A method and device for determining a multi-angle observation opportunity of a heterogeneous SAR based on graph theory

Abstract

This invention provides a graph-theory-based method and apparatus for determining multi-angle observation opportunities in heterogeneous SAR, comprising: calculating the intersection points of the ground trajectory centerlines of several SAR satellites; establishing an edge on the ground trajectory centerlines connected by any two intersection points based on geometric constraints; for each intersection point, determining the neighboring intersection points based on the edge, obtaining interior angles based on the intersection point and its neighboring intersection points, and determining candidate cooperative points based on the interior angles; generating a local connectivity graph based on the candidate cooperative points, performing a heuristic search on the local connectivity graph according to preset rules to obtain polygonal regions; and verifying and filtering the polygonal regions to obtain cooperative observation areas. This scheme can proactively and completely identify geometrically feasible cooperative observation opportunities from a massive number of trajectory intersection points, providing a systematic solution for the efficient utilization of global on-orbit SAR resources.

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 determining heterogeneous SAR multi-angle observation opportunities based on graph theory. Background Technology

[0002] Synthetic Aperture Radar (SAR), with its all-weather, all-day imaging capabilities, has become an indispensable means of Earth observation. Currently, spaceborne SAR images have reached high resolutions such as 1.0 meter and 0.5 meter. However, they are still constrained by the inherent limitations of the SAR imaging mechanism. SAR images acquired from a single observation suffer from geometric distortions such as overlay and shadows, and the target scattering characteristics exhibit strong anisotropy, making it difficult to fully reveal ground features from a single perspective. Utilizing multi-angle observations and fusing information from different perspectives is an important technical approach to improve interpretation accuracy (such as in target detection and recognition, 3D reconstruction, and other applications).

[0003] With the large-scale deployment and commercial application of global SAR constellations (such as ICEYE and Capella), it has become possible to acquire multi-view images of the same target within a short time baseline by combining multiple platforms. However, planning collaborative tasks from a vast amount of heterogeneous resources is a computationally extremely challenging nondeterministic polynomial problem. Traditional task planning methods mostly follow a demand-driven passive scheduling model, that is, exhaustive intersection verification for specific target points. However, when faced with large-scale constellations and high-order collaborative requirements (i.e., N views), the search space grows exponentially. Due to the lack of an effective geometric pre-screening mechanism, most of the computational resources are wasted on complex operations on geometrically disconnected or invalid combinations, resulting in low recognition efficiency and difficulty in achieving proactive discovery.

[0004] Therefore, in order to meet the urgent need for efficient and comprehensive mission planning in advanced multi-satellite collaborative remote sensing applications, there is a need for a graph theory-based method and device for determining heterogeneous SAR multi-angle observation opportunities. Summary of the Invention

[0005] This invention provides a graph theory-based method and apparatus for determining multi-angle observation opportunities in heterogeneous SAR, which solves the problems of excessive computational complexity and inability to actively discover observation opportunities in existing technologies when dealing with multi-angle mission planning of large-scale heterogeneous constellations.

[0006] In a first aspect, embodiments of the present invention provide a method for determining multi-angle observation opportunities in heterogeneous SAR based on graph theory, including: Obtain the intersection points of the ground trajectory centerlines of several SAR satellites; Based on geometric constraints, an edge is established on the center line of the ground trajectory by connecting any two intersection points; For each intersection point, the neighboring intersection points of the intersection point are determined based on the edge, and the interior angle is obtained based on the intersection point and the neighboring intersection points, and the candidate cooperative points are determined through the interior angle; 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 polygonal regions; The polygonal region is verified and filtered to obtain the collaborative observation area.

[0007] Secondly, embodiments of the present invention also provide a graph theory-based device for determining multi-angle observation opportunities in heterogeneous SAR, comprising: Obtain the intersection points of the ground trajectory centerlines of several SAR satellites; Based on geometric constraints, an edge is established on the center line of the ground trajectory by connecting any two intersection points; For each intersection point, the neighboring intersection points of the intersection point are determined based on the edge, and the interior angle is obtained based on the intersection point and the neighboring intersection points, and the candidate cooperative points are determined through the interior angle; 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 polygonal regions; The polygonal region is verified and filtered to obtain the collaborative 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 graph theory-based method for determining heterogeneous SAR multi-angle observation opportunities as described above.

[0009] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the graph theory-based method for determining heterogeneous SAR multi-angle observation opportunities as described above.

[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 graph theory-based method and apparatus for determining multi-angle observation opportunities in heterogeneous SAR. By establishing a theoretical model that transforms dynamic multi-satellite coverage into static ground geometric constraints, a graph theory-based algorithm for discovering potential collaborative observation areas is designed. This transforms the search for an effective multi-angle observation opportunity into the search for a convex polygon formed by the center lines of satellite ground trajectories that satisfies specific geometric constraints. The dynamic three-dimensional spatial coverage problem is transformed into a static two-dimensional ground geometric problem. This enables the proactive and complete identification of geometrically feasible collaborative observation opportunities from a massive number of trajectory intersections, providing a systematic solution for the efficient utilization of global on-orbit SAR resources. 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 graph theory-based method for determining multi-angle observation opportunities in heterogeneous SAR, 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 a comparison chart of the operational efficiency of the heterogeneous SAR multi-angle observation opportunity determination method provided by the present invention and the traditional method under different event scales. Figure 6 This is a hardware architecture diagram of a computing device provided in an embodiment of the present invention; Figure 7 This is a structural diagram of a graph theory-based heterogeneous SAR multi-angle observation opportunity determination 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 graph theory-based method for determining multi-angle observation opportunities in heterogeneous SAR, the method comprising: Step 100: Obtain the intersection points of the ground trajectory centerlines of several SAR satellites; Step 102: Based on geometric constraints, establish an edge on the center line of the ground trajectory that is connected by any two intersection points; Step 104: For each intersection point, determine the neighboring intersection points of the intersection point based on the edges, obtain the interior angles based on the intersection point and the neighboring intersection points, and determine the candidate cooperative points through the interior angles; Step 106: 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 polygonal regions; Step 108: Verify and filter the polygonal regions to obtain the collaborative observation area.

[0016] In this embodiment of the invention, the intersection points of the ground trajectory centerlines of several SAR satellites are obtained. Then, a connection graph structure is established, consisting of edges on the ground trajectory centerlines connecting any two intersection points that satisfy geometric constraints. Based on these edges, neighboring intersection points that can form interior angles are found for each intersection point. The intersection points corresponding to interior angles that meet preset requirements are identified as candidate cooperative points. A local connection graph is generated based on these candidate cooperative points, and a heuristic search is performed according to preset rules to connect candidate cooperative points on effective paths to obtain closed polygonal regions. By verifying and filtering these polygonal regions, cooperative observation areas with cooperative observation opportunities are finally determined. Thus, finding an effective multi-angle observation opportunity is transformed into finding a convex polygon formed by the satellite ground trajectory centerlines that satisfies specific geometric constraints. The dynamic three-dimensional spatial coverage problem is transformed into a static two-dimensional ground geometry problem. This enables the proactive and complete identification of geometrically feasible cooperative observation opportunities from a massive number of trajectory intersection points, providing a systematic solution for the efficient utilization of global on-orbit SAR resources.

[0017] It should be noted that the number of candidate cooperative points is less than the number of intersection points. The number of cooperative observation regions is less than the number of polygonal regions. The polygonal regions are preferably convex polygonal regions.

[0018] The following description Figure 1 The execution method for each step is shown.

[0019] 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.

[0020] For step 102, the geometric constraint condition includes that the side length of the edge is not greater than the 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, thus obtaining 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 significantly shortens the time for discovering observation opportunities.

[0023] In step 104, for each intersection point, neighboring intersection points are determined based on the edges to obtain an interior angle based on the intersection point and its neighboring intersection points, and candidate cooperative points are determined using the interior angles, including: Find the first intersection point adjacent to the intersection point on the intersecting trajectory of the center line of the ground trajectory; The first intersection point that forms the edge by connecting it to the intersection point is determined 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 greater than the supplementary angle of 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.

[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 intersection point, interior angles, information about neighboring intersection points, and the turning direction constituting the interior angles; the information about the intersection point includes a unique identifier, geographic coordinates, IDs of the two trajectories forming the intersection point, SAR satellite information, and the time each trajectory passes through the intersection point; The interior angle is formed by connecting the first neighboring intersection point, the second neighboring intersection point, and so on.

[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 V center V next The interior angles can be obtained. θ It was determined that the interior angle satisfies... θ ∈( θmin ,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 106, 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 polygonal regions, including: For each of the aforementioned candidate collaboration points, the following is executed: 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.

[0028] It should be noted that the preset angle range is ( θ min,π). And the edges connected in step 106 all satisfy the geometric constraints, that is, trajectory growth is performed according to the edges and preset rules.

[0029] 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.

[0030] Specifically, such as Figures 3 to 4 As 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.

[0031] In step 108, the polygonal region is verified and filtered to obtain a cooperative observation region, including: 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.

[0032] Specifically, pointing observations are performed on the polygonal region on each of the participating SAR satellites to obtain verification results, including: 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 collaborative 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 observation can be achieved; if the determination result is no, the verification result is that pointing observation 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] To further verify the effectiveness of the method of this invention, a scenario with 10 heterogeneous SAR satellites is used as an example. The names and related information of the 10 heterogeneous SAR satellites are shown in Table 1, where MIO is a medium inclination satellite orbit and SSO is a sun-synchronous orbit. In a simulated scenario including the above 10 heterogeneous satellites and a 48-hour period, the method of this invention identified 134,362 cooperative observation areas. Actual measurement data shows that when the number of transit events M=43, the traditional combined exhaustive method takes more than 780 seconds, while the method of this invention takes less than 6 seconds. Figure 5The comparative experiment shown indicates that as the number of transit events M increases, the running time of the traditional combined exhaustive search method exhibits a high-order power-law increase of b≈6.28, which is consistent with... O ( M 6 The order of magnitude is 1.5. In contrast, the method of this invention uses graph topology search to lock the degrees of freedom, reducing the fitting slope to b ≈ 4.45, which is consistent with the desired result. O ( M 4 The efficiency improvement is on the order of magnitude, reducing the time from 780 seconds to less than 6 seconds when M=43, achieving a two-order-of-magnitude efficiency improvement. This demonstrates the excellent engineering practicality of this invention in handling large-scale constellation long-cycle collaborative mission planning. It should be noted that... Figure 5 The x-coordinate in the middle is The vertical axis is M represents the number of transit events; s represents the time taken.

[0037] Table 1 Compared with the prior art, the advantages of this invention are: (1) In this invention, by employing a computation-verification decoupling strategy, the asymptotic time complexity of the search process is reduced from that of the traditional exhaustive search method. O ( M 6 Reduced to approximately O ( M 4 On the order of magnitude ), in large-scale constellations (such as the number of orbits per orbit) M >200) reduces processing time from hundreds of seconds to seconds, resulting in an order-of-magnitude leap in computational efficiency; (2) This invention can proactively reveal potential high-value collaborative observation areas around the world from a resource-centric perspective, and has the ability to proactively discover opportunities, which greatly enhances the certainty and proactivity of task discovery; (3) The preset neighborhood radius based on graph theory ensures that no geometrically feasible cooperative combination is missed in the massive search space, and the final verification stage ensures that the output results conform to the physical orientation and coverage constraints, ensuring that the search results of the cooperative observation area have completeness and accuracy.

[0038] like Figure 6 , Figure 7 As shown, this embodiment of the invention provides a graph theory-based device for determining multi-angle observation opportunities in heterogeneous SAR. The device embodiment can be implemented in software, hardware, or a combination of both. From a hardware perspective, as... Figure 6The diagram shown is a hardware architecture diagram of a computing device for determining heterogeneous SAR multi-angle observation opportunities based on graph theory, provided in an embodiment of the present invention. Besides... Figure 6 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 7 As shown, a device in a logical sense is formed by the CPU of its computing device reading the corresponding computer program from non-volatile memory into memory and running it. This embodiment provides a graph theory-based heterogeneous SAR multi-angle observation opportunity determination device, comprising: The acquisition module 700 is used to acquire the intersection points of the ground trajectory centerlines of several SAR satellites; Graph construction module 702 is used to establish an edge on the center line of the ground trajectory based on geometric constraints, which is obtained by connecting any two intersection points. The seed identification module 704 is used to determine the neighboring intersection points of each intersection point based on the edge, to obtain the interior angle based on the intersection point and the neighboring intersection points, and to determine the candidate cooperative point through the interior angle; The path search module 706 is used to generate a local connectivity graph based on the candidate cooperating points, and perform a heuristic search on the local connectivity graph according to preset rules to obtain a polygonal region. The refinement verification module 708 is used to verify and filter the polygonal region to obtain the collaborative observation area.

[0039] In some specific implementations, the acquisition module 700 can be used to perform the above step 100, the graph construction module 702 can be used to perform the above step 102, the seed identification module 704 can be used to perform the above step 104, the path search module 706 can be used to perform the above step 106, and the refinement verification module 708 can be used to perform the above step 108.

[0040] 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.

[0041] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a graph theory-based heterogeneous SAR multi-angle observation opportunity determination device. In other embodiments of the present invention, a graph theory-based heterogeneous SAR multi-angle observation opportunity determination device may include more or fewer components than illustrated, or combine some components, split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0042] 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.

[0043] 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 graph theory-based method for determining heterogeneous SAR multi-angle observation opportunities according to any embodiment of this invention.

[0044] 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 graph theory-based method for determining multi-angle observation opportunities of heterogeneous SAR according to any embodiment of this invention.

[0045] 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 a graph theory-based method for determining heterogeneous SAR multi-angle observation opportunities as described in any of the above embodiments.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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).

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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 graph-theory-based method for determining multi-angle observation opportunities in heterogeneous SAR, characterized in that, include: Obtain the intersection points of the ground trajectory centerlines of several SAR satellites; Based on geometric constraints, an edge is established on the center line of the ground trajectory by connecting any two intersection points; For each intersection point, the neighboring intersection points of the intersection point are determined based on the edge, and the interior angle is obtained based on the intersection point and the neighboring intersection points, and the candidate cooperative points are determined through the interior angle; 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 polygonal regions; The polygonal region is verified and filtered to obtain the collaborative observation area.

2. The method according to claim 1, characterized in that, The geometric constraint condition includes that the side length of the edge is not greater than the 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.

3. The method according to claim 1, characterized in that, For each intersection point, the neighboring intersection points of that intersection point are determined based on the edge, and an interior angle is obtained based on the intersection point and the neighboring intersection points. Candidate cooperative points are then determined using the interior angles, including: Find the first intersection point adjacent to the intersection point on the intersecting trajectory of the center line of the ground trajectory; The first intersection point that forms the edge by connecting it to the intersection point is determined 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 greater than the supplementary angle of 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.

4. The method according to claim 1, characterized in that, The candidate cooperating point includes the information of the intersection point, the interior angle, the information of the neighboring intersection points, and the turning direction constituting the interior angle; the information of the intersection point includes a unique identifier, geographic coordinates, the IDs of the two trajectories forming the intersection point, SAR satellite information, and the time when each trajectory passes through the intersection point; The interior angle is formed by connecting the first neighborhood intersection point, the intersection point and the second neighborhood intersection point in sequence; The step of generating a local connectivity graph based on the candidate co-location points and performing a heuristic search on the local connectivity graph according to preset rules to obtain a polygonal region includes: For each of the aforementioned candidate collaboration points, the following is executed: 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.

5. The method according to any one of claims 1 to 4, characterized in that, The process of verifying and filtering the polygonal region to obtain the collaborative observation area includes: 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.

6. The method according to claim 5, 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.

7. A graph-theory-based device for determining the multi-angle observation opportunities of heterogeneous SAR, characterized in that, include: The acquisition module is used to acquire the intersection points of the ground trajectory centerlines of several SAR satellites; The graph construction module is used to establish an edge on the center line of the ground trajectory, which is formed by connecting any two intersection points, based on geometric constraints. The seed identification module is used to determine the neighboring intersection points of each intersection point based on the edge, to obtain the interior angle based on the intersection point and the neighboring intersection points, and to determine the candidate cooperative points through the interior angle; The path search module is used to generate a local connectivity graph based on the candidate cooperating points, and perform a heuristic search on the local connectivity graph according to preset rules to obtain a polygonal region. The refinement verification module is used to verify and filter the polygonal region to obtain the collaborative 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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