Target aggregation method considering earth observation load imaging mode difference

Abstract

The invention relates to the technical field of remote sensing satellites, in particular to a target aggregation method considering earth observation load imaging mode difference, and the method comprises the following steps: S1, judging whether the observation of a plurality of point targets has an aggregation observation condition or not; s2, providing initialized task input for a resource scheduling system under the condition of meeting an aggregation observation condition, and generating an aggregation scheme through a pre-aggregation algorithm; and S3, dynamically adjusting the aggregation scheme through an in-event aggregation algorithm according to the actual task satisfaction condition in the resource scheduling process. According to the method, on the basis of conventional pre-static aggregation, aggregation tasks are tried to be integrated into a resource scheduling process, the aggregation tasks are regenerated according to task distribution conditions which are not met in an original scheduling scheme and a currently generated plan scheme, and the aggregation tasks are processed by a scheduling system after evaluation, so that in-process dynamic aggregation is realized; therefore, sufficiency and effectiveness of task aggregation operation are improved.

Description

Technical Field

[0001] The invention relates to the technical field of remote sensing satellites, and in particular to a target aggregation method that takes into account the differences in imaging modes of earth observation payloads. Background Art

[0002] In current remote sensing satellite applications, the imaging area is usually regarded as a single target and is observed one by one, which has the disadvantages of low satellite resource utilization efficiency and low mission satisfaction rate. According to the satellite payload imaging mechanism, if the observation angles of multiple point targets are within a certain range and their imaging times have a sufficient length of intersection (or meet the conditions for splicing), aggregate observation can be considered, such as the attached Figure 1 As shown. Domestic and foreign scholars are conducting research on efficient observation methods for dense point targets. Cohen analyzed the possibility of a single strip covering multiple point targets at the same time. Wang Jun et al. considered the problem of time window overlap when observing adjacent point targets in aggregate. Xu Yula et al. analyzed the problem of task merging observation under static conditions. However, these studies are limited to fixed observation angles and do not consider the impact of satellite payloads on the possibility of target aggregate observation in different imaging modes. In response to the above shortcomings, this technology improves the aggregation constraint model and designs an optimization algorithm for the two aggregation stages. Summary of the invention

[0003] The present invention provides a target aggregation method that takes into account the differences in imaging modes of earth observation payloads to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: A target aggregation method considering the differences in imaging modes of earth observation payloads includes the following steps: S1: Determine whether multiple point target observations meet the conditions for aggregate observation; S2: Provide the resource scheduling system with the initial task input when the aggregation observation conditions are met, and generate an aggregation plan through the prior aggregation algorithm; S3: According to the actual task satisfaction in the resource scheduling process, the aggregation plan is dynamically adjusted through the in-process aggregation algorithm.

[0005] Preferably, step S1 specifically includes: Earth observation satellite Point target imaging requirements No. t The visible window is , and are the start and end times of the window, respectively. is the load side swing angle corresponding to the visible window; In the observation mode of fixed strip imaging, for any two fixed strip imaging tasks and The aggregation observation conditions are: (1); in, for right The visible window collection of for Minimum working time required for a single imaging session; for The instantaneous field of view of the load.

[0006] Preferably, when the fixed strip imaging task exist Shanghe exist When the aggregation observation conditions are met, the aggregation task is recorded as , whose characteristic parameters include the visible window and roll angle range ; in, The calculation method is: (2); The calculation method is: (3).

[0007] Preferably, step S1 specifically further includes: In the push-scan strip imaging observation mode, for any two push-scan strip imaging tasks and The aggregation observation conditions are: (4); in, for Maximum continuous shooting time for a single imaging.

[0008] Preferably, for push-scan strip imaging tasks and , the corresponding aggregation task The characteristic parameters include the side swing angle range and the minimum tracking arc ; in, The calculation method is: (5); The calculation method of is the same as formula (3).

[0009] Preferably, the step S2 of generating an aggregation scheme by using a pre-aggregation algorithm specifically includes: Step 21: Sort the task requirements according to the principle of giving priority to densely populated areas; Step 22: From the Task Sequence Take out the tasks one by one , aggregated with subsequent tasks in the sequence; Step 23: Merge or remove duplicates of aggregated tasks that have the same individual task composition or have a containment relationship in the task set; Step 24: Obtain the simplified aggregated task set through the above steps, and select the aggregated tasks according to the strategy of minimum tasks and maximum coverage. The constraints of this strategy are as follows: (6); in, Used for marking Subtask set pair If ,make Otherwise, let .

[0010] Preferably, step S21 specifically includes: Step 211: Use the K-mean algorithm to perform clustering based on task distribution and obtain several cluster center points ; Step 212: For ,calculate and The distance between each cluster center point in is set to ,make As Distance from the cluster center; Step 213: Sort by distance from the cluster center and regenerate the task sequence as input for the aggregation operation.

[0011] Preferably, step S22 specifically includes: Step 221: If For a fixed strip imaging task, and satisfying equation (1), the aggregated visible window and side swing angle interval are calculated according to equations (2) and (3); Step 222: If For push-scan strip imaging tasks, and satisfying equation (4), the minimum tracking arc segment and side swing angle interval after aggregation are calculated according to equations (5) and (3); Step 223: If it can be aggregated into a new task , then Alternative , continue to participate in aggregation until aggregation is no longer possible.

[0012] Preferably, the dynamically adjusting the aggregation scheme by the in-progress aggregation algorithm described in step S3 specifically includes: Step 31: Classify all tasks and set the set of tasks that have not been met or newly added as , the set of tasks that have been satisfied and are to be executed is ; Step 32: For ,calculate Zhongke The number of tasks to aggregate ;for ,calculate Zhongke The number of tasks to aggregate ; Step 33: and The tasks are divided into and Reorder in ascending order; Step 34: Take out the tasks in turn, and Aggregate the tasks in Step 35: As a separate task to the resource scheduling system, if it still cannot be satisfied, then Add to the collection of unavailable tasks ; Step 36: from Remove if conditions are met , dynamic adjustment ends; otherwise, go to step 32.

[0013] Preferably, step S34 specifically includes: Step 341: For ,like If the aggregation condition is met, proceed to step 342; otherwise, proceed to step 35; Step 342: Perform constraint check on the task aggregation result. If the satellite resource allocation constraint is met, generate an aggregation task and update the resource usage plan; otherwise, go to step 35.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention takes into account the differences in imaging modes of earth observation payloads, sorts out the basic conditions that need to be met for the fixed strip imaging mode and the push-broom strip imaging mode, and then designs different aggregation constraint condition models, so as to reduce the influence of satellite payloads on the possibility of target aggregation observation under different imaging modes; and, on the basis of conventional ex ante static aggregation, the present invention attempts to integrate aggregation tasks into the resource scheduling process, regenerates aggregation tasks according to the distribution of unmet tasks in the original scheduling plan and the currently generated plan, and hands them over to the scheduling system for processing after evaluation, that is, to realize dynamic aggregation in the process, so as to improve the adequacy and effectiveness of task aggregation operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of the effect of the observation satellite aggregating observations for multiple point target observation tasks; Figure 2 A flowchart of a pre-aggregation algorithm and an in-process aggregation algorithm in a target aggregation method that takes into account the differences in imaging modes of earth observation payloads provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] An embodiment of the present invention provides a target aggregation method that takes into account the differences in imaging modes of earth observation payloads, including the following steps: S1: Determine whether multiple point target observations meet the conditions for aggregate observation; S2: Provide the resource scheduling system with the initial task input when the aggregation observation conditions are met, and generate an aggregation plan through the prior aggregation algorithm; S3: According to the actual task satisfaction in the resource scheduling process, the aggregation plan is dynamically adjusted through the in-process aggregation algorithm.

[0018] According to the imaging mechanism of satellite payloads, if the observation angles of multiple point targets are within a certain range and their imaging times have an intersection of sufficient length (or have the conditions for splicing), aggregate observation can be considered. The existing satellite observation imaging modes include two observation modes: fixed strip imaging (mainly considering optical array mode, SAR beamforming mode, and SAR strip mode) and push-broom strip imaging (mainly considering optical linear array mode and SAR scanning mode). The impact of satellite payloads on the possibility of target aggregate observation is different in different imaging modes. Therefore, the present invention sorts out the basic conditions that need to be met for the fixed strip imaging mode and the push-broom strip imaging mode, and then designs different aggregation constraint condition models.

[0019] In one embodiment of the present invention, step S1 specifically includes: Earth observation satellite Point target imaging requirements No. t The visible window is , and are the start and end times of the window, respectively. is the load side swing angle corresponding to the visible window; In the observation mode of fixed strip imaging, for any two fixed strip imaging tasks and The aggregation observation conditions are: (1); in, for right The visible window collection of for Minimum working time required for a single imaging session; for The instantaneous field of view of the load.

[0020] Furthermore, when the fixed strip imaging task exist Shanghe exist When the aggregation observation conditions are met, the aggregation task is recorded as , whose characteristic parameters include the visible window and roll angle range ; in, The calculation method is: (2); The calculation method is: (3).

[0021] And, step S1 specifically also includes: In the push-scan strip imaging observation mode, for any two push-scan strip imaging tasks and The aggregation observation conditions are: (4); in, for Maximum continuous shooting time for a single imaging session.

[0022] Furthermore, for push-scan strip imaging tasks and , the corresponding aggregation task The characteristic parameters include the side swing angle range and the minimum tracking arc ; in, The calculation method is: (5); The calculation method of is the same as formula (3).

[0023] For the problem of task aggregation, a common solution is to perform pre-integration, that is, before starting resource scheduling, first merge the point targets according to certain rules to form several point target sets (aggregated tasks), and each set as a whole can participate in satellite resource allocation as a separate task. Pre-integration does not need to consider the actual use of satellite resources. It belongs to pre-static aggregation. The implementation method is relatively simple and suitable for rapid analysis of task distribution status and preliminary formulation of aggregation schemes. On this basis, the present invention attempts to integrate task integration into the resource scheduling process, that is, to achieve dynamic aggregation during the process, so as to improve the adequacy and effectiveness of task aggregation operations.

[0024] Figure 2 The flowchart of the pre-aggregation algorithm and the in-process aggregation algorithm in a target aggregation method considering the differences in imaging modes of earth observation payloads provided by an embodiment of the present invention is shown. Figure 2 As shown, the overall aggregation process formed by the aggregation scheme of the present invention is as follows: after the demand is clear, first generate the aggregation task according to the basic constraints (the subtasks can overlap). According to certain rules, evaluate the overall importance or priority of the aggregation task. Submit the aggregation task to the satellite resource scheduling system to participate in centralized selection and resource allocation. According to the original task situation that is not met and the currently generated plan, regenerate the aggregation task and hand it over to the resource scheduling system for processing after evaluation. Repeat the above process until the plan output conditions are met.

[0025] In one embodiment of the present invention, the generation of the aggregation scheme by the pre-aggregation algorithm in step S2 specifically includes: Step 21: Sort the task requirements according to the principle of giving priority to densely populated areas; Step 22: From the Task Sequence Take out the tasks one by one , aggregated with subsequent tasks in the sequence; Step 23: Merge or remove duplicates of aggregated tasks that have the same individual task composition or have a containment relationship in the task set; Step 24: Obtain the simplified aggregated task set through the above steps, and select the aggregated tasks according to the strategy of minimum tasks and maximum coverage. The constraints of this strategy are as follows: (6); in, Used for marking Subtask set pair Including situations, if ,make Otherwise, let .

[0026] Furthermore, step S21 specifically includes: Step 211: Use the K-mean algorithm to perform clustering based on task distribution and obtain several cluster center points ; Step 212: For ,calculate and The distance between each cluster center point in is set to ,make As Distance from the cluster center; Step 213: Sort by distance from the cluster center and regenerate the task sequence as input for the aggregation operation.

[0027] Step S22 specifically includes: Step 221: If For a fixed strip imaging task, and satisfying equation (1), the aggregated visible window and side swing angle interval are calculated according to equations (2) and (3); Step 222: If For push-scan strip imaging tasks, and satisfying equation (4), the minimum tracking arc segment and side swing angle interval after aggregation are calculated according to equations (5) and (3); Step 223: If it can be aggregated into a new task , then Alternative , continue to participate in aggregation until aggregation is no longer possible.

[0028] The pre-aggregation of tasks in the present invention is mainly to provide the resource scheduling system with initialization task input. The actual allocation of resources is usually not considered during the aggregation operation, while the aggregation of tasks in progress is mainly to dynamically adjust the aggregation plan according to the actual task satisfaction in the resource scheduling process, and to assist in supporting the resource scheduling system to generate the final task execution plan. In one embodiment of the present invention, the dynamic adjustment of the aggregation plan by the in-progress aggregation algorithm described in step S3 specifically includes: Step 31: Classify all tasks and set the set of tasks that have not been met or newly added as , the set of tasks that have been satisfied and are to be executed is ; Step 32: For ,calculate Zhongke The number of tasks to aggregate ;for ,calculate Zhongke The number of tasks to aggregate ; Step 33: and The tasks are divided into and Reorder in ascending order; Step 34: Take out the tasks in turn, and Aggregate the tasks in Step 35: As a separate task to the resource scheduling system, if it still cannot be satisfied, then Add to the collection of unavailable tasks ; Step 36: from Remove if conditions are met , dynamic adjustment ends; otherwise, go to step 32.

[0029] Further, step S34 specifically includes: Step 341: For ,like If the aggregation condition is met, proceed to step 342; otherwise, proceed to step 35; Step 342: Perform constraint check on the task aggregation result. If the satellite resource allocation constraint is met, generate an aggregation task and update the resource usage plan; otherwise, go to step 35.

[0030] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A target aggregation method considering the differences in imaging modes of earth observation payloads, characterized in that: The following steps are involved: S1: Determine whether multiple point target observations meet the conditions for aggregate observation; S2: Provide the resource scheduling system with the initial task input when the aggregation observation conditions are met, and generate an aggregation plan through the prior aggregation algorithm; S3: According to the actual task satisfaction in the resource scheduling process, the aggregation plan is dynamically adjusted through the in-process aggregation algorithm.

2. The target aggregation method considering the differences in imaging modes of earth observation payloads according to claim 1 is characterized in that: Step S1 specifically includes: Earth observation satellite Point target imaging requirements No. t The visible window is , and are the start and end times of the window, respectively. is the load side swing angle corresponding to the visible window; In the observation mode of fixed strip imaging, for any two fixed strip imaging tasks and The aggregation observation conditions are: （1）； in, for right The visible window collection of for Minimum working time required for a single imaging session; for The instantaneous field of view of the load.

3. The target aggregation method considering the differences in imaging modes of earth observation payloads according to claim 2 is characterized in that: When fixed strip imaging tasks exist Shanghe exist When the aggregation observation conditions are met, the aggregation task is recorded as , whose characteristic parameters include the visible window and roll angle range ; in, The calculation method is: （2）； The calculation method is: （3）。 4. The target aggregation method considering the differences in imaging modes of earth observation payloads according to claim 3 is characterized in that: Step S1 specifically also includes: In the push-scan strip imaging observation mode, for any two push-scan strip imaging tasks and The aggregation observation conditions are: （4）； in, for Maximum continuous shooting time for a single imaging session.

5. The target aggregation method considering the differences in imaging modes of earth observation payloads according to claim 4 is characterized in that: For push-scan swath imaging tasks and , the corresponding aggregation task The characteristic parameters include the side swing angle range and the minimum tracking arc ; in, The calculation method is: （5）； The calculation method of is the same as formula (3).

6. The target aggregation method considering the differences in imaging modes of earth observation payloads according to claim 1, characterized in that: The step S2 of generating an aggregation scheme by using a pre-aggregation algorithm specifically includes: Step 21: Sort the task requirements according to the principle of giving priority to densely populated areas; Step 22: From the Task Sequence Take out the tasks one by one , aggregated with subsequent tasks in the sequence; Step 23: Merge or remove duplicates of aggregated tasks that have the same individual task composition or have a containment relationship in the task set; Step 24: Obtain the simplified aggregated task set through the above steps, and select the aggregated tasks according to the strategy of minimum tasks and maximum coverage. The constraints of this strategy are as follows: （6）； in, Used for marking Subtask set pair If ,make Otherwise, let .

7. The target aggregation method considering the differences in imaging modes of earth observation payloads according to claim 6 is characterized in that: Step S21 specifically includes: Step 211: Use the K-mean algorithm to perform clustering based on task distribution and obtain several cluster center points ; Step 212: For ,calculate and The distance between each cluster center point in is set to ,make As Distance from the cluster center; Step 213: Sort by distance from the cluster center and regenerate the task sequence as input for the aggregation operation.

8. The target aggregation method considering the differences in imaging modes of earth observation payloads according to claim 6, characterized in that: Step S22 specifically includes: Step 221: If For a fixed strip imaging task, and satisfying equation (1), the aggregated visible window and side swing angle interval are calculated according to equations (2) and (3); Step 222: If For push-scan strip imaging tasks, and satisfying equation (4), the minimum tracking arc segment and side swing angle interval after aggregation are calculated according to equations (5) and (3); Step 223: If it can be aggregated into a new task , then Alternative , continue to participate in aggregation until aggregation is no longer possible.

9. The target aggregation method considering the differences in imaging modes of earth observation payloads according to claim 1, characterized in that: The dynamic adjustment of the aggregation scheme by the in-progress aggregation algorithm described in step S3 specifically includes: Step 31: Classify all tasks and set the set of tasks that have not been met or newly added as , the set of tasks that have been satisfied and are to be executed is ; Step 32: For ,calculate Zhongke The number of tasks to aggregate ;for ,calculate Zhongke The number of tasks to aggregate ; Step 33: and The tasks are divided into and Reorder in ascending order; Step 34: Take out the tasks in turn, and Aggregate the tasks in Step 35: As a separate task to the resource scheduling system, if it still cannot be satisfied, then Add to the collection of unavailable tasks ; Step 36: from Remove if conditions are met , dynamic adjustment ends; otherwise, go to step 32.

10. The target aggregation method considering the differences in imaging modes of earth observation payloads according to claim 1, characterized in that: Step S34 specifically includes: Step 341: For ,like If the aggregation condition is met, proceed to step 342; otherwise, proceed to step 35; Step 342: Perform constraint check on the task aggregation result. If the satellite resource allocation constraint is met, generate an aggregation task and update the resource usage plan; otherwise, go to step 35.

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