A low-altitude networking system target photoelectric assignment method

Abstract

The application relates to a low-altitude networking system target photoelectric assignment method and belongs to the technical field of radar photoelectric networking. The method comprises the following steps: acquiring target and photoelectric equipment information, calculating a target threat degree, constructing a photoelectric detection cost function containing equipment performance characteristics and use state characteristics, constructing a cost matrix, constructing a non-balanced Hungarian algorithm processing flow suitable for a low-altitude networking scene, and finally solving an optimal assignment by using an improved non-balanced Hungarian algorithm. The method overcomes the assignment defects when the number of equipment and the number of targets are inconsistent, realizes dynamic and autonomous matching and distribution between multiple targets and multiple photoelectric equipment, and significantly improves the real-time performance, stability and overall detection efficiency of the low-altitude networking system. The method solves the problem that the existing technology cannot guarantee the timeliness and accuracy of photoelectric equipment distribution when corresponding to multiple low-altitude targets, so that dynamic and adaptive distribution cannot be realized between multiple low-altitude targets and multiple photoelectric equipment in a radar photoelectric networking system, and the overall detection efficiency of the low-altitude networking system is seriously restricted.

Description

Technical Field

[0001] This invention relates to a target photoelectric assignment method for a low-altitude networking system, belonging to the field of radar photoelectric networking technology. Background Technology

[0002] Radar and optoelectronic equipment networking is a common combination in low-altitude target detection. Low-altitude airspace surveillance primarily uses multi-target tracking. When multiple targets exist simultaneously in the airspace, the low-altitude networking system sends the multi-target position information detected by the radar to different optoelectronic devices for precise tracking and further identification and confirmation, thereby accurately grasping the overall air situation. Therefore, the rational allocation of optoelectronic equipment resources for multi-target tracking tasks becomes crucial to maximizing the effectiveness of radar-optoelectronic networked low-altitude detection systems. However, currently, the allocation of optoelectronic equipment resources for multi-target tracking tasks is still mainly manual, with high-value targets being manually selected before their position information is sent to the most suitable optoelectronic device. Obviously, when there are many targets, the timeliness and accuracy of this operation method cannot be guaranteed, resulting in the inability to achieve dynamic autonomous allocation between multiple targets and multiple devices in the radar-optoelectronic networked system, severely restricting the overall detection effectiveness of the low-altitude networking system. Therefore, it is very important to develop a target optoelectronic assignment method for low-altitude networking systems that incorporates the performance characteristics and usage status characteristics of optoelectronic devices into the assignment cost matrix, and combines it with the unbalanced Hungarian algorithm to achieve real-time assignment of multiple targets in radar and optoelectronic device low-altitude networking systems, and achieve dynamic autonomous matching and allocation between multiple targets and multiple optoelectronic devices. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies by providing a target photoelectric assignment method for low-altitude network systems. This method involves designing a cost function for target detection by photoelectric devices, incorporating the performance characteristics and usage status of the photoelectric devices into the assignment cost matrix, and utilizing an unbalanced Hungarian algorithm to minimize the overall cost of multi-target detection by multiple photoelectric devices. This enables real-time assignment of multiple targets in a low-altitude radar and photoelectric device network system, achieving the invention's objective of dynamic and autonomous matching and allocation between multiple targets and multiple photoelectric devices. This solves the problem that existing manual assignment methods prevent dynamic and autonomous allocation between multiple targets and multiple devices in radar and photoelectric network systems, severely restricting the overall detection efficiency of low-altitude network systems.

[0004] The technical solution of the present invention:

[0005] A target electro-optical assignment method for a low-altitude network system includes target threat level, electro-optical detection cost, and assignment algorithm; wherein the assignment algorithm is implemented using an unbalanced Hungarian algorithm; characterized in that the target electro-optical assignment method for the low-altitude network system is implemented through the following steps:

[0006] Step 1: Obtain the status information of the low-altitude networking system

[0007] Read and record all target information entering the monitoring area and all optoelectronic equipment information in the low-altitude networking system. The optoelectronic equipment information includes performance characteristics and usage status characteristics. The performance characteristics and usage status characteristics of optoelectronic equipment are introduced into the assignment cost matrix.

[0008] Step 2: Calculate the threat level of each low-altitude target.

[0009] Calculate and output the threat level of all low-altitude targets; the threat level calculation formula is as shown in equation (1):

[0010] (1)

[0011] The meaning and value rules of each parameter are described below:

[0012] φ th This represents the target threat level parameter, where 0 ≤ φ th ≤4;

[0013] 1) ω t The target type parameter is ω, which represents the target type when it is a regular drone, bird, static object at a high altitude, confirmed clutter, or object in another location. t The values ​​are 1, 0.5, 0, 0, and 0.9 respectively.

[0014] 2) ω c ω represents the target responsiveness parameter, used to determine whether the target is a cooperative target. When the target responds or is a known target, ω... c ω is 0.8; when the target does not respond or is an unknown target, ω c =1;

[0015] 3) ω m This represents the manually labeled parameter; when the target is the default target, ω m It is 0.6; when marked as a target of priority, ω m ω is 1; when marked as a target that does not require attention, ω m =0;

[0016] 4) ω d This represents the target distance parameter, when the target distance c ≤ c n At (minimum warning distance), ω d =1; when c n <c <c m At (maximum warning distance), ω d For (c m -c) / (c m -c n When c≥c m When, ωd =0;

[0017] 5) ε v This represents the absolute velocity parameter, when the target velocity v ≥ v h (At the inevitable alarm speed) ε v =1; when v l (Minimum attention speed) < v < v h At that time, ε v (0.7V + 0.3V) h -v l ) / (v h -v l ); when v < v l At that time, ε v =0;

[0018] 6) ε a ε represents the target heading angle parameter. When the target heading angle α ≤ π / 2, ε a For 1-4a 2 / π 2 When the target heading angle α > π / 2, ε a =0;

[0019] 7) ε h This represents the height parameter, when the target height h ≤ h min At (minimum target altitude), ε h =1; when h min <h<h max At (maximum target altitude), ε h For (h) max -h) 2 / (h max -h min ) 2 When h ≥ h max At that time, ε h =0;

[0020] 8) ε g This represents the group number identifier parameter, which is used when the radar reports the number of targets, g. n When ε < 3, g It is 0.7; when 3≤g n When ε < 10, g It is 0.85; when g n When ≥10, ε g =1;

[0021] 9) μ1 to μ4 are the weights of the corresponding variables, which are manually specified.

[0022] This threat metric quantifies the target's priority in the current air situation and serves as an important basis for subsequent electro-optical assignments.

[0023] Step 3: Calculate the cost of photoelectric detection

[0024] The smaller the cost of photoelectric detection, the more likely the target is to be assigned to that photoelectric detection; for low-altitude targets detected by photoelectric equipment, the formula for calculating the cost of photoelectric detection is as follows (2):

[0025] (2)

[0026] The variables appearing in equation (2) have the following meanings:

[0027] The cost value of photoelectric detection of target j;

[0028] Distance from the target to the photoelectric device;

[0029] Target threat level;

[0030] : Azimuth difference coefficient, the difference between the current azimuth angle of the photoelectric sensor and the target azimuth angle;

[0031] Photoelectric occupancy factor: if the photoelectric system has established an observation relationship with the target in the previous cycle, take 0; otherwise, take 1.

[0032] : Photoelectric performance rating. This parameter is used to comprehensively judge the focusing and locking time of a photoelectric device. The higher the value, the better the performance.

[0033] : Variable coefficient, which can adjust whether the assignment scheme switches photoelectric equipment more frequently; the larger the value, the more frequent the switching frequency.

[0034] This cost function comprehensively reflects the difficulty, time, stability, and system switching cost of photoelectric equipment tracking a target. The lower the cost, the more suitable it is for assignment.

[0035] Step 4: Determine if an observable target exists.

[0036] Determine whether there is a target within the observation range of any photoelectric device. If there is no target that can be observed by the photoelectric system, the target assignment scheme for this cycle ends.

[0037] Step 5: Implement different assignment strategies based on the target quantity.

[0038] Determine if the number of targets within the observation range of any photoelectric device is 1. If so, check if the target is already observed. If it is, check if the photoelectric device from the previous cycle can continue observation. If observation can continue, execute the inertial assignment procedure, meaning the observation of this target will continue using the photoelectric device from the previous cycle. If observation cannot continue or if it is a newly added target, execute the single-target assignment procedure. If the number of targets within the range exceeds 1, execute the multi-target assignment procedure.

[0039] Step 6: Construct the cost matrix and adapt it to the unbalanced Hungarian algorithm

[0040] The connection relationship between the optoelectronic device and the target is converted into a cost matrix, and the assignment process of the cost matrix is ​​abstracted. The assignment calculation is completed by the Hungarian algorithm. The specific description of the classic Hungarian algorithm is as shown in equation (3):

[0041] (3)

[0042] Step 7: Solve using the improved unbalanced Hungarian algorithm

[0043] Based on completing steps 1-5, the following procedures need to be performed when solving the runtime cost matrix assignment calculation using the improved unbalanced Hungarian algorithm:

[0044] 1) Ensure that the rows (electro-optical devices) and columns (low-altitude targets) of the cost matrix are not 1. When the number of low-altitude targets or electro-optical devices is 1, the allocation scheme does not need to proceed to the next step. When the number of electro-optical devices is only 1, the electro-optical devices select the low-altitude target with the lowest cost for observation. When the number of low-altitude targets is only 1, the low-altitude target is observed by the electro-optical device with the lowest cost for observing the low-altitude target.

[0045] 2) Determine whether the number of rows and columns of the current cost matrix are consistent (i.e., whether the number of optoelectronic devices is consistent with the number of observable low-altitude targets). If the number of rows and columns are inconsistent, record the value of the difference between the number of rows and columns as c. At the same time, use the maximum value, which is 10 times the largest element in the current cost matrix, to expand the cost matrix to be consistent with the number of rows and columns.

[0046] 3) When the number of rows and columns of the current cost matrix are inconsistent, the cost matrix is ​​reduced. The reduction process is defined as follows: select the smallest value in a single row / column of the matrix and subtract this value from the values ​​of all elements in that row / column. When the reduction process is performed using rows, it is called row reduction. When the reduction process is performed using columns, it is called column reduction. Whether the matrix is ​​reduced by rows or columns depends on the value of c, which is the difference between the number of rows and the number of columns obtained in step 2). When the number of optoelectronic devices is greater than the number of low-altitude targets (c>0), row reduction is used. When the number of optoelectronic devices is less than the number of low-altitude targets (c<0), column reduction is used. The reduction process is described by mathematical formula as shown in equation (4).

[0047] (4)

[0048] 4) Based on the number of zeros in each row of the reduced cost matrix, record the number of zeros in the row with the fewest zeros. At the same time, based on the size of the cost matrix at this time, create a traversal identifier matrix M of the same size as the cost matrix, and a result matrix R of the same size as the cost matrix; the initial values ​​of the elements in M ​​and R are all 0.

[0049] 5) Check if the number of "0"s in step 4) is 1. When the number of "0"s is 1, count the number of "0"s in each column of the cost matrix. The "0"s being counted must meet the condition that they have not been marked as traversed by the marking matrix M. Each time a "0" that meets the requirement is found in each column, increment the value of the element in the marking matrix M corresponding to the row where the "0" is located by 1, and mark the position of the "0" in the result matrix R. Then repeat this step for each row. When the number of "0"s is greater than 1, change the execution order to count the number of "0"s by row first, and then count the number of "0"s by column, and execute the same scheme as the above steps. Count the cumulative number of times the two transformations are performed, and record it as the number of times n.

[0050] 6) Determine if the number of times n is equal to the number of rows in the current cost matrix. If the number of times n is equal to the number of rows in the cost matrix, count the positions marked in the result matrix R, and output each column (each target) corresponding to each marked row (representing each device) as the result, which is the allocation result; then execute step (9); if the number of times n is not equal to the number of rows in the current cost matrix, continue to execute step (7).

[0051] 7) Find all the identifier matrix M and the corresponding cost matrix elements that are still 0 after step 5); if the minimum value of these elements is not 0, subtract the value of the cost matrix element at the position corresponding to the element 0 in the identifier matrix M, and add the value of the cost matrix element at the position corresponding to the element 2 in the identifier matrix M, and then re-execute steps 4) to 6); (in step 4) creating the identifier matrix M and the result matrix R is changed to resetting the values ​​of M and R to their initial state); if the minimum value of these elements is 0, continue to the next step.

[0052] 8) Reset the result matrix R. Change the element in the result matrix R corresponding to the element with a value of 0 in the current cost matrix to 1, and change the elements in other positions to 0. At this time, the position of the element in the result matrix R with a value of 1 indicates that the photoelectric device in the corresponding row can observe the target in the corresponding column. Find all the schemes of single device (single row) corresponding to single target (single column) and use them as the allocation result.

[0053] 9) Organize the existing allocation results; regardless of whether the allocation result is obtained in step 6) or step 8), if the number of rows and columns in the original cost matrix is ​​unequal, solving the cost matrix after step 2) will inevitably result in a situation where a false target is assigned to a real optoelectronic device (the number of targets is less than the number of optoelectronic devices) or a real target is assigned to a false optoelectronic device (the number of targets is greater than the number of optoelectronic devices). Such problems in the allocation results should be screened and eliminated. When the allocation result is obtained from step 8), there are multiple solutions to the allocation scheme. Therefore, it is necessary to merge some schemes that are completely consistent after screening. If there are still several different schemes after merging, the cost of each scheme is minimized. Therefore, a scheme can be randomly selected as the allocation scheme for the current period. Finally, this allocation scheme is used as the algorithm's output to complete the dynamic autonomous allocation between multiple low-altitude targets and multiple optoelectronic devices in the radar optoelectronic networking system.

[0054] The advantages of this invention compared to the prior art are:

[0055] This target electro-optical assignment method for low-altitude networking systems introduces equipment performance characteristics and usage status characteristics into the electro-optical assignment cost matrix for the first time, making the assignment results more consistent with the actual system capabilities. It comprehensively considers multi-dimensional information such as target type, speed, altitude, heading, communicability, and manual identification, proposing a multi-dimensional target threat assessment model, creating threat calculation formula (1) and cost calculation formula (2), and designing and constructing an unbalanced Hungarian algorithm processing flow adapted to low-altitude networking scenarios, overcoming the assignment defects when the number of devices and targets are inconsistent. It achieves dynamic autonomous matching and allocation between multiple targets and multiple electro-optical devices, significantly improving the real-time performance, stability, and overall detection efficiency of the low-altitude networking system. It solves the problem that existing methods cannot guarantee the timeliness and accuracy of electro-optical device assignment when dealing with multiple low-altitude targets, resulting in the inability to achieve dynamic adaptive assignment between multiple low-altitude targets and multiple electro-optical devices in radar electro-optical networking systems, which seriously restricts the overall detection efficiency of low-altitude networking systems. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating the overall workflow of a target photoelectric assignment method for a low-altitude networking system according to the present invention.

[0057] Figure 2 This is a schematic diagram of experimental scenario 1 of embodiment 1 of the present invention;

[0058] Figure 2 -(a) is a schematic diagram of the assignment results in Experiment Scenario 1 without considering the occupancy factor and performance score of optoelectronic equipment;

[0059] Figure 2 -(b) is a schematic diagram of the assignment results for experimental scenario 1, considering only the occupancy coefficient of optoelectronic equipment;

[0060] Figure 2 -(c) is a schematic diagram of the complete cost matrix assignment result of low-altitude target-optoelectronic equipment in experimental scenario 1;

[0061] Figure 3 This is a schematic diagram of experimental scenario 2 of embodiment 2 of the present invention;

[0062] Figure 3 -(a) is a schematic diagram of the assignment results in Experiment Scenario 2 without considering the occupancy factor and performance score of optoelectronic equipment;

[0063] Figure 3 -(b) is a schematic diagram of the assignment results for experimental scenario 2, considering only the occupancy factor of optoelectronic equipment;

[0064] Figure 3 -(c) is a schematic diagram of the complete cost matrix assignment result for experimental scenario 2;

[0065] Figure 4 This is a schematic diagram of experimental scenario 3 in embodiment 3 of the present invention;

[0066] Figure 4 -(a) is a schematic diagram of the assignment results using only simple cost in experimental scenario 3;

[0067] Figure 4 -(b) is a schematic diagram of the assignment results in Experiment Scenario 3 using the cost of considering the azimuth coefficient; Figure 4 -(c) is a schematic diagram of the assignment results for Experiment Scenario 3 considering only the cost of photoelectric occupancy coefficient; Figure 4 -(d) is a schematic diagram of the result of the complete cost matrix assignment in experimental scenario 3. Detailed Implementation

[0068] The following detailed description, in conjunction with the accompanying drawings, illustrates a specific embodiment of the target photoelectric assignment method for a low-altitude networking system according to the present invention (see attached figures). Figure 1-4 ):

[0069] A target electro-optical assignment method for a low-altitude network system includes an assignment algorithm; the assignment algorithm is implemented using an unbalanced Hungarian algorithm; characterized in that the target electro-optical assignment method for the low-altitude network system is achieved through the following steps:

[0070] • Obtain target and optoelectronic device information; the optoelectronic device information includes performance characteristics and usage status characteristics, and the performance characteristics and usage status characteristics of the optoelectronic device are incorporated into the assignment cost matrix;

[0071] • Calculate the target threat level;

[0072] Calculate and output the threat level of all low-altitude targets; the threat level calculation formula is as shown in equation (1):

[0073] (1)

[0074] The meaning and value rules of each parameter are described in Table 1. This threat level calculation quantifies the priority of the target under the current air situation and becomes an important basis for subsequent photoelectric assignment.

[0075] • Construct a photoelectric detection cost function that includes equipment performance characteristics and usage status characteristics;

[0076] Calculate the photoelectric detection cost; different photoelectric devices in the low-altitude networking system have different lock times and lock difficulties for different targets. It is necessary to quantify the difficulty of different photoelectric devices detecting different targets through photoelectric detection cost; the smaller the photoelectric detection cost, the more likely the target is to be assigned to that photoelectric detection; for low-altitude targets detected by photoelectric devices, the formula for calculating their photoelectric detection cost is as follows (2):

[0077] (2)

[0078] The variables appearing in equation (2) have the following meanings:

[0079] The cost value of photoelectric detection of target j;

[0080] Distance from the target to the photoelectric device;

[0081] Target threat level;

[0082] : Azimuth difference coefficient, the difference between the current azimuth angle of the photoelectric sensor and the target azimuth angle;

[0083] Photoelectric occupancy factor: if the photoelectric system has established an observation relationship with the target in the previous cycle, take 0; otherwise, take 1.

[0084] : Photoelectric performance rating. This parameter is used to comprehensively judge the focusing and locking time of a photoelectric device. The higher the value, the better the performance.

[0085] : Variable coefficient, which can adjust whether the assignment scheme switches photoelectric equipment more frequently; the larger the value, the more frequent the switching frequency.

[0086] This cost function comprehensively reflects the difficulty, time, stability, and system switching cost of photoelectric equipment tracking a target. The lower the cost, the more suitable it is for assignment.

[0087] • Construct the cost matrix;

[0088] The connection relationship between optoelectronic equipment and low-altitude targets is converted into a cost matrix, where the row elements of the cost matrix represent optoelectronic equipment and the column elements represent low-altitude targets.

[0089] • Construct an improved unbalanced Hungarian algorithm processing flow adapted to low-altitude networking scenarios to achieve adaptive optimal assignment.

[0090] When solving the runtime cost matrix assignment calculation using the improved unbalanced Hungarian algorithm, the following procedures need to be performed:

[0091] 1) Ensure that neither the rows (optoelectronic equipment) nor the columns (low-altitude targets) of the cost matrix are equal to 1.

[0092] 2) Determine whether the number of rows and columns of the current cost matrix are consistent, that is, whether the number of optoelectronic devices is consistent with the number of observable low-altitude targets. If they are inconsistent, record the value of the difference between the number of rows and the number of columns as c; and expand the cost matrix to be consistent with the number of rows and columns.

[0093] 3) When the number of rows and columns of the current cost matrix are inconsistent, the cost matrix is ​​reduced. Row reduction is used when the number of optoelectronic devices is greater than the number of low-altitude targets (c>0), and column reduction is used when the number of optoelectronic devices is less than the number of low-altitude targets (c<0).

[0094] 4) By recording the number of "0"s in the row with the fewest "0"s in the reduced cost matrix, create an identifier matrix M of the same size as the current cost matrix, and a result matrix R of the same size; the initial values ​​of the elements in M ​​and R are all 0.

[0095] 5) In step 4), when the number of "0"s is 1, count the number of "0"s in each column of the cost matrix. Each time a "0" that meets the requirement of not being marked as traversed by the marking matrix M is found in each column, increment the value of the element in the marking matrix M corresponding to the row where the "0" is located by 1, and mark the position of the "0" in the result matrix R. Then repeat this step for each row. When the number of "0"s is greater than 1, change the execution order to count the number of "0"s by row first, and then count the number of "0"s by column, and execute the same scheme as the above steps. Count the cumulative number of times the two transformations are performed, and record it as the number of times n.

[0096] 6) If the number of times n is determined, and it is equal to the number of rows in the current cost matrix, then each column (each target) corresponding to each row (representing each device) in the result matrix R is output, which is the allocation result. Then, step 9 is executed. If the number of times n is not equal to the number of rows in the current cost matrix, then step 7 is executed.

[0097] 7) Find all the identifier matrix M and the corresponding cost matrix elements that are still 0 after step 5); if the minimum value of these elements is not 0, subtract the value of the cost matrix element at the position corresponding to the element 0 in the identifier matrix M, and add the value of the cost matrix element at the position corresponding to the element 2 in the identifier matrix M, and then re-execute steps 4) to 6); (in step 4) creating the identifier matrix M and the result matrix R is changed to resetting the values ​​of M and R to their initial state); if the minimum value of these elements is 0, continue to the next step.

[0098] 8) Reset the result matrix R. Change the element in the result matrix R corresponding to the element with a value of 0 in the current cost matrix to 1, and change the elements in other positions to 0. At this time, the position of the element in the result matrix R with a value of 1 indicates that the photoelectric device in the corresponding row can observe the target in the corresponding column. Find all the schemes of single device (single row) corresponding to single target (single column) and use them as the allocation result.

[0099] 9) Organize the existing allocation results; regardless of whether the allocation result is obtained in step 6) or step 8), as long as the number of rows and columns in the original cost matrix is ​​not equal, solving the cost matrix after step 2) will definitely result in a problem where a certain false target is assigned to a real optoelectronic device (the number of targets is less than the number of optoelectronic devices) or a certain true target is assigned to a false optoelectronic device (the number of targets is greater than the number of optoelectronic devices). Such problems in the allocation results should be screened and eliminated.

[0100] When the allocation result is obtained from step 8), there are multiple solutions to the allocation scheme. Therefore, it is necessary to merge some schemes that are completely consistent after screening. If there are still several different schemes after merging, the cost of each scheme is minimized. Therefore, a scheme can be randomly selected as the allocation scheme for the current cycle. Finally, the allocation scheme is used as the algorithm solution output to complete the dynamic autonomous allocation between multiple low-altitude targets and multiple optoelectronic devices in the radar optoelectronic networking system.

[0101] Furthermore, the photoelectric detection cost function includes: target distance, target threat level, azimuth difference, equipment occupancy coefficient, equipment performance score, and variation coefficient.

[0102] Furthermore, the unbalanced Hungarian algorithm includes: when the number of devices is not equal to the target number, using a maximum value to expand the matrix, and selecting row reduction or column reduction according to the relationship between the number of devices and the target number.

[0103] Furthermore, the target threat calculation formula includes eight dimensions: target type, communication capability, human identification, distance, speed, heading, altitude, and group size.

[0104] The result of multi-target assignment depends on the target threat level, photoelectric detection cost, and assignment algorithm; threat level assessment is used to quantify the level of attention that all targets should receive, and its calculation is related to factors such as the nature, location, motion characteristics, and group attributes of the target. The threat level calculation formula of the multi-dimensional target threat level assessment model proposed in this invention is as shown in equation (1).

[0105] (1)

[0106] The meaning of the parameters and the rules for taking values ​​in equation (1) are shown in Appendix Table 1.

[0107] Appendix 1 Meaning of Threat Level Estimation Variables

[0108]

[0109]

[0110] The time and difficulty for different optoelectronic devices to lock onto different targets in a low-altitude network system are different. It is necessary to quantify the difficulty of different optoelectronic devices to detect different targets by optoelectronic detection cost. The smaller the detection cost, the more attractive the optoelectronic device is. The cost calculation formula is as shown in equation (2).

[0111] (2)

[0112] The variables appearing in Equation 2 have the following meanings:

[0113] The cost value of photoelectric detection of target j;

[0114] Distance from the target to the optoelectronic device;

[0115] Target threat level;

[0116] : Azimuth difference coefficient, the difference between the current azimuth angle of the photoelectric sensor and the target azimuth angle;

[0117] Photoelectric occupancy factor: if the photoelectric system has established an observation relationship with the target in the previous cycle, take 0; otherwise, take 1.

[0118] : Photoelectric performance rating. This parameter is used to comprehensively judge the focusing and locking time of a photoelectric device. The higher the value, the better the performance.

[0119] : Variable coefficient, which can adjust whether the assignment scheme switches photoelectric equipment more frequently; the larger the value, the more frequent the switching frequency.

[0120] When using formula (2) to calculate the cost, it should be noted that the cost calculation is only applicable to targets that can be observed by the corresponding photoelectric device. When the target is outside the observation range of a certain photoelectric device, the cost value between the photoelectric device and the target is -1.

[0121] After summarizing the connection between photoelectric and target as a cost matrix, the original problem is abstracted into an assignment problem of the cost matrix. The Hungarian algorithm can be used to solve this type of problem, and the solution of the Hungarian algorithm is shown in equation (3):

[0122] (3)

[0123] This invention discloses a target photoelectric assignment method for a low-altitude networking system. It designs and constructs a processing flow adapted to low-altitude networking scenarios using an unbalanced Hungarian algorithm, overcoming assignment defects when the number of devices and the number of targets are inconsistent. The essence of the processing flow is a cost-solving scheme, and its specific steps are as follows:

[0124] 1) When the cost matrix needs to be assigned, it must be ensured that the rows (devices) and columns (targets) of the cost matrix are not 1; when the number of targets or devices is 1, the allocation scheme does not need to proceed to the next step; when the number of optoelectronic devices is only 1, the optoelectronic devices select the target with the lowest cost for observation; when the number of targets is only 1, the optoelectronic device with the lowest cost for observing the target observes the target.

[0125] 2) Determine whether the number of rows and columns of the current cost matrix are consistent, that is, whether the number of devices and the number of observable targets are consistent. If the number of rows and columns are inconsistent, record the value of the difference between the number of rows and the number of columns as c, and at the same time use the maximum value, that is, 10 times the largest element in the current cost matrix, to expand the cost matrix to have the same number of rows and columns.

[0126] 3) When the number of rows and columns in step 2) is inconsistent, the cost matrix is ​​reduced. The reduction process is defined as follows: select the smallest value in a single row / column of the matrix and subtract this value from the values ​​of all elements in that row / column. When using rows for reduction, it is called row reduction; when using columns for reduction, it is called column reduction. Whether to perform row reduction or column reduction on the matrix depends on the value c obtained in step 2). When the number of devices is greater than the target (c>0), row reduction is used; when the number of devices is less than the target (c<0), column reduction is used. The reduction scheme is described by mathematical formula as shown in equation (4).

[0127] (4)

[0128] 4) Count the number of zeros in each row of the processed cost matrix, record the number of zeros in the row with the fewest zeros, and create an identifier matrix M and a result matrix R of the same size as the cost matrix, based on the current size of the cost matrix. The initial values ​​of the elements in M ​​and R are all 0.

[0129] 5) Check if the number of "0"s in step 4) is 1. When the number of "0"s is 1, count the number of "0"s in each column of the cost matrix. The "0"s to be counted must meet the condition that they have not been marked as traversed by the labeling matrix M. Each time a "0" that meets the requirements is found in each column, increment the value of the element in the labeling matrix M corresponding to the row where the "0" is located by 1, and mark the position of the "0" in the result matrix R. Then repeat this step for each row. When the number of "0"s is greater than 1, change the execution order to count the number of "0"s by row first, and then count the number of "0"s by column. Execute the same scheme as the above steps, and count the cumulative number of times the two transformations are performed, denoted as the number of times n.

[0130] 6) Determine if the number of times n is equal to the number of rows in the current cost matrix. If the number of times n is equal to the number of rows in the cost matrix, count the positions marked in the result matrix R, and output each column (each target) corresponding to each marked row (each device) as the allocation result. Then execute step (9). If the number of times n is not equal to the number of rows in the current cost matrix, continue to execute step (7).

[0131] 7) Find all the identifier matrices M whose elements are still 0 after step 5), and the corresponding cost matrix elements; if the minimum value of these elements is not 0, subtract this value from the cost matrix element value at the position corresponding to the 0 element in the identifier matrix M, and add this value to the cost matrix element value at the position corresponding to the 2 element in the identifier matrix M, and then re-execute steps 4) to 6); in step 4), create the identifier matrix M and the result matrix R, and reset the values ​​of M and R to their initial state; if the minimum value of these elements is 0, continue to the next step.

[0132] 8) Reset the result matrix R; change the result matrix element at the position corresponding to the element with a value of 0 in the current cost matrix to 1, and change the other elements to 0; at this time, the position of the element with a value of 1 in the result matrix R indicates that the device in the corresponding row can observe the target in the corresponding column; find all the schemes of single device (single row) corresponding to single target (single column) and use them as the allocation result.

[0133] 9) Organize the existing allocation results. Regardless of whether the allocation result is obtained in step (6) or step (8), as long as the number of rows and columns in the original cost matrix is ​​not equal, solving the cost matrix after step (2) will inevitably result in a false target being assigned to a true photoelectric target (the number of targets is less than the number of photoelectric targets) or a true target being assigned to a false photoelectric target (the number of targets is greater than the number of photoelectric targets). Screen out such problems in the allocation results. If the allocation result is obtained from step (8), there are multiple solutions to the allocation scheme. Therefore, it is necessary to merge some schemes that are completely consistent after screening. If there are still several different schemes after merging, the cost of each scheme is the minimum. Therefore, a scheme can be randomly selected as the allocation scheme for the current period (for example, the first scheme). Finally, the allocation scheme is used as the output of the algorithm solution.

[0134] The following three specific embodiments further illustrate the implementation effect of the target photoelectric assignment method for a low-altitude networking system according to the present invention:

[0135] Example 1: Single target traversing the overlapping region of two photoelectric sensors

[0136] Establish multi-electro-optical-multi-target detection scenarios with varying numbers, plot the computation time curves throughout the target's operational cycle, and analyze whether the assignment results of this method meet the expectations of manual assignment and whether the allocation satisfies the timeliness requirements to verify the feasibility and advancement of the method of this invention.

[0137] Example 1 scenario design is as follows: (see Figure 2 )like Figure 2 As shown, two optoelectronic devices with different performance are deployed, and the two optoelectronic devices have overlapping detection areas. When a target is sent across the center of the boundary line between the overlapping areas of the two optoelectronic devices, the error of the target position information measured by the radar sensor is Gaussian distributed.

[0138] After a data update cycle, a target passing through the area will display a corresponding number of trackpoints. Let the state when the electro-optical system is tracking the target be the target number, and the state when the electro-optical system is idle be 0. A schematic diagram of the electro-optical system's state is then drawn, with the horizontal axis representing the cycle number and the vertical axis representing the trackpoint number. See the simple cost matrix, which does not consider the electro-optical occupancy factor and performance score. Figure 2 - (a) The cost matrix considering only the photoelectric occupancy factor is shown in [reference]. Figure 2 -(b), and the complete cost matrix designed by the method of the present invention are assigned (see reference). Figure 2 -(c)

[0139] Depend on Figure 2 It can be seen that considering the photoelectric occupancy factor in single-target assignment prevents a target from frequently switching photoelectric devices after being captured by one, which also makes... Figure 2 -(b) and Figure 2 The number of switching operations for optoelectronic devices in (c) is much smaller than that in [the following context]. Figure 2 -(a). Through Figure 2 -(b) and Figure 2 As shown in the comparison in (c), the cost calculation of the complete cost matrix achieves adaptive assignment, prioritizing the allocation of targets to optoelectronic devices with better performance, higher scores, and idle states, while ensuring stable tracking of individual targets by the devices. Therefore, Example 1 demonstrates that the introduction of optoelectronic device performance scoring can generate a better optoelectronic device observation scheme in real time.

[0140] Example 2: Single target traversing a four-electro-optical overlap region

[0141] To further verify the allocation performance of the complete cost matrix under complex optoelectronic device conditions, Example 2 sets up four optoelectronic devices with identical performance. Each pair of the four devices has overlapping detection areas. A target is moved across the center of the boundary line between the overlapping areas of the four optoelectronic devices. The adaptive capability and stability of the allocation scheme are tested by observing the target's movement within the overlapping areas of multiple optoelectronic detections. Example scenario design (see...) Figure 3 ).

[0142] like Figure 3 As shown; the same three cost matrices as in Example 1 were used for task assignment experiments, specifically: the simple cost matrix without considering photoelectric occupancy coefficient and performance score is shown in [reference]. Figure 3 - (a) The cost matrix considering only the photoelectric occupancy factor is shown in [reference]. Figure 3 -(b), and the complete cost matrix designed by the method of the present invention are assigned (see reference). Figure 3 -(c)

[0143] Depend on Figure 3 It can be seen that the photoelectric occupancy coefficient can maintain a stable observation relationship between the target and the photoelectric equipment when the target frequently crosses the detection overlap area of ​​multiple devices. Without considering the photoelectric occupancy coefficient, the allocation scheme will be repeatedly modified, which is not conducive to a single device establishing a stable observation relationship with the target. The cost calculation of the complete cost matrix realizes the adaptive allocation of the target to the photoelectric equipment with better performance, higher score and idle state, while ensuring that the device can stably track a single target.

[0144] Example 3: Complex Scene with Two Targets and Multiple Optical Devices

[0145] The azimuth coefficient is designed to reduce the time spent on the device's servo system and refocusing when switching targets. To verify the impact of photoelectric occupancy coefficient and azimuth coefficient on device selection in multi-target, multi-photoelectric scenarios, Example 3 adds a target moving along the negative y-axis compared to Example 2. The photoelectric position and target trajectory in Example 3 are shown in (see...). Figure 4 ).

[0146] The full-cycle target-photoelectric distribution situation in Example 3 is as follows: Figure 4 As shown. From Figure 4 -(a) Assignment results using only simple costs Figure 4 -(b) Assignment results that take into account the cost of azimuth coefficients and Figure 4 -(c) A comparison of assignment results considering only the cost of the photoelectric occupancy factor shows that the azimuth factor helps improve the stability of observation relationships in multi-target scenarios; comparison Figure 4 -(c) and Figure 4 -(d) The assignment results of the cost using the complete design show that the cost design implementation of the complete cost matrix prioritizes the allocation of new targets to the idle photoelectric equipment when the photoelectric equipment is available, so as to minimize the delay caused by the switching of photoelectric equipment between different targets, thereby effectively improving the response speed of the low-altitude networking system.

[0147] By introducing different parameters in the cost design in the above three embodiments, the roles of different coefficients in target assignment are obtained. Inertial design is used to continue the observation relationship when the photoelectric captures a single target image to maintain the stability of the system observation. Photoelectric performance evaluation design ensures that the target is always observed by the photoelectric device with better performance. Photoelectric occupancy coefficient stabilizes the observation relationship when multiple targets are observed by multiple photoelectric devices at the same time. Photoelectric azimuth coefficient helps the system select the photoelectric device with a more suitable current rotation angle when the target needs to be observed by a different photoelectric device.

[0148] The above description is merely a preferred embodiment of the present invention. The above examples do not limit the substantive content of the present invention in any way. Any simple modifications or variations made by those skilled in the art to the above specific embodiments based on the technical essence of the present invention after reading this specification, as well as equivalent embodiments that may be changed or modified using the disclosed technical content, shall still fall within the scope of the technical solution of the present invention and shall not depart from the essence and scope of the present invention.

Claims

1. A target photoelectric assignment method for a low-altitude network system, comprising an assignment algorithm; the assignment algorithm is implemented using an unbalanced Hungarian algorithm; characterized in that, The target photoelectric assignment method for this low-altitude networking system is achieved through the following steps:

1. Obtain target and optoelectronic device information; the optoelectronic device information includes performance characteristics and usage status characteristics, which are incorporated into the assignment cost matrix; II. Calculate the target threat level; Calculate and output the threat level of all low-altitude targets; the threat level calculation formula is as shown in equation (1): ； The quantitative targets will prioritize the focus under the current air situation, providing a key basis for subsequent photoelectric assignments. III. Construct a photoelectric detection cost function that includes equipment performance characteristics and usage status characteristics; Calculate the cost of photoelectric detection; quantify the difficulty of different photoelectric devices detecting different targets; The smaller the cost of photoelectric detection, the more likely the target is to be assigned to that photoelectric detection; for low-altitude targets detected by photoelectric equipment, the formula for calculating the cost of photoelectric detection is as follows (2): ； IV. Constructing the cost matrix; The connection relationship between optoelectronic equipment and low-altitude targets is converted into a cost matrix, where the row elements of the cost matrix are optoelectronic equipment and the column elements are low-altitude targets. V. Constructing an improved unbalanced Hungarian algorithm processing flow adapted to low-altitude networking scenarios to achieve adaptive optimal assignment; when calculating the assignment allocation using the improved unbalanced Hungarian algorithm to solve the running cost matrix, the following process operations need to be performed: 1) Ensure that neither the rows (optoelectronic equipment) nor the columns (low-altitude targets) of the cost matrix are equal to 1. 2) Determine whether the number of rows and columns of the current cost matrix are consistent, that is, whether the number of optoelectronic devices is consistent with the number of observable low-altitude targets. If they are inconsistent, record the value of the number of rows minus the number of columns as c. And the cost matrix is ​​expanded to match the number of rows and columns; 3) When the number of rows and columns of the current cost matrix are inconsistent, the cost matrix is ​​reduced. Row reduction is used when the number of optoelectronic devices is greater than the number of low-altitude targets (c>0), and column reduction is used when the number of optoelectronic devices is less than the number of low-altitude targets (c<0). 4) By recording the number of "0"s in the row with the fewest "0"s in the reduced cost matrix, create an identifier matrix M of the same size as the current cost matrix, and a result matrix R of the same size; the initial values ​​of the elements in M ​​and R are all 0. 5) In step 4), when the number of "0"s is 1, count the number of "0"s in each column of the cost matrix. Each time a "0" that meets the requirement of not being marked as traversed by the marking matrix M is found in each column, increment the value of the element in the marking matrix M corresponding to the row of the "0" by 1, and mark the position of the "0" in the result matrix R. Then repeat this step for each row. When the number of "0"s is greater than 1, change the execution order to count the number of "0"s by row first, and then count the number of "0"s by column, and execute the same scheme as the above steps. Count the cumulative number of times the two transformations are performed, and record it as the number of times n. 6) Calculate the number of times n is performed. If n equals the number of rows in the current cost matrix, output each column (each target) corresponding to each row (representing each device) in the result matrix R, which is the allocation result. Then proceed to step 9. If n does not equal the number of rows in the current cost matrix, proceed to step 7. 7) Find all the identifier matrices M whose elements are still 0 after step 5) and the corresponding cost matrix elements; If the minimum value of these elements is not 0, subtract the value of the cost matrix element at the position corresponding to the element with 0 in the identification matrix M, add the value of the cost matrix element at the position corresponding to the element with 2 in the identification matrix M, and then repeat steps 4) to 6). If the minimum value of these elements is 0, proceed to the next step; 8) Reset the result matrix R. Change the element in the result matrix R corresponding to the element with a value of 0 in the current cost matrix to 1, and change the elements in other positions to 0. At this time, the position of the element with a value of 1 in the result matrix R indicates that the photoelectric device in the corresponding row can observe the target in the corresponding column. Find all the schemes of single device (single row) corresponding to single target (single column) and use them as the allocation result. 9) Organize the existing allocation results; Regardless of whether the allocation result is obtained in step 6) or step 8), as long as the number of rows and columns in the original cost matrix is ​​unequal, solving the cost matrix after step 2) will inevitably result in a situation where a false target is assigned to a real optoelectronic device (the number of targets is less than the number of optoelectronic devices) or a real target is assigned to a false optoelectronic device (the number of targets is greater than the number of optoelectronic devices). Such problems in the allocation result should be screened and eliminated. When the allocation result is obtained from step 8), there are multiple solutions to the allocation scheme. Therefore, it is necessary to merge some schemes that are completely consistent after screening. If there are still several different schemes after merging, the cost of each scheme is minimized. Thus, a scheme is randomly selected as the allocation scheme for the current period. Finally, the allocation scheme is used as the output of the algorithm solution to complete the dynamic autonomous allocation between multiple low-altitude targets and multiple optoelectronic devices in the radar optoelectronic networking system.

2. The target photoelectric assignment method for a low-altitude networking system according to claim 1, characterized in that: The target threat level calculation formula includes eight dimensions: target type, communication capability, human identification, distance, speed, heading, altitude, and group size.

3. The target photoelectric assignment method for a low-altitude networking system according to claim 1, characterized in that: The photoelectric detection cost function includes: target distance, target threat level, azimuth difference, equipment occupancy coefficient, equipment performance score, and variation coefficient.

4. The target photoelectric assignment method for a low-altitude networking system according to claim 1, characterized in that: The unbalanced Hungarian algorithm includes: when the number of devices is not equal to the target number, using a maximum value to expand the matrix, and selecting row reduction or column reduction according to the relationship between the number of devices and the target number.

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