Cooperative attack threat calculation and task allocation method for laser anti-unmanned aerial vehicle system

Abstract

The invention discloses a cooperative attack threat calculation and task allocation method for a laser anti-unmanned aerial vehicle system, and belongs to the technical field of laser anti-unmanned aerial vehicle, and the method comprises the following steps: threat-oriented cooperative attack task allocation: firstly, calculating the threat degree of an unmanned aerial vehicle to a defense important place; secondly, ranking according to threat degrees, and determining a striking sequence of striking targets; thirdly, distributing an attack task; according to the cooperative attack threat calculation and task allocation method for the laser anti-unmanned aerial vehicle system, an unmanned aerial vehicle threat calculation method and an attack task allocation strategy in a networking state can be provided for multi-laser cooperative defense, an overall attack effect is formed, and the purposes of fully utilizing attack resources and improving the overall protection capability of the system are achieved; the method is especially suitable for low-altitude cooperative defense of multiple important places and large areas.

Description

Technical Field

[0001] The present invention relates to a laser anti-UAV system coordinated attack threat calculation and task allocation method, belonging to the technical field of laser anti-UAV. Background Art

[0002] The rapid development of drone technology has led to its widespread commercial application, not only in the civilian sector but also in modern warfare, where drones are increasingly used for reconnaissance and strike operations. Given the limited destructive power of a single drone, leveraging the numerical advantage of multiple drones to simultaneously attack key targets from multiple directions is becoming a highly effective tactic for sabotage operations using drones in an increasing number of scenarios. This is to deplete strike resources, effectively penetrate restricted areas, and successfully damage targets.

[0003] Laser counter-drone (UAV) systems are an effective means of dealing with low-altitude threats. They offer unparalleled operational advantages over other means, such as precise strikes, instantaneous destruction, high tracking accuracy, continuous beam output, and low cost. These advantages provide an effective means and favorable conditions for countering drone swarm attacks. Traditionally, laser counter-drone systems have designed key locations to be protected within a radius centered on the laser's location and within the laser's effective strike range. The laser is responsible for low-altitude defense within this radius. With the increasing application of swarm drone strike tactics, relying on a single laser to counter swarm drone attacks is increasingly limited by current laser strike capabilities, resulting in a decrease in defensive effectiveness. To address these combat scenarios, a coordinated defense operation model involving multiple laser counter-drone systems is being developed to fully utilize combat resources and improve strike efficiency. This is one effective strategy for protecting key military and political locations over a larger area.

[0004] Multi-laser coordinated defense mode differs significantly from single-laser defense mode, involving conflict resolution regarding the occupation and release, allocation, and optimization of strike resources. Therefore, this invention provides a threat-oriented laser counter-UAV coordinated strike task allocation method. This method provides a networked drone threat calculation method and strike task allocation strategy for multi-laser coordinated defense, achieving a comprehensive strike effect. This approach is particularly suitable for low-altitude coordinated defense across multiple key locations and large areas. Summary of the Invention

[0005] To solve the above problems, the present invention proposes a laser anti-UAV system coordinated strike threat calculation and task allocation method, which is used in the combat background of multi-system coordinated operations to fully utilize strike resources and improve the overall protection capability of the system.

[0006] The laser anti-UAV system coordinated strike threat calculation and task allocation method of the present invention includes the following steps: threat-oriented coordinated strike task allocation, firstly, calculating the threat level of UAVs to key defense locations; secondly, sorting the targets by threat level and determining the strike order; and thirdly, allocating strike tasks; The method for calculating the threat level of drones to key defense locations comprises the following steps: Let K j Indicates the center of the key point. When calculating the threat posed by drones to the key point, the coordinates of the center are used as the reference. The threat factors of each drone to the key point are considered and the threat value of each threat factor is calculated. The overall threat value is then calculated. The threat factors include distance factor, heading factor and speed factor. The method for determining the order of attacking targets based on threat levels comprises the following steps: (1) Establish a threat matrix; Calculate the threat value of each drone target to each key location and form an m×n threat matrix; (2) Establish a strike capability matrix; Establish a strike capability function, calculate the strike capability values of all lasers against each target, and obtain a strike capability matrix; thereby, select the target to be struck first from multiple targets, or select the best laser from multiple lasers to complete the strike; The assignment of strike tasks includes task assignment and laser strike; the laser strike updates the laser occupancy state matrix and the target strike state matrix according to the specific circumstances of the strike, and is read and used by the task assignment process.

[0007] Furthermore, the method for calculating the threat level of drones to key defense locations specifically includes the following steps: The first step is to calculate the threat value of each threat factor; (1) Calculation of distance factor threat value; The closer the drone is to a key location, the greater the threat it poses to the location, and this threat is quantitatively characterized using an elliptical calculation model of the distance factor. (2) Calculation of heading threat value; When the drone's heading is toward the center of the key area, it is considered to have a clear and strong attack intention, and its threat level reaches the maximum. When the drone's heading deviates from or even moves away from the center of the key area, it is judged to have a weak or even no attack intention, and its threat level becomes smaller, or even negative. (3) Calculation of speed factor threat value; UAVs with high speeds pose a greater threat to key locations, while those with low speeds pose a smaller threat to key locations. The inverse tangent function is used as a quantitative calculation model for the threat value of the speed factor. The second step is to calculate the overall threat value; For an incoming UAV target, its comprehensive threat function to a certain key location is: (1); The above formula indicates that at each moment, the threat function of a target is the average value of the threat values generated by the distance factor, direction factor, and speed factor, which is [0, 1].

[0008] Furthermore, the distance factor threat value is calculated as follows: an elliptical calculation model of the distance factor is designed, and the expression of the elliptical model is: (2); Where x represents the UAV target U detected by the radar k To the center of K i The distance, y represents the threat value corresponding to the distance factor; Since in this system, the distance interval between a certain UAV and a key point is within [0,2R], which is twice the radar detection radius R, when calculating the distance factor threat value, R a Take twice the radar detection radius as 2R, R b Take 1, so: 1) UAV target U k Distance from the center of the city i When the distance is greater than 2R, the threat value is 0; 2) UAV target U k K from the center of the area i When the distance is in the interval [0, 2R], let the distance be d. The threat value is calculated as follows: (3); The specific calculation of distance d is as follows: Assume that in the radar coordinate system, the UAV target U k The coordinates of (x u ,y u ), key center K i The coordinates of (x k ,y k ), then the distance between the two is: (4).

[0009] Furthermore, the calculation method of the heading factor threat value is as follows: Assume that the UAV target U at time t k With the key center K i The coordinates of are (x u ,y u ), (x k ,y k ); First, determine the quadrant of the drone relative to the center coordinate system of the key point based on the coordinate relationship between the two points; When x u -x k >0,y u -y k When it is >0, it is in quadrant I; When x u -x k <0,y u -y k When it is >0, it is in quadrant II; When x u -x k <0,y u -y k When <0, it is in quadrant III; When x u -x k >0,y u -y k When <0, it is in quadrant IV; Then calculate the UAV target U at time t when the UAV is in a certain quadrant in the central coordinate system of a certain location. k To the center of the key point K i The heading factor threats are: (5); Among them, the UAV target U k With the key center K i The midpoint of the line between the two is the center of the circle, with the drone target U k With the key center K i Draw a circle with half the distance between them as the radius and intersect with the target heading straight line. The intersection point P is the intersection point of the key point K. i The distance between them is d2; R represents the radar detection radius.

[0010] Furthermore, the speed factor threat value is calculated as follows: The inverse tangent function is used as a quantitative calculation model for the speed factor threat value; the inverse tangent function expression is: , the extreme value of y is , and finally set the threat value of each factor to [0,1], normalize the above formula, and get: (6); Then multiply the speed x so that the speed range of the drone can fall within The value changes significantly between [0,20]; the final expression is: (7).

[0011] Furthermore, the specific method of determining the order of attacking targets by ranking them according to threat levels includes the following steps: The first step is to build a threat matrix; Assume that the overall system deploys a total of p sets of laser strike systems to jointly defend n key locations. At time t, the radar detects m drones. According to formula (1), the threat value of each drone target to each key location is calculated to form an m×n threat matrix: (8); The second step is to establish a strike capability matrix; First, establish the strike capability function, which is expressed as follows: (9); in: ; (10); In the above formula, d min Indicates the minimum striking distance, d opt Indicates the target striking distance, d max Indicates the maximum striking distance; The specific calculation of distance x in formula (9) is: Assume that in the laser coordinate system, the UAV target U k The coordinates of (x u ,y u ), laser center O i The coordinates of (x o ,y o ), then the distance between the two is: (11); Substituting equation (11) into equation (9), we can obtain the strike capability value a of a laser against a UAV target. For p lasers, we calculate the strike capability values of all lasers against each target and obtain the strike capability matrix: (12).

[0012] Furthermore, the laser occupancy state matrix is expressed as: (13); in, When the element in is 1, it means that the laser is in the occupied state and cannot be assigned tasks; When the element in is 0, it means that the laser is in idle state and can be assigned tasks.

[0013] Furthermore, the target strike state matrix is expressed as: (14); Among them, the elements When it is 1, it means the target is being attacked. When it is 0, it means the target is not being attacked or the attack fails and the target still exists. When it is -1, it means the target has been shot down.

[0014] Furthermore, during the process of assigning strike tasks, the target traversal state matrix is used as a judgment condition for ending task assignment; the target traversal state matrix is expressed as: (15); in, When the element is 1, it means that the target has been traversed, and when it is 0, it means that the target has not been traversed. This is used as the judgment condition for ending task allocation.

[0015] Compared with the existing technology, the laser anti-UAV system collaborative strike threat calculation and task allocation method of the present invention can provide a UAV threat calculation method and strike task allocation strategy in a networked state for multi-laser collaborative defense, forming an overall strike effect, which is particularly suitable for low-altitude collaborative defense of multiple key locations and large areas; as long as a threat to a certain key location is found to need to be dealt with immediately, regardless of whether the target poses a threat to the key locations within the defense range of this laser, as long as the laser can strike, it can accept task allocation, eliminate the threat to the target, and achieve the purpose of fully utilizing strike resources and improving the overall protection capability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is an example diagram of the system deployment of the present invention and an incoming target detected by radar.

[0017] Figure 2 Schematic diagram of the distance factor threat value calculation model of the present invention.

[0018] Figure 3 This is a schematic diagram of the calculation of the heading factor threat value of the present invention when the target is in the first quadrant of the center of a certain key location.

[0019] Figure 4 This is a schematic diagram of the calculation of the heading factor threat value of the present invention when the target is in the second quadrant of the center of a certain important location.

[0020] Figure 5 This is a schematic diagram of the calculation of the heading factor threat value when the target is in the third quadrant of the center of a certain important location in the present invention.

[0021] Figure 6 This is a schematic diagram of the calculation of the heading factor threat value when the target is in the IV quadrant of the center of a certain important location in the present invention.

[0022] Figure 7 This is an example diagram of the speed factor threat value calculation model before normalization of the present invention.

[0023] Figure 8 This is an example diagram of the normalized speed factor threat value calculation model of the present invention.

[0024] Figure 9 Schematic diagram of the striking distance between the laser center of the present invention and the UAV target to be struck.

[0025] Figure 10 The figure is a diagram showing the relationship between the laser striking capability and distance of the present invention.

[0026] Figure 11 A flowchart of the method for task allocation of the present invention. DETAILED DESCRIPTION

[0027] Based on radar detection information, the present invention provides a threat calculation method and strike allocation method for multiple simultaneously deployed laser anti-UAV systems to coordinately respond to UAV swarm attacks. The method is particularly suitable for the combat context of multi-system coordinated operations, breaking away from the traditional single-system field-oriented protection (i.e., the laser is only responsible for the protection of key points within the laser's strike range) and expanding to threat-oriented key point protection, expanding from single-system independent operations to multi-system coordinated operations. As long as a threat to a certain key point is found to need immediate disposal, regardless of whether the target poses a threat to the key points within the laser's defense range, as long as the laser can strike it, it can accept task allocation to eliminate the threat to the target, thereby fully utilizing strike resources and improving the overall protection capability of the system. The prerequisites for this calculation method to be applicable are: (1) Able to obtain UAV target information within a 360° range of the defense area through radar (excluding radar detection blind spots or targets that are not detected due to obstruction), and at least provide the target's current coordinate information, heading angle information, and speed information; (2) With the radar deployment point as the coordinate origin, the north direction is the positive direction of the Y axis, the east direction is the positive direction of the X axis, the Y axis direction is the starting point of the heading angle, the clockwise direction is positive, and the heading angle range is 0°~360°; (3) The X-axis and Y-axis of the laser coordinate system and the drone coordinate system at a certain time t are parallel to the X-axis and Y-axis of the radar coordinate system and have the same direction. The simple coordinate transformation relationship between the coordinate systems is not discussed in this invention, and the conversion result of the coordinate translation is directly applied; (4) Networking and operation between detection radar and laser anti-UAV systems.

[0028] Specifically, the present invention discloses a method for calculating and allocating coordinated strike threats in a laser anti-UAV system, which includes the following steps: first, calculating the threat level of UAVs to key defense locations; second, determining the order of attacking targets based on the threat level; and third, allocating attack tasks; like Figure 1 As shown, one radar is deployed, and the radar deployment site is used as the coordinate origin. Due north and due east are the positive directions of the Y axis and the positive direction of the X axis, respectively. The effective detection radius of the radar is R, which is larger than the strike range r of the laser anti-UAV system. The coordinate systems of each laser and UAV target are horizontal or vertical translations of the radar coordinate system to facilitate the discussion of subsequent issues. Deploy multiple laser anti-UAV systems within the radar detection range. i Each laser anti-UAV system has its own effective attack range. i ; Within the effective attack range of each laser anti-UAV system, there are one or more key defense points K j ; Figure 1 The example in the figure shows that within the radar detection range, N drone targets U to be attacked are detected. k , each UAV target U k Have their own coordinate information, heading and speed, etc. It should be noted that the radar coordinate origin only represents the deployment coordinates of the radar, not the protection center of the key point; the laser coordinate origin also only represents the deployment coordinates of the laser, not the protection center of the key point; this situation is consistent with the actual deployment scenario of the system and is universal; the target U k In actual application, the target batch number is used for unique identification, and the trajectory data with the same batch number in the continuous state are the trajectory data of the same drone.

[0029] The method for calculating the threat level of drones to key defense locations comprises the following steps: Let K j Indicates the center of the key point. When calculating the threat posed by drones to the key point, the coordinates of the center are used as the reference. The threat factors of each drone to the key point are considered and the threat value of each threat factor is calculated. The overall threat value is then calculated. The threat factors include distance factor, heading factor and speed factor. The first step is to calculate the threat value of each threat factor; (1) Calculation of distance factor threat value; It is generally believed that the closer a drone is to a key location, the greater the threat it poses. To quantitatively characterize this factor, an elliptical calculation model for the distance factor is first designed: The expression of the ellipse model is: (1); Where x represents the UAV target U detected by the radar k To the center of K i The distance, y represents the threat value corresponding to the distance factor; Take the first quadrant of the ellipse model, such as Figure 2 As shown, the horizontal axis X represents the UAV target U detected by the radar k To the center of K i The vertical axis Y represents the threat value corresponding to the distance factor. Since in this system, the distance interval between a certain UAV and a key point is within [0,2R], which is twice the radar detection radius R, when calculating the distance factor threat value, R a Take twice the radar detection radius as 2R, R b Take 1, so: 1) UAV target U k K from the center of the area i When the distance is greater than 2R, the threat value is 0; 2) UAV target U k K from the center of the area i When the distance is in the interval [0, 2R], let the distance be d. The threat value is calculated as follows: (2); The specific calculation of the distance d is as follows: Assume that in the radar coordinate system, the UAV target U k The coordinates of (x u ,y u ), key center K i The coordinates of (x k ,y k ), then the distance between the two is: (3); The ellipse model selected for the distance factor is based on the following considerations: First, the model has R a 、R b The two boundaries correspond exactly to the detection range of radar detection and the threat value has a limit value of 1; Secondly, the curve is convex upward, which helps to strengthen the characteristics of the target's attack and express the degree of threat to the center of the defense zone. The closer the distance, the more likely there is intention to attack, and the higher the threat value. Third, when the target approaches from far away, the radar starts to monitor and evaluate the target from the moment it is detected. Although the threat value is small at a long distance, its rate of change is drastic, which can be used to enhance the early warning feature when necessary; (2) Calculation of heading threat value; The heading factor refers to the threat impact of the drone's flight direction on the key point. Under normal circumstances, when the drone's heading is toward the center of the key point, it can be considered to have a clear and strong attack intention, and its threat level reaches the maximum. When the drone's heading deviates from or even moves away from the center of the key point, it can be judged to have a weaker or even no attack intention, and its threat level becomes smaller, or even negative. In this way, when a drone is relative to the center of a key point, considering the distance factor and the heading factor, some targets that appear to be threatening but are actually not threatening can be filtered out, such as some targets that are close but are flying away from the key point. In this way, limited attack resources can be freed up in the task allocation stage to deal with targets with relatively greater threat levels, thereby improving the overall defense efficiency of the system and ensuring the overall system defense effect. Assume that the UAV target U at time t k With the key center K i The coordinates of are (x u ,y u ), (x k ,y k ), First, determine the quadrant of the drone relative to the center coordinate system of the key point based on the coordinate relationship between the two points; When x u -x k >0,y u -y k When it is >0, it is in quadrant I; When x u -x k <0,y u -y k When it is >0, it is in quadrant II; When x u -x k <0,y u -y k When <0, it is in quadrant III; When x u -x k >0,y u -y k When <0, it is in quadrant IV; 1) When in Quadrant I: like Figure 3 As shown in the figure, when the UAV is in quadrant I of the coordinate system of the center of a certain key point, when the heading angle θ is between [180°, 270°], the heading factor takes a positive value for the threat to the key point, and other heading angles take negative values. According to the principle of maximum threat, θ also takes positive values at 180° and 270°. When θ is outside [180°, 270°], it indicates that the target is far away from the center of the key point, and the threat takes a negative value. Assume that the UAV target U at time t k With the key center Ki The positional relationship between Figure 3 , from the calculation of the distance factor threat value, we can know that the UAV target U k With the key center K i The distance d between them has been calculated. Suppose the radar detects the UAV target U k The heading angle is θ; the UAV target U k With the key center K i The midpoint of the line connecting the two is the center of the circle. If we construct a circle with radius , then the circle must pass through point U k and point K i ; The straight line representing the target heading at this time intersects the circle at point P, connecting K i and P, then according to the characteristics of the circle, △U k K i P is a right triangle; Assume that the drone target U k With the key center K i The angle between the connecting line and the X coordinate of the key point is β, ∠K i U k P=α, then according to the angle relationship, we have: (4); in, , θ is known from radar detection, so: (5); According to the relationship between the sides and angles of a right triangle, we can get: (6); For the whole system, the value range of d2 is also within [0,2R], so U k K i The heading factor threat is also calculated using an elliptical model: (7); Combined with the judgment of whether the angle θ is in the interval [180°, 270°], the drone target U at time t is obtained k To the center of the key point K i The heading factor threats are: (8); 2) When in Quadrant II: like Figure 4 As shown in the figure, when the UAV is in quadrant II in the coordinate system of the center of a certain key point, the heading angle is [90°, 180°]. At this time, the threat of the direction factor to the key point is positive, and other heading angles are negative. According to the principle of maximum threat, the two positions of 90° and 180° also take positive values. Assume that the drone target Uk With the key center K i The angle between the connecting line and the negative X-axis of the key point is β, ∠K i U k P=α, then according to the angle relationship, we have: (9); in, , θ is known from radar detection, so: (10); According to the relationship between the sides and angles of a right triangle, we can get: (11); For the whole system, the value range of d2 is also within [0,2R], so U k K i The heading factor threat is also calculated using an elliptical model: (12); Combined with the judgment of whether the angle θ is in the interval [90°, 180°], the drone target U at time t is obtained k To the center of the key point K i The heading factor threats are: (13); 3) When in Quadrant III: like Figure 5 As shown in the figure, when the UAV is in quadrant III in the coordinate system of the center of a certain key point, the heading angle is [0°, 90°]. At this time, the threat of the direction factor to the key point is positive, and other heading angles are negative. According to the principle of maximum threat, the two positions of 0° and 90° also take positive values. Assume that the drone target U k With the key center K i The angle between the connecting line and the negative X-axis of the key point is β, ∠K i U k P=α, then according to the angle relationship, we have: (14); in, , θ is known from radar detection, so: (15); According to the relationship between the sides and angles of a right triangle, we can get: (16); For the whole system, the value range of d2 is also within [0,2R], so U k K i The heading factor threat is also calculated using an elliptical model: (17); Combined with the judgment of whether the angle θ is in the interval [0°, 90°], the drone target U at time t is obtained k To the center of the key point K i The heading factor threats are: (18); 3) When in quadrant IV: like Figure 6 As shown in the figure, when the UAV is in quadrant IV in the coordinate system of the center of a certain key point, the heading angle is [270°, 360°]. At this time, the threat factor of the direction to the key point takes a positive value, and other heading angles take negative values. According to the principle of maximum threat, the two positions of 270° and 360° also take positive values. Assume that the drone target U k With the key center K i The angle between the connecting line and the negative X-axis of the key point is β, ∠K i U k P=α, then according to the angle relationship, we have: (19); in, , θ is known from radar detection, so: (20); According to the relationship between the sides and angles of a right triangle, we can get: (twenty one); For the whole system, the value range of d2 is also within [0,2R], so U k K i The heading factor threat is also calculated using an elliptical model: (twenty two); Combined with the judgment of whether the angle θ is in the interval [270°, 360°], the drone target U at time t is obtained k To the center of the key point K i The heading factor threats are: (twenty three); (3) Calculation of speed factor threat value: Radar detection can provide target speed. Based on this condition, the threat level of the target to the defense key point can be evaluated according to the speed. Generally speaking, UAVs with higher speeds pose a greater threat to the defense key point, while those with lower speeds pose a smaller threat. However, a method needs to be designed to quantitatively calculate the speed. This invention selects the inverse tangent function for specific design as the calculation model of the speed factor threat value. Let x represent the speed (m / s) and y represent the threat value, then the inverse tangent function is as follows Figure 7 As shown; The inverse tangent function expression is: , the extreme value of y is , and finally set the threat value of each factor to [0,1], normalize the above formula, and get: (twenty four); After normalization, the final speed is limited to [0,1] due to the mutual threat value, which is consistent with the normalization rules of other factors. The model curve is as follows Figure 8 As shown; By looking up the trigonometric function table or calculating, when the drone's flight speed x takes values of 5, 10, 20, 60, 80, and 100, the corresponding threat values The values are 0.8743, 0.9365, 0.9682, 0.9894, 0.9920, and 0.9936 respectively; it can be seen that when the speed x is 20, The value is close to the extreme value of 1. Considering that, except for military operations, most drones that threaten key areas are consumer drones, and the flight speed of such drones is mostly less than 20m / s, mostly around 10m / s, it just represents the speed factor threat within this range; However, from the above data analysis, when the drone speed is higher, The difference between the values is very small, and the difference in y' value from 5m / s to 100m / s is not very large; that is, when the speed of the drone is greater than 20m / s, the threat value represented by the speed change is no longer clearly distinguishable; when the speed is from 5m / s to 100m / s, the difference in y' value is not very large either. The difference in values is too small to differentiate the threats caused by speed factors; Therefore, the speed x is multiplied in the present invention so that the speed range of the UAV can fall within The value changes significantly between [0,20]; the final expression is: (25); After processing, when the UAV flight speed x takes values of 5, 10, 20, 60, 80, and 100, the corresponding threat values y3 are 0.5000, 0.7048, 0.8440, 0.9471, 0.9603, and 0.9682, respectively. Compared with before processing, the difference in threat values is greater, which can more effectively characterize the threat caused by speed factors, especially in the speed range of about 10m / s for general UAVs. This processing effect is more obvious. In the above formula, the independent variable , is a custom value; in actual application, other values can be selected according to the actual need to further increase the value difference, such as , The greater the compression ratio, the more the difference in threat level caused by speed difference can be highlighted; The second step is to calculate the overall threat value; For an incoming UAV target, its comprehensive threat function to a certain key location is: (26); The above formula indicates that at each moment, the threat function of a target is the average value of the threat values generated by the distance factor, direction factor, and speed factor. The value is [0, 1]. In actual application, the data presented to the commander can also be converted to a percentage system, so that its representation range is limited to [0, 100], which is more in line with people's common judgment habits.

[0030] The method for determining the order of attacking targets based on threat levels comprises the following steps: (1) Establish a threat matrix; Assume that the overall system has p sets of laser strike systems deployed to jointly defend n key locations. At time t, the radar detects m drones (such as Figure 1 In this example, 5 laser strike systems are deployed to defend 8 key locations, and the radar detects 6 incoming targets. The threat value of each drone target to each key location is calculated according to formula (2), forming an m×n threat matrix: (27); (2) Establish a strike capability matrix; like Figure 9 As shown in the figure, for an actual laser strike system, when the target distance is too far, the laser energy loss is too large and the strike effect is not ideal; when the target distance is too close, the laser pitch angle reaches the maximum, resulting in the inability to strike the target at too close a distance; therefore, for a general laser strike system, its strike capability is within a certain distance range; for example, the nominal strike distance d opt =1000m laser, its actual attack range may be between 100m-1200m, indicating the maximum target distance d max =1200m, it is possible to strike, but it may take a long time to shoot down a drone; if the target distance is less than d min =100m, due to the limitation of the pitch mechanism, it is impossible to continue tracking the target and striking it; therefore, the optimal striking distance is between 100m-1000m; Therefore, in order to quantitatively characterize this capability and provide support for subsequent coordinated strike task allocation, while also considering computational convenience, such as Figure 10As shown, the present invention uses a piecewise function to characterize and distinguish the strike capability function of a certain laser against a certain UAV target. The piecewise function expression is: (28); in: (29); (30); In the above formula, d min Indicates the minimum striking distance, d opt Indicates the target striking distance, d max Indicates the maximum striking distance; d in the above formula min d opt and d max , set according to the specific conditions of each laser, such as 200m, 1000m, 1200m, etc.; 0.8 is a custom value, which is selected based on experience or specific needs. This value will be related to the impact of distance on the strike effect. If you want to emphasize the greater impact of distance on the strike effect, select a smaller value, otherwise select a larger value; The specific calculation of distance x in formula (28) is: Assume that in the laser coordinate system, the UAV target U k The coordinates of (x u ,y u ), laser center O i The coordinates of (x o ,y o ), then the distance between the two is: (31); Substituting (31) into (28), we can obtain the strike capability value a of a laser against a UAV target. For p lasers, we calculate the strike capability values of all lasers against each target and obtain the strike capability matrix: (32); Usually, the strike capability matrix is a sparse matrix with most elements being 0; if an element a pn If the value is within (0, 1], it means that the target n is within the attack range of the laser p, and the size of this value represents the ability of the laser to strike the target. The larger the value, the better the attack effect under the same conditions. Therefore, the target to be attacked first can be selected from multiple targets, or the best laser can be selected from multiple lasers to complete the attack. The assignment of strike tasks includes task assignment and laser strikes; the laser strikes update the laser occupancy state matrix and the target strike state matrix according to the specific circumstances of the strikes, and are read and used by the task assignment process; The laser occupation state matrix specifically refers to the occupation and release states of the laser, which is expressed as: (33); Where, When the element in is 1, it means that the laser is in the occupied state and cannot be assigned tasks; When the element in is 0, it means that the laser is in idle state and can be assigned tasks. In actual application, for safety reasons, the laser operator will manually start the laser light strike and determine whether the strike is completed based on the strike effect. Therefore, when the operator operates, the occupation and release status of the laser can be updated in the software. The target strike status matrix refers to whether the current target is being struck, which is expressed as: (34); In the formula, the element When it is 1, it means the target is being attacked; when it is 0, it means the target is not being attacked or the attack fails and the target still exists; when it is -1, it means the target has been shot down; In the process of assigning strike missions, the target traversal state matrix is used as the judgment condition for ending the mission assignment; the target traversal state matrix refers to the state in which all detected drone targets have been traversed during the mission assignment process; the target traversal state matrix is expressed as: (35); Where, When the element in is 1, it means that the target has been traversed, and when it is 0, it means that the target has not been traversed. This is used as the judgment condition for ending task allocation.

[0031] Example 1: For ease of understanding, the present invention uses a specific example method to illustrate the method flow of task allocation, for example Figure 1 The example includes 5 lasers, 6 incoming drones, and 8 defensive locations; The task allocation process of the present invention includes two process lines, one is the task allocation process and the other is the laser striking process, which are represented by steps (•) and [•] respectively; the laser striking process mainly updates the laser occupancy state matrix L and the target striking matrix H according to the specific situation of the strike, and is read and used by the task allocation process; specifically, Figure 11 As shown, the task allocation process is as follows: (1) Start: The task allocation process begins; (2) Initialize the laser occupancy state matrix L: Initialize the laser occupancy state matrix, such as: L = [0 0 0 0 0], which means that at the initial moment, the lasers are not occupied and no strike mission is assigned; (3) Radar detection information at time t: At a certain time t, the target detection information provided by the radar is obtained, focusing on the target position coordinates, target heading angle, and target flight speed; (4) Initialize / read the target strike matrix H: If the task allocation process is just started, the target attack matrix is initialized according to the obtained target information, such as: H = [0 0 0 0 0 0 0], which means that at the initial moment, no target has been attacked and is in a pending state; If the task allocation process is in progress and has been looping detection, the dimensions of the target strike matrix may change due to the appearance and disappearance of targets. At this time, the updated status in the laser strike process must be read and the target strike matrix must be regenerated based on the target batch number. The existing target batch numbers will retain the status of the previous moment, and all new targets will be initialized to 0. The disappeared targets will be removed from the matrix. (5) Calculate and generate threat matrix T: Assume that the threat matrix T (example) corresponding to the drone and defense key points obtained by the threat calculation algorithm is as follows: ; A negative threat indicates that the target poses no threat to the key location and is not considered. Therefore, the above formula is simplified to: ; In the threat matrix T, the row elements represent drones U k The threat size to all key locations (negative number is 0), the column elements represent the threat size of all current drones to key location K i the size of the threat; (6) Calculate and generate the strike capability matrix A: Assume that the capability matrix corresponding to the laser and the UAV obtained by the strike capability algorithm is: ; In the strike capability matrix A, the row elements represent the laser O j The target that can be attacked is represented by the numerical value; the column elements represent the target that can be attacked by the UAV U k The lasers used to carry out the strike, with the strike capability of each laser represented by a numerical value; Specifically, looking at the rows, for example, the two non-zero data of 0.8 and 0.9 in row 1 indicate that laser 1 can strike UAV 1 and UAV 5, and the effect of striking UAV 5 is better than that of striking UAV 1. Looking at the columns, if the data in column 3 is all zero, it means that UAV 3 has no laser that can attack it and UAV 3 is outside the attack range of all lasers. Column 4 shows that UAV 4 can be attacked by both Laser 4 and Laser 5, with Laser 4 being the preferred attack because UAV 4 is closer to Laser 4. (7) Initialize the target traversal state matrix S: Initialize the laser traversal state matrix: S = [0 0 0 0 0 0 0], where a column element of S is 0, indicating that the target has not been traversed yet, and a column element of S is 1, indicating that the target has been traversed. A traversed target will no longer participate in task allocation within the same time period. (8) Sort the elements in matrix T by size: According to the threat matrix T, the threats to the target are sorted from large to small, such as: (U2, K5) = 0.9; (U1, K1) = 0.8; (U3, K6) = 0.7; (U4, K8) = 0.7; (U4, K4) = 0.6; (U6, K7) = 0.6; (U4, K6) = 0.6; (U3, K8) = 0.6; … (9) Find the maximum threat value from T by sorting: Find the current maximum threat value, such as (U2, K5) = 0.9; (10) Determine the target based on the maximum threat value: According to the maximum threat value of the key point (U2, K5) = 0.9, it is determined that the target that poses the greatest threat to key point K5 is U2, so U2 is the target to be attacked; (11) Determine whether the element corresponding to the target in H is 1 or -1: After determining that U2 is the target, first check whether the element corresponding to U2 in H is 1 or -1, that is, whether it is in the state of being attacked or has been shot down: If yes: it means the target is being hit or has been shot down, then go to step (12); If not: the target can be attacked, go to step (13); (12) The corresponding elements in the T matrix are set to 0: Set the element in the T matrix to 0, exit the sorting, and go to step (8), and continue to find the matrix element with the largest threat value according to the sorting, such as (U1, K1) = 0.8 in step (8); (13) Find the column corresponding to the target from A: For example, the column corresponding to target U2 in A is found to be column 2; (14) Are all the elements in this column 0? For example, determine whether the data in the second column of A are all 0: If yes, then it means that there is no laser that can strike target U2. Although the key point is under the greatest threat at this time, the target cannot be dealt with due to the limitation of attack resources. Therefore, go to step (12). If not: it means that there is a laser that can strike target U2, and continue with step (15); (15) Find the maximum value of the column element, which is the optimal laser to hit the target: For example, according to the second column of the capability matrix A, the laser that can strike U2 is O2. In this example, there is only one laser O2 that can strike U2, and its strike capability is 0.7. (16) Determine whether the laser is occupied based on the matrix L: Query the column element corresponding to the occupancy state matrix L. If it is 0, it is not occupied, and go to step (18); if it is 1, it means that the laser is performing a task and is occupied, and go to step (17); (17) Determine whether there are other optional lasers in the same column in A: In the same column of the strike capability matrix A, determine whether there are other optional lasers, that is, whether there are other non-zero elements in the same column: If it exists, find the largest data and go to step (16); If it does not exist, it means that all lasers capable of hitting the target are occupied and there are no other available attack resources. In this case, the target cannot be hit, and the allocation ends, and the process goes to step (12). (18) Assignment of strike missions: For example, assign the strike task to laser O2, send the current coordinate information of U2 to the laser command system, and guide the laser to strike U2; the laser strike process starts and enters step [1]; (19) The corresponding elements in the T matrix are set to 0: Set the element in the T matrix to 0 and exit sorting; (20) Set the laser occupancy state in the occupancy state matrix L to 1: For example, set the element corresponding to laser O2 in L to 1, indicating that the laser is performing a task and is occupied: L = [0 1 0 0 0]; (21) Set the target state in the target attack matrix H to 1: For example, set the element corresponding to target U2 in H to 1, indicating that the target is being hit and no laser will be assigned to hit it in the future: H=[(0 1 0 0 0 0 0)]; (22) Traverse the corresponding column elements of matrix S and set them to 1: For example, set the element corresponding to laser O2 in S to 1, indicating that the laser has completed the traversal in this time period: S = [ 0 1 0 0 0 ]; (23) Determine whether all elements in S are 1: If yes: it means that within this time period, all lasers have successfully assigned tasks and there are no unsuccessful tasks, but all have been traversed, so enter the next time period and go to step (22); If not: it indicates that there are lasers that have not been traversed within the time period, and the process goes to step (8) to proceed to the next cycle and continue to allocate the strike tasks that can be allocated; (24) t = t + 1: Let the radar data at time t+1 be the current data, go to step (3), and enter a new cycle; (25) End: The task assignment process ends.

[0032] Laser striking process: [1] Laser strike: After a task is assigned to a certain laser in the task assignment process, the target information is forwarded to the laser strike system, which guides the laser strike system to search, track, aim at the target, and carry out the strike; [2] Update the laser occupancy state matrix L: Laser strikes usually take several seconds to tens of seconds. During this period, the laser cannot accept new strike tasks. Regardless of whether the strike is successful or failed, the operator must finally judge and confirm it, and then update the corresponding laser occupancy status in the laser occupancy status matrix L for reading and use in the task allocation process. [3] Update the target attack matrix H: After the laser is shot down or fails to strike, the corresponding target strike status in the target strike matrix H is updated after personnel judgment and confirmation, and is read and used in the task allocation process; the target that is successfully shot down will no longer appear in the next time period; if it is not successfully shot down and the target is still under radar detection, the target status will be updated and it will continue to participate in task allocation in the next time period according to its threat level to various key locations.

[0033] The laser anti-UAV system collaborative strike threat calculation and task allocation method of the present invention can provide a UAV threat calculation method and strike task allocation strategy in a networked state for multi-laser collaborative defense, forming an overall strike effect, and is particularly suitable for low-altitude collaborative defense of multiple key locations and large areas.

[0034] The above embodiments are only preferred implementations of the present invention. Therefore, any equivalent changes or modifications made according to the structures, features and principles described in the scope of the patent application of the present invention are included in the scope of the patent application of the present invention.

Claims

1. A laser anti-UAV system coordinated attack threat calculation and task allocation method, characterized in that: The following steps are involved: The threat-oriented coordinated strike mission allocation involves first calculating the threat level of drones to key defense locations; secondly, ranking the targets by threat level to determine the order of attack; and thirdly, assigning strike missions. The method for calculating the threat level of drones to key defense locations comprises the following steps: Let K j Indicates the center of the key point. When calculating the threat posed by drones to the key point, the coordinates of the center are used as the reference. The threat factors of each drone to the key point are considered and the threat value of each threat factor is calculated. The overall threat value is then calculated. The threat factors include distance factor, heading factor and speed factor. The method for determining the order of attacking targets based on threat levels comprises the following steps: (1) Establish a threat matrix; Calculate the threat value of each drone target to each key location and form an m×n threat matrix; (2) Establish a strike capability matrix; Establish a strike capability function, calculate the strike capability values of all lasers against each target, and obtain a strike capability matrix; thereby, select the target to be struck first from multiple targets, or select the best laser from multiple lasers to complete the strike; The assignment of strike tasks includes task assignment and laser strike; the laser strike updates the laser occupancy state matrix and the target strike state matrix according to the specific circumstances of the strike, and is read and used by the task assignment process.

2. The method for collaborative attack threat calculation and task allocation of a laser anti-UAV system according to claim 1 is characterized in that: The method for calculating the threat level of the drone to the defense key point specifically includes the following steps: The first step is to calculate the threat value of each threat factor; (1) Calculation of distance factor threat value; The closer the drone is to a key location, the greater the threat it poses to the location, and this threat is quantitatively characterized using an elliptical calculation model of the distance factor. (2) Calculation of heading threat value; When the drone's heading is toward the center of the key area, it is considered to have a clear and strong attack intention, and its threat level reaches the maximum. When the drone's heading deviates from or even moves away from the center of the key area, it is judged to have a weak or even no attack intention, and its threat level becomes smaller, or even negative. (3) Calculation of speed factor threat value; UAVs with high speeds pose a greater threat to key locations, while those with low speeds pose a smaller threat to key locations. The inverse tangent function is used as a quantitative calculation model for the threat value of the speed factor. The second step is to calculate the overall threat value; For an incoming UAV target, its comprehensive threat function to a certain key location is: （1）； The above formula indicates that at each moment, the threat function of a target is the average value of the threat values generated by the distance factor, direction factor, and speed factor, which is [0, 1].

3. The method for collaborative attack threat calculation and task allocation of a laser anti-UAV system according to claim 2 is characterized in that: The calculation method of the distance factor threat value is as follows: an elliptical calculation model of the distance factor is designed, and the expression of the elliptical model is: （2）； Where x represents the UAV target U detected by the radar k To the center of K i The distance, y represents the threat value corresponding to the distance factor; Since in this system, the distance interval between a certain UAV and a key point is within [0,2R], which is twice the radar detection radius R, when calculating the distance factor threat value, R a Take twice the radar detection radius as 2R, R b Take 1, so: 1) UAV target U k K from the center of the area i When the distance is greater than 2R, the threat value is 0; 2) UAV target U k K from the center of the area i When the distance is in the interval [0, 2R], let the distance be d. The threat value is calculated as follows: （3）； The specific calculation of distance d is as follows: Assume that in the radar coordinate system, the UAV target U k The coordinates of (x u ,y u ), key center K i The coordinates of (x k ,y k ), then the distance between the two is: （4）。 4. The method for collaborative attack threat calculation and task allocation of a laser anti-UAV system according to claim 2, characterized in that: The calculation method of the heading factor threat value is as follows: Assume that the UAV target U at time t k With the key center K i The coordinates of are (x u ,y u ), (x k ,y k ); First, determine the quadrant of the drone relative to the center coordinate system of the key point based on the coordinate relationship between the two points; When x u -x k >0,y u -y k When it is >0, it is in quadrant I; When x u -x k <0,y u -y k When it is >0, it is in quadrant II; When x u -x k <0,y u -y k When <0, it is in quadrant III; When x u -x k >0,y u -y k When <0, it is in quadrant IV; Then calculate the UAV target U at time t when the UAV is in a certain quadrant in the central coordinate system of a certain location. k To the center of the key point K i The heading factor threats are: （5）； Among them, the UAV target U k With the key center K i The midpoint of the line between the two is the center of the circle, with the drone target U k With the key center K i Draw a circle with half the distance between them as the radius and intersect with the target heading straight line. The intersection point P is the intersection point of the key point K. i The distance between them is d2; R represents the radar detection radius.

5. The method for collaborative attack threat calculation and task allocation of a laser anti-UAV system according to claim 2, characterized in that: The speed factor threat value is calculated as follows: The inverse tangent function is used as a quantitative calculation model for the speed factor threat value; the inverse tangent function expression is: , the extreme value of y is , and finally set the threat value of each factor to [0,1], normalize the above formula, and get: （6）； Then multiply the speed x so that the speed range of the drone can fall within The value changes significantly between [0,20]; the final expression is: （7）。 6. The method for collaborative attack threat calculation and task allocation of a laser anti-UAV system according to claim 1, characterized in that: The specific method for determining the order of attacking targets by ranking them according to threat levels includes the following steps: The first step is to build a threat matrix; Assume that the overall system deploys a total of p sets of laser strike systems to jointly defend n key locations. At time t, the radar detects m drones. According to formula (1), the threat value of each drone target to each key location is calculated to form an m×n threat matrix: （8）； The second step is to establish a strike capability matrix; First, establish the strike capability function, which is expressed as follows: （9）； in: ; （10）； In the above formula, d min Indicates the minimum striking distance, d opt Indicates the target striking distance, d max Indicates the maximum striking distance; the specific calculation of distance x in formula (9) is: Assume that in the laser coordinate system, the UAV target U k The coordinates of (x u ,y u ), laser center O i The coordinates of (x o ,y o ), then the distance between the two is: （11）； Substituting equation (11) into equation (9), we can obtain the strike capability value a of a laser against a UAV target. For p lasers, we calculate the strike capability values of all lasers against each target and obtain the strike capability matrix: （12）。 7. The method for collaborative attack threat calculation and task allocation of a laser anti-UAV system according to claim 1, characterized in that: The laser occupancy state matrix is expressed as: （13）； in, When the element in is 1, it means that the laser is in the occupied state and cannot be assigned tasks; When the element in is 0, it means that the laser is in idle state and can be assigned tasks.

8. The method for collaborative attack threat calculation and task allocation of a laser anti-UAV system according to claim 1, characterized in that: The target strike state matrix is expressed as: （14）； Among them, the elements When it is 1, it means the target is being attacked. When it is 0, it means the target is not being attacked or the attack fails and the target still exists. When it is -1, it means the target has been shot down.

9. The method for collaborative attack threat calculation and task allocation of a laser anti-UAV system according to claim 1, characterized in that: In the process of assigning strike tasks, the target traversal state matrix is used as the judgment condition for ending task assignment; the target traversal state matrix is expressed as: （15）； in, When the element in is 1, it means that the target has been traversed, and when it is 0, it means that the target has not been traversed. This is used as the judgment condition for ending task allocation.

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