A heterogeneous multi-sensor automatic planning system for unmanned aerial platforms

Abstract

This invention is a heterogeneous multi-sensor automatic planning system suitable for unmanned aerial vehicles (UAVs). First, it determines whether each target intersects with each flight segment. Based on the intersection, targets are split and merged to form new target points. Tasks are then arranged according to the order of intersection between the UAV and the new target points. The required flight segment length is truncated based on the maximum capture count required for each target. Next, the compatibility between the sensors on the UAV and the targets is tested, and scarce sensor resources are selected based on the compatibility. A greedy algorithm combined with flight segment migration is used to assign new targets to corresponding sensors. Finally, a task migration mechanism is used to improve the capture effect for targets that have not completed their capture tasks. This invention can quickly allocate sensors for capture based on the UAV's flight path and the positional relationship of the targets to be captured, ultimately generating a sensor scheduling order.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) control, specifically relating to a heterogeneous multi-sensor automatic planning system suitable for unmanned flight platforms. Background Technology

[0002] Currently, the acquisition, localization, and analysis of ground targets primarily rely on unmanned aerial vehicles (UAVs). Multiple heterogeneous sensors mounted on these UAVs are the main tools for target acquisition. With the development of information technology and electronic technology, the types and number of sensors that can be integrated into UAVs are gradually increasing. A single UAV can now simultaneously acquire multiple targets of different frequency bands and types, such as… Figure 1 As shown. However, with the increasing integration of flight platforms and the enhanced target acquisition capabilities, the efficiency of manually planning acquisition tasks is decreasing and can no longer meet actual mission requirements. Therefore, adopting automated heterogeneous multi-sensor mission planning is of great significance in adapting to the needs of modern target acquisition mission planning.

[0003] In the early days, due to the small number of sensors on unmanned aerial platforms, capture tasks could be quickly and manually formulated. However, in the current target capture tasks, the number of sensors, the types of targets, and the number of radiation sources are all showing a rapid growth trend, making manual capture task formulation extremely cumbersome and time-consuming. Moreover, this work has strict requirements on scheduling planning time and high requirements on the quality of scheduling planners. Relevant personnel need to have certain knowledge of aviation and sensors. At the same time, the training of planning and scheduling technicians is difficult and the training cycle is long. In addition, relying on manual capture planning has the following main shortcomings: (1) The geographical and aerial conditions of different targets are different, and the types of radiation sources carried by the targets are numerous, increasing the difficulty of determining when and where the targets can be captured and increasing the difficulty of modeling the intersection relationship between the targets and the flight path; (2) When planning and scheduling sensors, it is necessary not only to consider the value that can be gained from capture, but also to capture as many targets as possible. Therefore, this is a multi-target problem, and the planning difficulty of considering two targets at the same time when manually formulating scheduling tasks is high; (3) With the increase in the number of sensors and targets, there will be more combinations of sensor and target allocation. Due to the diverse combinations of sensors and targets, if a suitable combination is not selected, there is a high possibility of sensor resource competition during the acquisition process.

[0004] In recent years, with the development of control science, sensor scheduling is progressing towards automation and intelligence. Currently, some methods have been proposed in the field of sensor scheduling for unmanned aerial platforms, such as the multi-sensor automatic planning technology based on optimization algorithms and the multi-sensor planning technology based on genetic algorithms proposed by the 27th Research Institute of China Electronics Technology Group Corporation. These methods have reduced the burden of manual operation to some extent, but they still have not achieved fully automatic task planning and require manual assistance to handle conflict areas. Furthermore, these methods are relatively time-consuming and cannot meet the real-world capture planning requirements. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a heterogeneous multi-sensor automatic planning system suitable for unmanned aerial platforms. This system can quickly allocate the sensors used for capture based on the flight path of the unmanned aerial platform and the positional relationship of the target to be captured, ultimately generating a sensor scheduling order.

[0006] The technical solution adopted in this invention is: a heterogeneous multi-sensor automatic planning system suitable for unmanned aerial platforms, comprising the following steps:

[0007] Step 1, Target splitting and merging: Calculate whether each target intersects with each flight segment, and split or merge the targets appropriately based on the intersection of the targets and flight segments to form new target points;

[0008] Step 2, Target Reconnaissance Sorting: Arrange the tasks according to the order in which the unmanned aerial platform intersects with the new target points;

[0009] Step 3, Flight Truncation: Truncate the length of the capture segment based on the number of captures required for each target to reach the maximum mission benefit;

[0010] Step 4, Sensor preprocessing: Test the compatibility between the sensors carried by the unmanned aerial platform and the target, and select scarce sensor resources based on the compatibility.

[0011] Step 5: Use a greedy algorithm combined with flight segment migration for sensor scheduling: For new targets, use a greedy algorithm combined with flight segment migration to assign the targets to the corresponding sensors. The main basis for the allocation is the value that can be gained when capturing the target and the idle status of sensor resources.

[0012] Step 6, perform task migration for targets that have not completed their tasks: For targets that have not completed their capture tasks, use the task migration mechanism to improve the capture effect.

[0013] Furthermore, the detailed steps of step 1 are as follows:

[0014] Step 101: Based on the positional relationship of the targets along the entire route and the acquisition distance of the sensors, determine the segments where the route intersects with the targets, and calculate the intersection points with these segments;

[0015] Step 102: Calculate the distance between different segments that intersect with this target. According to the order in which the segments intersect with the target, calculate the distance between the end point of the previous intersecting segment and the start point of the next intersecting segment.

[0016] Step 103: When the distance between two adjacent flight segments intersecting the target is greater than a certain threshold, the two adjacent flight segments are split; otherwise, the two adjacent flight segments are merged.

[0017] Furthermore, the detailed steps of step 4 are as follows:

[0018] Step 401: Sort the targets according to the order in which they were captured, and match and record the sensors that can capture the current target one by one;

[0019] Step 402: Find the list of available sensors for all targets. If a target can only be captured by a single sensor, mark that sensor as a scarce sensor.

[0020] Furthermore, the detailed steps of step 5 are as follows:

[0021] Step 501, Initialize the sensor occupancy list OList a and the list of available sensors for the current target (CList) i , where a is the sensor serial number and i is the target serial number;

[0022] Step 502: Assign sensors and the start and end points of the capture segment to target i in sequence; before assigning sensors to target i, update the OList list of all sensors according to the capture start point of target i, and release targets that have completed the capture task in OList.

[0023] Step 503, based on the current target's available sensor list CList i , determine OList a (a∈CList i ) Check if there are available sensors in the list; if there are available sensors, select a sensor based on its scarcity and add target i to the selected sensor list; if OList a (a∈CList i If no sensor is available in step 504;

[0024] Step 504, based on the occupancy list of all available sensors for target i, OList a (a∈CList i) Among the targets, select all the targets in the occupancy list whose capture end points of the targets are on the same flight segment as the capture start point of target i, and calculate the distance between the capture start point of target i and the capture end point of the target in OList a (a ∈ CList i ) to find the distance to the capture end point of the target, and find the target T with the smallest overlapping distance min ; and judge whether the current minimum overlapping distance L min is less than the truncated distance L of target i in splitting and merging i , if L i > L min , then move the capture start point of target i to the capture end point of T min , move the capture end of target i backward by L min ; if the current condition is not met, go to step 505;

[0025] Step 505, when L i < L min , first move the capture end of i to the end position before truncation, and determine the start position according to the threat levels of i and T min ; after the capture end of target i is moved, the new overlapping distance becomes L new = L i - L min ; compare the threat levels of T min and i, and allocate the new overlapping flight segment to the target with a higher threat level. The capture start and end points corresponding to the two targets will also change, and record the corresponding changes in the occupancy list OList a of the selected sensor, and at the same time record the targets not assigned to the overlapping flight segment and the number of captures of the overlapping flight segment.

[0026] Further, the detailed steps of step 6 are as follows:

[0027] Step 601, the number of times that target i can be captured on the occupied flight segment is S1, and find whether there is a sub-target T among all the targets that is the same as the target before i is split s ;

[0028] Step 602, if T s exists, execute step 603, if not, execute step 604;

[0029] Step 603, detect the remaining available capture times S2 of the truncated flight segment of T s ; if S2 > S1, then migrate the detection task occupied by i to T s ; if S2 < S1, the number of migrated captures is S2, and the remaining un-migrated tasks execute step 604;

[0030] Step 604: Based on the remaining available capture attempts for the truncation segments of all targets competing with i, select the target T with the most remaining capture attempts. m Transfer the remaining capture tasks of i to T as much as possible. m .

[0031] The beneficial effects of this invention are:

[0032] (1) The present invention uses a target splitting and merging algorithm. For the intersection of the same target with multiple different segments on the route, the splitting and merging of the target is determined according to the distance between adjacent segments. When the distance between two adjacent segments is far, in order to prevent the target from occupying sensor resources for a long time, the target is split, so that the corresponding sensor can be released after the capture of a certain segment of the target is completed. When the distance is close, the two segments are merged. At this time, although some sensors will be idle, the impact on sensor resources is not significant due to the short distance.

[0033] (2) The present invention employs a sensor preprocessing mechanism to detect the compatibility between the sensor and the target, and can identify the scarce sensor among multiple sensors.

[0034] (3) This invention combines a greedy algorithm for flight segment migration to allocate sensor resources. Based on the scarcity of sensors and the availability of available sensors for the target during planning, the sensor required for capture, as well as the start and end points of capture, are allocated to the target. Targets in resource conflict areas are reasonably selected.

[0035] (4) The present invention adopts a target task migration algorithm. Based on the allocation of sensor resources by the greedy algorithm, for targets that have not completed the predetermined capture task, this algorithm re-allocates the capture task and finds some idle flight segments to allocate to targets that have not completed the task. Attached Figure Description

[0036] Figure 1 A schematic diagram illustrating the target acquisition process of an unmanned aerial platform;

[0037] Figure 2 This is a flowchart of the process of the present invention;

[0038] Figure 3 A diagram illustrating the splitting and merging of targets;

[0039] Figure 4 To split and merge the targets;

[0040] Figure 5 A schematic diagram of the available sensors for the target;

[0041] Figure 6 This is a schematic diagram of the flight segment movement;

[0042] Figure 7 Flowchart for sensor allocation;

[0043] Figure 8 For task migration between tasks with the same original goal;

[0044] Figure 9 For task migration between different objectives. Detailed Implementation

[0045] The invention will now be further described with reference to the accompanying drawings.

[0046] like Figure 2 As shown, this invention is a heterogeneous multi-sensor automatic planning system suitable for unmanned aerial platforms, comprising the following steps:

[0047] Step 1, Target splitting and merging: Calculate whether each target intersects with each flight segment, and split or merge the targets appropriately based on the intersection of the targets and flight segments to form new target points;

[0048] Step 2, Target Reconnaissance Sorting: Arrange the tasks according to the order in which the unmanned aerial platform intersects with the new target points;

[0049] Step 3, Flight Truncation: Truncate the length of the capture segment based on the number of captures required for each target to reach the maximum mission benefit;

[0050] Step 4, Sensor preprocessing: Test the compatibility between the sensors carried by the unmanned aerial platform and the target, and select scarce sensor resources based on the compatibility.

[0051] Step 5: Use a greedy algorithm combined with flight segment migration for sensor scheduling: For new targets, use a greedy algorithm combined with flight segment migration to assign the targets to the corresponding sensors. The main basis for the allocation is the value that can be gained when capturing the target and the idle status of sensor resources.

[0052] Step 6, perform task migration for targets that have not completed their tasks: For targets that have not completed their capture tasks, use the task migration mechanism to improve the capture effect.

[0053] This invention is mainly divided into four parts: target splitting and combination, sensor preprocessing, sensor allocation and target task migration using a greedy algorithm combined with flight segment migration.

[0054] Step 1 is designed to address the issue of needing to allocate sensors and capture start and end points multiple times for the same target when it intersects with multiple segments of the same flight path. To solve this problem, the present invention designs a corresponding operation process, the specific steps of which are as follows:

[0055] Step 101: Based on the positional relationship of the targets along the entire route and the acquisition distance of the sensors, determine the segments where the route intersects with the targets, and calculate the intersection points with these segments;

[0056] Step 102: Calculate the distance between different segments that intersect with this target. According to the order in which the segments intersect with the target, calculate the distance between the end point of the previous intersecting segment and the start point of the next intersecting segment.

[0057] Step 103: When the distance between two adjacent flight segments intersecting the target is greater than a certain threshold, the two adjacent flight segments are split; otherwise, the two adjacent flight segments are merged.

[0058] It should be noted that frequently switching between sensors when capturing the same target can impair the sensor's positioning and capture accuracy. For example... Figure 3 As shown, flight segments AB and DE represent the ranges where targets can be acquired on flight segments T1 and T2, respectively, while targets cannot be acquired on flight segment BCD. When a target is split into two sub-targets on flight segments AB and DE, if the length of BCD is short, switching sensors back and forth will affect the sensor acquisition accuracy. Therefore, a certain splitting threshold is set. When the length of BCD exceeds the threshold, AB and DE will be split, which is beneficial for effective sensor scheduling. Figure 4 As shown, there are two adjacent flight segments that pass through targets 10, 11, and 12. After splitting, target 12 was not split because its two segments were connected. Target 11 was split into target 11 and target 13, and target 10 was split into target 10 and target 14.

[0059] Step 4 is designed to obtain a suitable combination of sensors and targets when assigning sensors to the target. To achieve the above objective, the present invention has designed corresponding operations, the operation steps of which are as follows:

[0060] Step 401: Sort the targets according to the order in which they were captured, and match and record the sensors that can capture the current target one by one;

[0061] Step 402: Find the list of available sensors for all targets. If a target can only be captured by a single sensor, mark that sensor as a scarce sensor.

[0062] It should be noted that if a target can only be detected by a single sensor, this sensor is considered scarce. When this sensor appears in the list of available sensors for other targets, the target should prioritize selecting other sensors if possible. Figure 5As shown, targets T1, T4, T5, and T8 can only select sensor number 5. If target T2 selects sensor number 5 during sensor allocation, it is very likely to affect subsequent sensor allocation, creating a resource competition situation and leaving some targets without available sensors. Therefore, when pairing sensors with targets, the scarce sensors selected in this step can be used to guide sensor allocation.

[0063] Step 5 involves allocating the sensors needed for target acquisition and determining the start and end points for each target acquisition. To achieve this, the present invention designs a corresponding algorithm, the steps of which are as follows:

[0064] Step 501, Initialize the sensor occupancy list OList a and the list of available sensors for the current target (CList) i , where a is the sensor serial number and i is the target serial number;

[0065] Step 502: Assign sensors and the start and end points of the capture segment to target i in sequence; before assigning sensors to target i, update the OList list of all sensors according to the capture start point of target i, and release targets that have completed the capture task in OList.

[0066] Step 503, based on the current target's available sensor list CList i , determine OList a (a∈CList i Check if there are available sensors in the list; if so, select a sensor based on its scarcity and add target i to the selected sensor list. If OList a (a∈CList i If no sensor is available in step 504;

[0067] Step 504, based on the occupancy list of all available sensors for target i, OList a (a∈CList i For targets in the list, select all targets whose capture endpoint and target capture start point are in the same flight segment as target i, and calculate the capture start point and target OList. a (a∈CList i Find the target T with the smallest overlap distance from the target capture endpoint. min And determine the current minimum overlap distance L. min Is it less than the distance L that the target i is truncated during splitting and merging? i If L i >L min Then the capture starting point of target i will be moved to T.min The endpoint of target i is moved by the distance of the response; if the current condition is not met, proceed to step 505.

[0068] Step 505, when L i <L min First, move the capture endpoint of i to the endpoint position before truncation, based on i and T. min The threat level determines the starting point location; after the target i's capture endpoint is moved, the new overlap distance becomes L. new =L i -L min Comparing T min Based on the threat level of i, new overlapping flight segments are assigned to targets with a higher threat level. The acquisition start and end points for the two targets will also change, and the corresponding changes will be recorded in the occupancy list of the selected sensor (OList). a In the process, it simultaneously records the number of targets not assigned to overlapping segments and the number of times overlapping segments were captured.

[0069] To facilitate understanding of steps 504 and 505, the present invention... Figure 6 The diagram shows the corresponding schematic. The black line represents the current flight segment, the blue line represents the flight segment before the target was cut off, and the red line represents the flight segment after the cut-off. When the flight segments of target T1 and target T2 overlap, if the remaining blue flight segment distance of target T2 is greater than the overlapping red flight segment distance, then the actual flight segment after the flight segment is moved will become situation (1). When the remaining blue flight segment distance of target T2 is less than the overlapping red flight segment distance, then the actual flight segment after the flight segment is moved will become situation (2). At this time, the assignment of the overlapping flight segment in the green dashed line part will be determined by the threat level of the two targets, and this flight segment will be assigned to the one with the greater threat level. The flowchart of the algorithm is as follows. Figure 7 As shown.

[0070] Step 6 involves migrating target acquisition tasks that are currently occupied to available flight segments for other targets as much as possible. To achieve this, a corresponding algorithm is designed for this part. The main steps of the algorithm are as follows:

[0071] Step 601: The number of times target i can be captured on the occupied flight segment is S1. Search among all targets for whether there exists a sub-target T that is the same as target i before it was split. s ;

[0072] Step 602, if T s If it exists, proceed to step 603; if it does not exist, proceed to step 604.

[0073] Step 603, detect T sThe remaining available capture times S2 of the truncated flight segment; if S2 > S1, transfer the detection tasks occupied by i to T s ; if S2 < S1, the number of transferred captures is S2, and the remaining untransferred tasks execute step 604;

[0074] Step 604, select the target T with the most remaining capture times according to the remaining available capture times of the truncated flight segments of all targets in competition with i m and transfer the remaining capture tasks of i to T as much as possible m .

[0075] It should be noted that the above method cannot completely solve the problem of flight path conflicts. To clearly illustrate steps 602 and 603, corresponding schematic diagrams are given in Figure 8 . As can be seen from Figure 8 , target T is split into two sub-targets T1 and T2 during the splitting and merging of targets. The blue line segment represents the flight segment before the target is truncated, and the red flight segment represents the flight segment after truncation. When T1 is preempted for capture resources by other targets, if T2 is not occupied by other targets at this time and there are idle capture resources in the blue truncated flight segment, then the algorithm will transfer the occupied capture tasks of T1 to T2 and extend the capture flight segment of T2 according to the actual situation of the transfer.

[0076] To clearly explain the specific operations of step 604, this part shows the steps of step 604 in the form of a schematic diagram in Figure 9 . Among them, the black line segment represents the flight segment direction, the blue line segment represents the flight segment before the target is truncated, the red flight segment represents the flight segment after truncation, the green flight segment represents the flight segment occupied by target T2, and the purple flight segment represents the truncated flight segment occupied by T2. As can be seen from the figure, target T2 cannot complete the tasks of the purple flight segment, and after searching, it is found that target T1 in competition with target T2 has extra capture flight segments. At this time, the sensor resources occupied by target T1 can be allocated to target T2, that is, the purple flight line in the figure is transferred to the flight segment of target T1. It can be seen from the schematic diagram after the transfer that the capture flight line of T1 is extended by a certain distance, and this distance is equal to the distance of the purple flight segment. Although the purple flight segment is extended to target T1, this flight segment actually captures target T2. After completing the capture task of the purple flight segment, it will continue to capture target T1.

[0077] To verify the effectiveness of the present invention, the present invention was verified on 10 different flight routes. The distances and the number of targets of the 10 flight routes are different, and the degree of competition of the targets for sensors is also different. As shown in Table 1, the number of targets on 10 test flight routes is given.

[0078] Table 1 Number of targets on the test route

[0079] Test route Route 1 Route 2 Route 3 Route 4 Route 5 Number of targets 169 170 144 135 149 Test route Route 6 Route 7 Route 8 Route 9 Route 10 Number of targets 164 175 195 180 140

[0080] Table 2 presents the operational results of this invention on 10 test routes. The results show that this invention can provide results for all test routes within 0.5 seconds. Regarding the evaluation of the test results, this invention uses the product of the number of target capture attempts and the target threat level as the evaluation index. The theoretical optimal value given in Table 2 is the product of the maximum number of target capture attempts required and the target threat level. However, due to sensor resource competition during capture, the capture may not reach the so-called theoretical optimal value. As can be seen from Table 2, this invention can obtain a relatively excellent result in a very short time. Therefore, this invention can meet the current needs for rapid scheduling of capture tasks on some unmanned aerial platforms.

[0081] Table 2 Test results of the present invention

[0082] Test route Route 1 Route 2 Route 3 Route 4 Route 5 Theoretical optimal value 39646 39620 31715 39674 38774 Results of the Invention 39591 38418 31563 39659 38540 Planning time (s) 0.3141 0.2119 0.1077 0.1225 0.1360 Test route Route 6 Route 7 Route 8 Route 9 Route 10 Theoretical optimal value 48211 52854 49719 48922 35108 Results of the Invention 48089 52281 48965 48802 34744 Planning time (s) 0.1169 0.1910 0.1363 0.1278 0.1482

Claims

1. A heterogeneous multi-sensor automatic planning system suitable for unmanned aerial platforms, characterized in that, Includes the following steps: Step 1, Target splitting and merging: Calculate whether each target intersects with each flight segment, and split or merge the targets appropriately based on the intersection of the targets and flight segments to form new target points; The detailed steps are as follows: Step 101: Based on the positional relationship of the targets along the entire route and the acquisition distance of the sensors, determine the segments where the route intersects with the targets, and calculate the intersection points with these segments; Step 102: Calculate the distance between different segments that intersect with this target. According to the order in which the segments intersect with the target, calculate the distance between the end point of the previous intersecting segment and the start point of the next intersecting segment. Step 103: When the distance between two adjacent flight segments intersecting the target is greater than a certain threshold, the two adjacent flight segments are split; otherwise, the two adjacent flight segments are merged. Step 2, Target Reconnaissance Sorting: Arrange the tasks according to the order in which the unmanned aerial platform intersects with the new target points; Step 3, Flight Truncation: Truncate the length of the capture segment based on the number of captures required for each target to reach the maximum mission benefit; Step 4, Sensor preprocessing: Test the compatibility between the sensors carried by the unmanned aerial platform and the target, and select scarce sensor resources based on the compatibility. The detailed steps are as follows: Step 401: Sort the targets according to the order in which they were captured, and match and record the sensors that can capture the current target one by one; Step 402: Find the list of available sensors for all targets. When a target can only be captured by a single sensor, mark that sensor as a scarce sensor. Step 5: Perform sensor scheduling using a greedy algorithm combined with flight segment migration: For new targets, use a greedy algorithm combined with flight segment migration to assign the target to the corresponding sensor. The allocation is based on the value that can be gained when capturing the target and the availability of sensor resources. Detailed steps are as follows: Step 501, Initialize the sensor occupancy list OList a and the list of available sensors for the current target (CList) i , where a is the sensor serial number and i is the target serial number; Step 502: Assign sensors and the start and end points of the capture segment to target i in sequence; before assigning sensors to target i, update the OList list of all sensors according to the capture start point of target i, and release targets that have completed the capture task in OList. Step 503, based on the current target's available sensor list CList i , determine OList a , a∈CList i Check if there are available sensors in the list; if so, select a sensor based on its scarcity and add target i to the selected sensor list; if OList a , a∈CList i If no sensor is available, proceed to step 504; Step 504, based on the occupancy list of all available sensors for target i, OList a , a∈CList i For targets in the list, select all targets whose capture endpoint and target capture start point are in the same flight segment as target i, and calculate the capture start point and target OList. a , a∈CList i Find the target T with the smallest overlap distance, based on the distance from the target capture endpoint. min ; And determine the current minimum overlap distance L min Is it less than the distance L that the target i is truncated during splitting and merging? i If L i >L min Then the capture starting point of target i will be moved to T. min The endpoint of target i is moved by the distance of the response; if the current condition is not met, proceed to step 505. Step 505, when L i <L min First, move the capture endpoint of i to the endpoint position before truncation, based on i and T. min The degree of threat determines the starting point location; After the capture endpoint of target i is moved, the new overlap distance becomes L. new =L i -L min Compare T min Based on the threat level of i, new overlapping flight segments are assigned to targets with a higher threat level. The acquisition start and end points for the two targets will also change, and the corresponding changes will be recorded in the occupancy list of the selected sensor (OList). a In the process, it simultaneously records targets not assigned to overlapping segments and the number of times overlapping segments were captured; Step 6, perform task migration for targets that have not completed their tasks: For targets that have not completed their capture tasks, use the task migration mechanism to improve the capture effect.

2. The heterogeneous multi-sensor automatic planning system for unmanned aerial platforms according to claim 1, characterized in that, The detailed steps of step 6 are as follows: Step 601: The number of times target i can be captured on the occupied flight segment is S1. Search among all targets for whether there exists a sub-target T that is the same as target i before it was split. s ; Step 602, if T s If it exists, proceed to step 603; if it does not exist, proceed to step 604. Step 603, detect T s for the remaining available capture times S2 of the truncated flight segment of s ; if S2 > S1, migrate the detection tasks occupied by i to T s ; if S2 < S1, the number of migrated captures is S2, and the remaining unmigrated tasks execute Step 604; Step 604: Based on the remaining available capture attempts for the truncation segments of all targets competing with i, select the target T with the most remaining capture attempts. m Transfer the remaining capture tasks of i to T as much as possible. m .

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