidate cooperative point is treated as a node, and at each node, the next neighboring intersection point is selected in the same direction as the turning direction. The current interior angle of the intersection point centered on the node is obtained. When the current interior angle is within the preset angle range, the next neighboring intersection point is determined to be a valid intersection point. The node is sequentially connected to the valid node to complete one trajectory growth. The next trajectory growth is performed with the valid intersection point as a node until the search is completed and the nodes are sequentially connected to the first neighboring intersection point to obtain a polygonal region. Obtain the participating SAR satellites and satellite beam pointing directions corresponding to the ground trajectory centerline that forms the polygonal region; The polygonal region was observed from each of the participating SAR satellites to obtain verification results; When the verification result indicates that pointing observation is achievable, determine whether there is a target point in the polygonal region that satisfies a preset constraint. If the judgment result is yes, then the polygonal region is determined to be a collaborative observation region.

[0028] In a preferred embodiment, for each of the candidate cooperative points, the following is performed: S1: Sequentially connect the first neighboring intersection point, the intersection point and the second neighboring intersection point in the candidate cooperative point to obtain a local connection diagram and a first interior angle, and when the first interior angle is within the preset angle range, connect the second neighboring intersection point and the first neighboring intersection point; S2: On the center line of the ground trajectory where the intersection point and the second neighboring intersection point are located, determine the third neighboring intersection point based on the edge; S3: Using the third neighborhood intersection point as a node, select a fourth neighborhood intersection point at the node in a direction consistent with the turning direction; S4: The second interior angle is obtained based on the intersection of the neighboring nodes on the center line of the ground trajectory, the intersection of the node and the fourth neighboring node; S5: When the second interior angle is within the preset angle range, connect the node corresponding to the second interior angle and the intersection point of the fourth neighborhood, and use the node corresponding to the second interior angle and the intersection point of the fourth neighborhood as the intersection point and the second neighborhood intersection point for the next trajectory growth, respectively. Return to step S2 until the search is completed and the nodes are sequentially connected to the first neighborhood intersection point.

[0029] It should be noted that the preset angle range is ( θ min (π). Furthermore, the edges connected in step 106 all satisfy geometric constraints, meaning trajectory growth is performed based on the edges and preset rules. The number of collaborative observation regions is less than the number of polygonal regions. The polygonal regions are preferably convex polygonal regions.

[0030] In this invention, a set of candidate cooperating points is used as input, and a breadth-first search strategy is employed to generate polygonal regions. This strategy performs a heuristic traversal of the graph according to preset rules, searching for closed paths that meet the requirements. By forcibly selecting a direction consistent with the initial turning direction of the candidate cooperating point at each node, it ensures that the generated path naturally forms a closed convex polygon, thus obtaining the polygonal region generated by that candidate cooperating point. Finally, for all polygonal regions, a polygon hash signature mechanism is used to eliminate duplicate closed paths.

[0031] Specifically, such as Figures 3 to 4As shown, taking the candidate cooperative point ORC (turning direction is left) as an example, ∠ORC is formed by connecting points O, R, and C in sequence. The following steps are executed: S1: Based on points R, C, and O, obtain the interior angle ∠RCO. When ∠RCO satisfies the condition of a supplementary angle greater than the maximum azimuth angle interval threshold and less than π, connect points C and O in sequence to complete one trajectory growth; S2: On the ground trajectory centerline where points R and C are located, determine the third neighbor intersection points as points N and D based on the edges; S3: Using the third neighbor intersection point N as a node, select the fourth neighbor intersection point B; using the third neighbor intersection point D... Point C is selected as a node, and the fourth neighbor intersection point B is chosen; S4: Based on points C, N, and B, the second interior angle ∠CNB is obtained, and based on points N, D, and B, the second interior angle ∠NDB is obtained; S5: It is determined that only ∠CNB satisfies the condition of being a supplementary angle greater than the maximum azimuth angle interval threshold and less than π. Therefore, points N and B are connected sequentially to complete one trajectory growth; and the node N corresponding to ∠CNB and the fourth neighbor intersection point B are used as the intersection point and the second neighbor intersection point for the next trajectory growth, respectively. The process returns to step S2 until the search is complete and the nodes are sequentially connected to the first neighbor intersection point, resulting in a polygonal region. Figure 3 The intersection trajectory grows to Figure 4 As can be seen from the polygon region generation, point D was removed in step S5, and the final polygon region obtained after the search is completed is polygons RCO, RNBH, and RNBLP.

[0032] In a preferred embodiment, pointing observations are performed on the polygonal region on each of the participating SAR satellites to obtain verification results. This includes: first, comparing and verifying the attitude control capability boundaries and sensor field-of-view constraints of the participating SAR satellites with the azimuth angles calculated from the cooperative observation area; second, determining whether there are target points in the verified polygonal region such that all participating SAR satellites satisfy their respective pointing constraints. If the determination result is yes, the verification result is that pointing observations can be achieved; if the determination result is no, the verification result is that pointing observations cannot be achieved.

[0033] In a preferred embodiment, the preset constraint is expressed by the following formula: in, , , All are the first i Parameters of the ground trajectory centerline equation for a SAR satellite For the first i Half-width of a stripe for a SAR satellite; The geographic location of the target point within the polygonal region.

[0034] It should be noted that the aforementioned pre-defined constraints indicate that the necessary and sufficient condition for the existence of an N-fold intersection region on a two-dimensional plane is the feasibility problem of having at least one target point satisfying a system of linear inequalities. Specifically, after verifying that all participating satellite platforms can physically achieve pointing observations, the existence of a non-empty geographic common intersection among the multiple observation strips of all participating SAR satellites is confirmed by solving the aforementioned inequalities. That is, if the judgment result is yes, a non-empty geographic common intersection is considered to exist, and the polygonal region that has been verified and screened is determined as a valid potential collaborative observation area.

[0035] It should be noted that, Figures 2 to 4 The lines in the diagram represent the center lines of the ground trajectory, and the intersections represent the intersections of the center lines of the ground trajectory.

[0036] In this invention, geometric constraints and graph topological priors are prioritized for rapid dimensionality reduction during the planning phase, significantly reducing the geometric computational overhead of invalid combinations. Through this decoupling strategy, when handling the discovery of large-scale constellation joint observation opportunities, the system can not only proactively reveal potential high-value collaborative observation areas globally from a resource-centric perspective, possessing proactive opportunity discovery capabilities and greatly improving the certainty and proactivity of mission discovery; it can also compress the exhaustive calculations that originally required hundreds of seconds to the second level, significantly improving the engineering response speed.

[0037] In step 106, multi-objective optimization is performed on the cooperative observation area based on cooperative constraint requirements and the task to be observed, to obtain the target observation area, including: A1: Initialize the population based on the cooperative observation region; wherein, each individual in the population corresponds to an observation region including at least one cooperative observation region, and the observation region satisfies the cooperative constraint requirement; A2: Construct a multi-objective optimization model based on the task to be observed and the SAR satellites corresponding to the observation area; A3: Based on the multi-objective optimization model, perform selection, crossover, and mutation operations on the initial population to generate a progeny population; A4: Filter the initial population and offspring population to obtain a new generation of parent population; A5: Use the new generation of parent population as the initial population for the next iteration, repeat steps A3 to A4 until the iteration termination condition is met, and output the optimal set of individuals. A6: Select the best individual from the set of best individuals, and determine the observation area corresponding to the best individual as the target observation area.

[0038] It should be noted that the cooperative constraint requirements include the minimum number of viewing angles, the maximum time interval between two SAR satellites, and the maximum azimuth interval threshold. Step A1 also includes chromosome encoding, which uses an integer encoding scheme to define the representation, length, and gene meaning of individuals in the population. The gene value is the index ID of the cooperative observation area. An individual can be an observation area consisting of one cooperative observation area, two cooperative observation areas, or n cooperative observation areas, where n is less than or equal to the number of cooperative observation areas. In step A3, a multi-objective optimization model is used to perform non-dominated sorting on the initial population to divide it into non-dominated levels, and the crowding distance between individuals at the same level is calculated. Then, selection, crossover, and mutation operations are performed based on the non-dominated sorting results and crowding distances to generate the offspring population. In step A4, the initial population and the offspring population are merged. The merged population is then subjected to non-dominated sorting and crowding distance calculations again. Individuals are selected from the merged population based on the non-dominated level sorting results and crowding distances to form a new generation of parent population. In step A5, the iteration termination condition can be reaching a preset maximum number of iterations or the change in the optimal individual set being less than a preset convergence threshold. In step A6, the optimal individual is selected from the set of optimal individuals, with the individual having the lowest satellite cost and the shortest combined vector length being the preferred optimal individual, and the observation area corresponding to the optimal individual is designated as the target observation area.

[0039] In one specific implementation, when dividing non-dominated levels, individuals with better overall performance across multiple objectives are assigned to higher-priority levels. The crowding distance within the same level is based on the distribution of objective function values ​​of individuals within that level (calculated by the multi-objective optimization model). This measure assesses the scarcity of individuals within the same level, preventing the population from converging to local optima and thus ensuring better preservation of population diversity along the model's optimization direction.

[0040] In a preferred embodiment, in step A2, the multi-objective optimization model includes a first evaluation index, a second evaluation index, and a third evaluation index: wherein, the first evaluation index is used to represent satellite cost, the satellite cost being calculated based on the type of SAR satellite corresponding to the observation area and the total number of scheduling operations required to execute the observation task within the planning period; the second evaluation index is the average task duration of the SAR satellite corresponding to the observation area executing the observation task within the planning period; and the third evaluation index is the sum vector of the unit azimuth vectors corresponding to the observation azimuth angles of the SAR satellite corresponding to the observation area within the planning period.

[0041] Specifically, the optimization objective of each evaluation index in the multi-objective optimization model is to minimize the objective. The observation azimuth angle is the azimuth angle of the SAR satellite pointing towards the target area vector along the flight direction when it is in the observation area, i.e., the forward side-view azimuth angle.

[0042] In one specific implementation, the first evaluation index is calculated using the following formula: in, For individuals S The primary evaluation indicator; For individuals S The type of SAR satellite corresponding to the observed area; Within the planning period, individuals S The total number of scheduling operations required for the SAR satellites corresponding to the observation area to perform the observation task; The second evaluation index is calculated using the following formula: in, For individuals S The second evaluation indicator; N For individuals S The total number of SAR satellites corresponding to the observed area; The observation area corresponding to this individual is the first i The duration of time a SAR satellite performs its observation task relative to its start time, i.e., the duration from the start time of the planning cycle to the execution of the observation task, is used to reflect timeliness.

[0043] In this invention, a first evaluation metric represents the cost of graded satellites, aiming to reduce costs by minimizing the number of unique satellite types and the total number of scheduling operations. A second evaluation metric quantifies the time efficiency of the scheme by measuring the average mission duration, allowing for the selection of observation areas with shorter execution times and ensuring high execution efficiency. A third evaluation metric quantifies angular diversity by measuring the length of the sum of the azimuth unit vectors; a shorter sum vector indicates a more uniform distribution of observation directions within 360°, resulting in higher observation geometric value. Thus, by minimizing the multi-objective optimization model, a balance can be struck between satellite cost, execution efficiency, and observation geometric quality.

[0044] In one specific implementation, after obtaining the target observation area, the method further includes: for each cooperative observation area included in the target observation area, using WGS84 geodesic interpolation and spherical geometry calculations to perform Boolean intersection operations, eliminating the bias caused by two-dimensional approximation, calculating the precise projection polygon of the field of view of the participating SAR satellites in the cooperative observation area on the WGS-84 ellipsoid, and then solving for the final observation area with clear geographic coordinate boundaries that can directly guide the execution of SAR satellites; at the same time, the final observation area can also be visualized and output. It should be noted that, while obtaining the final observation area, the SAR satellites corresponding to the final observation area and their related information can also be determined.

[0045] To further verify the effectiveness of the method of this invention, a maritime monitoring scenario in the W Strait of Singapore was used for evaluation. A heterogeneous constellation containing 10 commercial SAR satellites was selected (as shown in Table 1, MIO is a medium inclination satellite orbit, and SSO is a sun-synchronous orbit), and a 48-hour planning cycle was set. The cooperative constraints were set as follows: a minimum of 3 viewing angles and a maximum time interval of 48 hours between two SAR satellites. Using the method of this invention, numerous multi-angle cooperative opportunities were discovered around the W Strait, i.e., multiple cooperative observation areas were obtained. By performing multi-objective optimization on the cooperative observation areas, the target observation area was obtained. Then, the WGS-84 model was used to solve for this target observation area, resulting in the following... Figure 5 The precisely observable area is shown. The SAR satellites corresponding to this target observation area are Haishao-1, Tianyi-41, Haishao-2, and Capella-13. The synchronous observation impact of Haishao-1, Tianyi-41, Haishao-2, and Capella-13 on the W Strait is obtained, such as... Figure 6 As shown in the figure; and Table 2 presents a comparison between the planning parameters determined based on the target observation area and the actual acquired geometric parameters, by Figure 2 It can be seen that the two exhibit a high degree of consistency, which confirms the effectiveness and reliability of the method of the present invention. Figures 7 to 9 The invention further demonstrates the differences in elevation projection of high-rise buildings and the complete characteristics of ships on the sea surface from different perspectives within the precisely observable area determined by this invention, intuitively proving the significant effect of this invention in enhancing the dimensionality of observation information.

[0046] Table 1 Table 2 It should be noted that "-" in Table 2 indicates that no data was collected. Figures 2 to 4 The lines in the diagram represent the center lines of the ground trajectory, and the intersections represent the intersections of the center lines of the ground trajectory. Figure 5 The blue lines represent the stripes of the ground swept by the satellite beam, equivalent to the satellite's field of view. The green area is the precisely observable area, and the yellow area represents the closed polygon formed by the center trajectory of the satellite beam (i.e., the center line of the ground trajectory), which is the cooperative observation area obtained in step 104, indicating the existence of the joint coverage area and the direction of satellite transit. Since the yellow area is composed of the center line of the ground trajectory, and this yellow area only indicates the existence of cooperative observation opportunities, the green area and the yellow area only intersect and do not overlap. Figure 6 The area where the actual collected data is obtained is Figure 5The subset of the precisely observable region in the data, i.e., the region actually observed, is selected from the precisely observable region. Figure 5 The shape of the region and Figure 6 The shapes of the regions are not the same. Figure 6 Look1, Look2, and Look3 in Table 2 correspond to the viewing angles of Haishao-1, Tianyi-41, and Haishao-2, respectively, with azimuth angles of 311.42°, 79.73°, and 258.08°.

[0047] As shown in Table 2, the effectiveness of this method can be demonstrated by comparing the simulation and actual data of three SAR satellites: Haishao-1, Tianyi-41, and Haishao-2. The three-view data obtained based on the actual data also has multi-view application value.

[0048] Compared with the prior art, the advantages of this invention are: (1) By discretizing the continuous search and introducing multi-stage screening, the present invention achieves efficient decision-making from coarse to fine, successfully solves the problem of nondeterministic polynomial search in multi-satellite collaborative planning, and greatly improves planning efficiency.

[0049] (2) The present invention utilizes a multi-objective optimization model to automatically balance satellite scheduling costs and the azimuth diversity of observation geometry, thereby optimizing resource utilization efficiency and observation quality, maximizing the development of on-orbit resource utilization, and ensuring that the generated scheme can maximize the information acquisition dimension while taking into account economy and timeliness.

[0050] (3) By integrating the complete link from orbit prediction to accurate geographic boundary calculation, this invention can directly generate observation commands that can be executed by satellite ground stations. It has been proven to have good robustness under different heterogeneous constellation combinations and has engineering practicality and generalization ability.

[0051] In summary, this invention establishes an end-to-end task planning process, introduces a multi-objective performance optimization model and a calculation verification decoupling strategy, and obtains an accurate observable area by solving the target observation area. In the collaborative observation planning of heterogeneous SAR constellations, it achieves a comprehensive improvement in scheme generation efficiency, angle diversity, and task completeness, providing an effective technical solution for intelligent global satellite resource interpretation and scheduling.

[0052] like Figure 10 , Figure 11 As shown, this embodiment of the invention provides a heterogeneous SAR constellation collaborative multi-angle observation mission planning device. The device embodiment can be implemented through software, hardware, or a combination of both. From a hardware perspective, as... Figure 10The diagram shown is a hardware architecture diagram of a computing device for a heterogeneous SAR constellation collaborative multi-angle observation mission planning device provided in an embodiment of the present invention. Besides... Figure 10 In addition to the processor, memory, network interface, and non-volatile memory shown, the computing device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 11 As shown, a device in a logical sense is formed by the CPU of its computing device reading the corresponding computer program from the non-volatile memory into memory and running it. This embodiment provides a heterogeneous SAR constellation collaborative multi-angle observation mission planning device, comprising: The acquisition module 1100 is used to acquire the ground trajectory centerline of each SAR satellite in the heterogeneous SAR constellation within the planning period and determine the intersection of the ground trajectory centerlines; The opportunity identification module 1102 is used to filter intersection points to obtain candidate cooperative points that meet geometric constraints; and to generate a local connectivity graph based on the candidate cooperative points, and to perform a heuristic search on the local connectivity graph according to preset rules to obtain the cooperative observation area. The multi-objective optimization module 1104 is used to perform multi-objective optimization on the collaborative observation area based on the collaborative constraint requirements and the task to be observed, so as to obtain the target observation area.

[0053] In some specific implementations, the acquisition module 1100 can be used to perform the above step 100, the opportunity recognition module 1102 can be used to perform the above steps 102 and 104, and the multi-objective optimization module 1104 can be used to perform the above step 106.

[0054] Since the contents of the above-described apparatus are based on the same concept as the method embodiments of the present invention, the specific contents can be found in the descriptions in the method embodiments of the present invention, and will not be repeated here.

[0055] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a heterogeneous SAR constellation collaborative multi-angle observation mission planning device. In other embodiments of the present invention, a heterogeneous SAR constellation collaborative multi-angle observation mission planning device may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0056] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0057] This invention also provides a computing device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a heterogeneous SAR constellation collaborative multi-angle observation mission planning method according to any embodiment of this invention.

[0058] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform a heterogeneous SAR constellation collaborative multi-angle observation mission planning method according to any embodiment of this invention.

[0059] Embodiments of this application also provide a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium and executes the computer program, causing the computer device to perform any of the heterogeneous SAR constellation collaborative multi-angle observation mission planning methods described in the above embodiments.

[0060] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0061] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0062] Storage media embodiments for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0063] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, system, or device.

[0064] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including, but not limited to, wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0065] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0066] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0067] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0068] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0069] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for planning multi-angle observation missions in a heterogeneous SAR constellation, characterized in that, include: Obtain the ground trajectory centerline of each SAR satellite in the heterogeneous SAR constellation within the planning period, and determine the intersection of the ground trajectory centerlines; The intersection points are filtered to obtain candidate cooperative points that satisfy the geometric constraints; A local connectivity graph is generated based on the candidate cooperative points, and a heuristic search is performed on the local connectivity graph according to preset rules to obtain the cooperative observation area; Based on the collaborative constraint requirements and the task to be observed, the collaborative observation area is optimized in multiple objectives to obtain the target observation area.

2. The method according to claim 1, characterized in that, The step of filtering the intersection points to obtain candidate cooperative points that satisfy the geometric constraints includes: On the center line of the ground trajectory, establish an edge whose length is no greater than the preset neighborhood radius, obtained by connecting any two intersection points; For each intersection point, find the first intersection point adjacent to the intersection point on the intersecting trajectory of the center line of the ground trajectory; and determine the first intersection point that forms the edge by connecting with the intersection point as the neighborhood intersection point; Based on the neighborhood intersection and the intersection, an interior angle is formed with the intersection as the center point; Determine whether the interior angle is within the preset angle range; preferably, the preset angle range is the supplementary angle that is greater than the maximum azimuth angle interval threshold and less than π; If the judgment result is yes, then the neighborhood intersection point that constitutes the interior angle and the intersection point are determined as candidate cooperative points; Preferably, the preset neighborhood radius is determined by the following formula: in, The preset neighborhood radius; For the first i The strip width of each SAR satellite; This is the maximum azimuth interval threshold.

3. The method according to claim 1, characterized in that, The candidate co-location points include information about the central intersection, interior angles, information about neighboring intersections, and the turning direction constituting the interior angles; the information about the central intersection includes a unique identifier, geographic coordinates, the IDs of the two trajectories that form the intersection, SAR satellite information, and the time when each trajectory passes through the central intersection. The interior angle is formed by connecting the first neighborhood intersection point, the center intersection point, and the second neighborhood intersection point in sequence; The process of generating a local connectivity graph based on the candidate cooperative points, and performing a heuristic search on the local connectivity graph according to preset rules to obtain the cooperative observation region includes: For each candidate cooperative point, the following steps are performed: each intersection point in the candidate cooperative point is treated as a node, and at each node, the next neighboring intersection point is selected in the same direction as the turning direction. The current interior angle of the intersection point centered on the node is obtained. When the current interior angle is within the preset angle range, the next neighboring intersection point is determined to be a valid intersection point. The node is sequentially connected to the valid node to complete one trajectory growth. The next trajectory growth is performed with the valid intersection point as a node until the search is completed and the nodes are sequentially connected to the first neighboring intersection point to obtain a polygonal region. Obtain the participating SAR satellites and satellite beam pointing directions corresponding to the ground trajectory centerline that forms the polygonal region; The polygonal region was observed from each of the participating SAR satellites to obtain verification results; When the verification result indicates that pointing observation is achievable, determine whether there is a target point in the polygonal region that satisfies a preset constraint. If the judgment result is yes, then the polygonal region is determined to be a collaborative observation region.

4. The method according to claim 3, characterized in that, The preset constraint is expressed by the following formula: in, , , All are the first i Parameters of the ground trajectory centerline equation for a SAR satellite For the first i Half-width of a stripe for a SAR satellite; For the target point in the polygonal region Q Its geographical location.

5. The method according to any one of claims 1 to 4, characterized in that, The multi-objective optimization of the collaborative observation area based on collaborative constraint requirements and the task to be observed yields the target observation area, including: A1: Initialize the population based on the cooperative observation region; wherein, each individual in the population corresponds to an observation region including at least one cooperative observation region, and the observation region satisfies the cooperative constraint requirement; A2: Construct a multi-objective optimization model based on the task to be observed and the SAR satellites corresponding to the observation area; A3: Based on the multi-objective optimization model, perform selection, crossover, and mutation operations on the initial population to generate a progeny population; A4: Filter the initial population and offspring population to obtain a new generation of parent population; A5: Use the new generation of parent population as the initial population for the next iteration, repeat steps A3 to A4 until the iteration termination condition is met, and output the optimal set of individuals; A6: Select the best individual from the set of best individuals, and determine the observation area corresponding to the best individual as the target observation area.

6. The method according to claim 5, characterized in that, The multi-objective optimization model includes a first evaluation index, a second evaluation index, and a third evaluation index: wherein, the first evaluation index is used to represent satellite cost, which is calculated based on the type of SAR satellite corresponding to the observation area and the total number of scheduling operations required to execute the observation task within the planning period; the second evaluation index is the average task duration of the SAR satellite corresponding to the observation area in executing the observation task within the planning period; and the third evaluation index is the sum of the unit azimuth vectors corresponding to the observation azimuth angles of the SAR satellite corresponding to the observation area within the planning period.

7. A heterogeneous SAR constellation collaborative multi-angle observation mission planning device, characterized in that, include: The acquisition module is used to acquire the ground trajectory centerline of each SAR satellite in the heterogeneous SAR constellation within the planning period, and to determine the intersection of the ground trajectory centerlines; An opportunity identification module is used to filter the intersection points to obtain candidate cooperative points that meet the geometric constraints; and to generate a local connectivity graph based on the candidate cooperative points, and to perform a heuristic search on the local connectivity graph according to preset rules to obtain the cooperative observation area; The multi-objective optimization module is used to perform multi-objective optimization on the collaborative observation area based on collaborative constraint requirements and the task to be observed, so as to obtain the target observation area.

8. A computing device comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the method of any one of claims 1-6.

10. A computer program product, characterized in that, Includes computer instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1-6.

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