Heterogeneous aircraft cluster collaborative hunting search method oriented to denial environment

Abstract

The invention relates to a heterogeneous aircraft cluster collaborative hunting search method oriented to a denial environment. The invention relates to the technical field of aircraft cluster control, and establishes a coordination mechanism among aircrafts based on a consensus initiative mechanism, so that the aircrafts influence the behaviors of each other to realize cooperation; in a denial environment, the aircraft reads and writes pheromones in a medium, so that indirect communication between the aircrafts is realized; and under a distributed search algorithm, carrying out fusion optimization on the pheromone map and a particle swarm algorithm to obtain an optimized search and hunting result. Aiming at the problems of limited communication, complex collaboration and the like existing in collaborative search and hunting of aircraft clusters in a denial environment, environment perception and collaborative control are fused and optimized, so that the search efficiency and hunting success rate are improved, and an effective distributed collaborative perception and decision-making mechanism is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft cluster control, and is a method for collaborative capture and search of heterogeneous aircraft clusters in a denial environment. Background Art

[0002] Traditional approaches to collaborative control of aircraft swarms primarily rely on centralized control architectures, requiring all information to be centralized at a central node for processing and decision-making. While this approach offers high control accuracy, practical applications suffer from communication bottlenecks, heavy computational burdens, and system fragility. While distributed control approaches can improve system robustness and flexibility, achieving effective coordination among aircraft swarms and optimizing task allocation remain key challenges.

[0003] In terms of communication technology in denied environments, aircraft swarms face challenges such as limited bandwidth, unstable links, and high latency due to electromagnetic interference, communication blockages, and other factors. Existing communication solutions often struggle to meet the real-time and reliability requirements of denied environments. This is especially true in scenarios where large-scale aircraft swarms collaborate, where ensuring the timely transmission and effective sharing of critical information becomes a pressing issue.

[0004] Existing research on collaborative target search technology for aircraft swarms has primarily focused on designing search algorithms for ideal environments, with insufficient consideration of the uncertainties and dynamics of denied environments. Traditional search methods struggle to effectively address situations involving highly maneuverable targets, incomplete environmental information, and limited perception capabilities. Furthermore, achieving collaborative search decisions for aircraft swarms under limited communication conditions, and balancing search efficiency with system robustness, remain key areas of research, along with significant challenges.

[0005] Therefore, it is urgent to propose a collaborative capture and search method for heterogeneous aircraft clusters that can effectively address the above challenges, so as to improve the system's operational capability and mission effectiveness in a denied environment. Summary of the Invention

[0006] In response to the problems of limited communication and complex collaboration in the collaborative search and roundup of aircraft clusters in a denied environment, the present invention provides a collaborative roundup search method for heterogeneous aircraft clusters in a denied environment. By integrating and optimizing environmental perception and collaborative control, the search efficiency and roundup success rate are improved, forming an effective distributed collaborative perception and decision-making mechanism.

[0007] The present invention provides the following technical solutions: A method for collaborative capture and search of heterogeneous aircraft clusters in a denial environment, comprising the following steps: Step 1: Initialize the position and velocity of the heterogeneous aircraft cluster and the target; Step 2: Entering the first stage: The heterogeneous aircraft cluster moves randomly, explores the search area, and uses the pheromone map construction algorithm to build the initial pheromone map; Step 3: When the upper limit of the iteration step of the first stage is reached, the second stage begins: the heterogeneous aircraft cluster uses the target search algorithm to update the motion state, and at the same time uses the pheromone map construction algorithm to build the pheromone map; when the upper limit of the iteration step of the second stage is reached, the second stage ends.

[0008] Preferably, the step 2 is specifically as follows: The target signal value detected by the aircraft is expressed by a three-dimensional function, namely: (1) In the formula, the function is a continuous function related to the position of the aircraft, signal Represents the target signal strength that the aircraft can detect at the coordinate (x, y, z); The blue aircraft moves from point A to point B along the blue dotted line, leaving a pheromone vector at point B. , expressed by the following formula: (2) Where, and Indicates the target signal values ​​detected by the blue aircraft at points A and B. and Represents the coordinates of point A and point B, Indicates the distance between point A and point B; use To represent the unit vector, that is, in formula (2) , formula (2) is rewritten as: (3) The target signal field is along the unit vector The directional derivative of the direction is According to the definition of directional derivative, we get: (4) Where, ρ =||Δ x ||2,Δ x is the difference in coordinates; when point A and point B are close, then: (5) Rewrite formula (4) as: (6) is the directional derivative of the target signal field at point B, that is: (7) From the relationship between directional derivatives and gradients, we can see that directional derivatives are the projections of gradients in that direction, namely: (8) Where, Represents the gradient of the signal field at point B. Combining the above formulas, we get: (9) The green aircraft moves along the green dotted line from point C to point B, leaving behind vector pheromones at point B. Written in the same form; if an aircraft moves to point B along the direction of the signal gradient, the vector pheromone it leaves at point B is equal to the signal gradient at that point; when multiple aircraft leave their own vector pheromones at B, the vector pheromones will be accumulated at point B, and the accumulated vector pheromone is expressed as , in formula (9) and In Cartesian coordinates it is: (10) (11) Where, and Represent the unit vectors of the horizontal axis and the vertical axis respectively. The vector pheromone generated by the aircraft at position B is expressed as: (12)

[0009] Considering that the pheromones generated by the aircraft from different directions need to be accumulated, the result is expressed as: (13)

[0010] When the aircraft passes the same position from multiple directions and generates corresponding vector pheromones, new vector pheromones are accumulated. The accumulated vector pheromones should theoretically be close to the gradient direction of the position, and the size is positively correlated with the gradient.

[0011] Preferably, in formula (2), It is considered as the growth of signal strength along the direction of aircraft movement, representing the vector length; and is a unit vector along the direction of the aircraft's motion, representing the vector The direction of ; the two together form the vector , which is regarded as the experience gained by the aircraft in the previous movement process.

[0012] Preferably, the pheromone update formula in the cumulative update mechanism is as follows: (14) Where, Represents the pair stored in n The first m The vector pheromone obtained by the update operation is Indicates that in this update i The aircraft releases the vector pheromone in the pheromone carrier; parameter and represent the pheromone dissipation coefficient and accumulation coefficient respectively.

[0013] Preferably, the accumulation formula of pheromones in the alternative update mechanism is as follows: (15)

[0014] Preferably, a pheromone update mechanism is adopted, and its update formula is as follows: (16) Where, It is an amplification coefficient. When the newly generated pheromone is obviously better than the existing pheromone at the current position, the new pheromone directly replaces the existing pheromone. When the newly generated pheromone is not as good as the existing pheromone at the current position, the two are accumulated.

[0015] Preferably, the step 3 is specifically as follows: During the search process, the aircraft continuously detects the target signal strength and reads the pheromone carrier to obtain the vector pheromone. In the basic particle swarm algorithm, the particles The speed position update formula is: (17) (18) Where, and Respectively represent i The particle in k The velocity and position in the iteration, and represent the historical optimal position and global optimal position of the particle respectively, ω is the coefficient of inertia, and They represent the self-learning factor and the social learning factor, and are two independent random numbers, Δ t is the time step; The particle swarm algorithm with linearly adjusted inertia weight is used in the early stage of the algorithm. ω Take the maximum value to allow particles to search a wider space so that they are not likely to miss the global optimal solution. ω Take the minimum value to speed up the convergence of the algorithm. The adjustment method is: (19) in, and are the initial and final values ​​of the inertia weight, t and T are the current number of iterations and the maximum number of iterations respectively; In the early stage of the algorithm, the self-learning factor is maximized, and the entire population fully searches for the optimal solution in the largest space. In the later stage of the algorithm, the social learning factor is maximized to speed up the convergence of the algorithm. The adjustment method is: (20) (twenty one) Where, and The maximum and minimum values ​​of the self-learning factor set before the start, and is the maximum and minimum value of the set social learning factor; When searching for a static target, the global variable in equation (17) The aircraft system is required to have very good global communication capabilities, and vector pheromones are used to replace the global optimal position. The speed and position update formula of the aircraft is: (twenty two) (twenty three) Where, and Respectively represent i The aircraft in k The velocity and position in the iteration, Indicates aircraft i Its own historical optimal position, parameters Indicates the i The aircraft from n The vector pheromone read in each pheromone carrier; For dynamic environments, the aircraft's velocity and position update formula is modified as follows: (twenty four) (25) Where, Indicates the i The aircraft in k The vector pheromone generated in each iterative step can still use the search experience of the aircraft in the previous step to speed up the search speed of the aircraft in a dynamic environment.

[0016] A heterogeneous aircraft cluster collaborative capture and search system for a denied environment, the system comprising: An initialization module, wherein the initialization module initializes the positions and velocities of the heterogeneous aircraft cluster and the target; The first phase module, in which a cluster of heterogeneous aircraft randomly moves to explore the search area and establishes an initial pheromone map using a pheromone map construction algorithm; The second stage module, when the upper limit of the iteration step of the first stage is reached, the second stage module starts the second stage: the heterogeneous aircraft cluster uses the target search algorithm to update the motion state, and at the same time uses the pheromone map construction algorithm to build the pheromone map; when the upper limit of the iteration step of the second stage is reached, the second stage ends.

[0017] A computer-readable storage medium stores a computer program, which is executed by a processor to implement a heterogeneous aircraft cluster collaborative capture and search method for a denial environment.

[0018] A computer device includes a memory and a processor, wherein the memory stores a computer program and the processor implements a heterogeneous aircraft cluster collaborative capture and search method for a denied environment when executing the computer program.

[0019] The present invention has the following beneficial effects: The present invention improves the search efficiency and capture success rate of a cluster of aircraft for a target, and forms an effective distributed collaborative perception and decision-making mechanism.

[0020] The present invention updates the motion state of aircraft by constructing a pheromone map and combining vector pheromone and particle swarm algorithm to achieve indirect communication and collaborative control of aircraft clusters in a denied environment; the present invention can meet the future requirements of intelligent and networked aircraft cluster combat and monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1Shown is a flow chart of the method for collaborative capture and search of heterogeneous aircraft clusters in a denial environment according to the present invention; Figure 2 Shown is a schematic diagram of the target signal function of the present invention; Figure 3 Shown is a schematic diagram of the vector pheromone production process of the present invention. DETAILED DESCRIPTION

[0023] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0024] The present invention is described in detail below with reference to specific embodiments. Specific embodiment one: according to Figures 1 to 3 As shown, the specific optimization technical solution adopted by the present invention to solve the above technical problems is: the present invention relates to a collaborative capture and search method for a cluster of heterogeneous aircraft in a denied environment.

[0026] The present invention provides a method for collaborative capture and search of heterogeneous aircraft clusters in a denial environment, the method comprising the following steps: Step 1: Initialize the position and velocity of the heterogeneous aircraft cluster and the target; Step 2: Entering the first stage: The heterogeneous aircraft cluster moves randomly, explores the search area, and uses the pheromone map construction algorithm to build the initial pheromone map; Step 3: When the upper limit of the iteration step of the first stage is reached, the second stage begins: the heterogeneous aircraft cluster uses the target search algorithm to update the motion state, and at the same time uses the pheromone map construction algorithm to build the pheromone map; when the upper limit of the iteration step of the second stage is reached, the second stage ends.

[0027] Based on a consensus initiative mechanism, the present invention establishes a coordination mechanism between aircraft, enabling them to influence each other's behavior and achieve collaborative cooperation. In a denial environment, indirect communication between aircraft is achieved by aircraft reading and writing pheromones in the medium. Under a distributed search algorithm, the pheromone map is integrated and optimized with the particle swarm algorithm to obtain optimized search and capture results. This invention addresses the problems of limited communication and complex collaboration in collaborative search and capture by aircraft clusters in a denial environment. By integrating and optimizing environmental perception and collaborative control, it improves search efficiency and capture success rate, forming an effective distributed collaborative perception and decision-making mechanism. Specific embodiment two: The difference between the second embodiment of the present application and the first embodiment is that: The step 2 is specifically as follows: The target signal value detected by the aircraft is expressed by a three-dimensional function, namely: (1) In the formula, the function is a continuous function related to the position of the aircraft, signal Represents the target signal strength that the aircraft can detect at the coordinate (x, y, z); The blue aircraft moves from point A to point B along the blue dotted line, leaving a pheromone vector at point B. , expressed by the following formula: (2) Where, and Indicates the target signal values ​​detected by the blue aircraft at points A and B. and Represents the coordinates of point A and point B, Indicates the distance between point A and point B; use To represent the unit vector, that is, in formula (2) , formula (2) is rewritten as: (3) The target signal field is along the unit vector The directional derivative of the direction is According to the definition of directional derivative, we get: (4) Where, ρ =||Δ x ||2,Δ x is the difference in coordinates; when point A and point B are close, then: (5) Rewrite formula (4) as: (6) is the directional derivative of the target signal field at point B, that is: (7) From the relationship between directional derivatives and gradients, we can see that directional derivatives are the projections of gradients in that direction, namely: (8) Where, Represents the gradient of the signal field at point B. Combining the above formulas, we get: (9) The green aircraft moves along the green dotted line from point C to point B, leaving behind vector pheromones at point B. Written in the same form; if an aircraft moves to point B along the direction of the signal gradient, the vector pheromone it leaves at point B is equal to the signal gradient at that point; when multiple aircraft leave their own vector pheromones at B, the vector pheromones will be accumulated at point B, and the accumulated vector pheromone is expressed as , in formula (9) and In Cartesian coordinates it is: (10) (11) Where, and Represent the unit vectors of the horizontal axis and the vertical axis respectively. The vector pheromone generated by the aircraft at position B is expressed as: (12)

[0029] Considering that the pheromones generated by the aircraft from different directions need to be accumulated, the result is expressed as: (13)

[0030] When the aircraft passes the same position from multiple directions and generates corresponding vector pheromones, new vector pheromones are accumulated. The accumulated vector pheromones should theoretically be close to the gradient direction of the position, and the size is positively correlated with the gradient. Specific embodiment three: The only difference between the third embodiment of the present application and the second embodiment is that: In formula (2), It is considered as the growth of signal strength along the direction of aircraft movement, representing the vector length; and is a unit vector along the direction of the aircraft's motion, representing the vector The direction of ; the two together form the vector , which is regarded as the experience gained by the aircraft in the previous movement process. Specific embodiment four: The only difference between the fourth embodiment of the present application and the third embodiment is that: The update formula of pheromone in the cumulative update mechanism is as follows: (14) Where, Represents the pair stored in n The first m The vector pheromone obtained by the update operation is Indicates that in this update i The aircraft releases the vector pheromone in the pheromone carrier; parameter and represent the pheromone dissipation coefficient and accumulation coefficient respectively. Specific embodiment five: The only difference between the fifth embodiment of the present invention and the fourth embodiment is that: The accumulation formula of pheromones in the substitution update mechanism is as follows: (15) Specific embodiment six: The only difference between the sixth embodiment of the present invention and the fifth embodiment is that: The pheromone update mechanism is adopted, and its update formula is as follows: (16) Where, It is an amplification coefficient. When the newly generated pheromone is obviously better than the existing pheromone at the current position, the new pheromone directly replaces the existing pheromone. When the newly generated pheromone is not as good as the existing pheromone at the current position, the two are accumulated. Specific embodiment seven: The only difference between the seventh embodiment of the present invention and the sixth embodiment is that: The step 3 is specifically as follows: During the search process, the aircraft continuously detects the target signal strength and reads the pheromone carrier to obtain the vector pheromone. In the basic particle swarm algorithm, the particles The speed position update formula is: (17) (18) Where, and Respectively represent i The particle in k The velocity and position in the iteration, and represent the historical optimal position and global optimal position of the particle respectively, ω is the coefficient of inertia, and They represent the self-learning factor and the social learning factor, and are two independent random numbers, Δ t is the time step; The particle swarm algorithm with linearly adjusted inertia weight is used in the early stage of the algorithm. ω Take the maximum value to allow particles to search a wider space so that they are not likely to miss the global optimal solution. ω Take the minimum value to speed up the convergence of the algorithm. The adjustment method is: (19) in, and are the initial and final values ​​of the inertia weight, t and T are the current number of iterations and the maximum number of iterations respectively; In the early stage of the algorithm, the self-learning factor is maximized, and the entire population fully searches for the optimal solution in the largest space. In the later stage of the algorithm, the social learning factor is maximized to speed up the convergence of the algorithm. The adjustment method is: (20) (twenty one) Where, and The maximum and minimum values ​​of the self-learning factor set before the start, and is the maximum and minimum value of the set social learning factor; When searching for a static target, the global variable in equation (17) The aircraft system is required to have very good global communication capabilities, and vector pheromones are used to replace the global optimal position. The speed and position update formula of the aircraft is: (twenty two) (twenty three) Where, and Respectively represent i The aircraft in k The velocity and position in the iteration, Indicates aircraft i Its own historical optimal position, parameters Indicates the i The aircraft from n The vector pheromone read in each pheromone carrier; For dynamic environments, the aircraft's velocity and position update formula is modified as follows: (twenty four) (25) Where, Indicates the i The aircraft in k The vector pheromone generated in each iterative step can still use the search experience of the aircraft in the previous step to speed up the search speed of the aircraft in a dynamic environment. Specific embodiment eight: The only difference between the eighth embodiment of the present invention and the seventh embodiment is that: The present invention provides a heterogeneous aircraft cluster collaborative capture and search system for a denial environment, the system comprising: An initialization module, wherein the initialization module initializes the positions and velocities of the heterogeneous aircraft cluster and the target; The first phase module, in which a cluster of heterogeneous aircraft randomly moves to explore the search area and establishes an initial pheromone map using a pheromone map construction algorithm; The second stage module, when the upper limit of the iteration step of the first stage is reached, the second stage module starts the second stage: the heterogeneous aircraft cluster uses the target search algorithm to update the motion state, and at the same time uses the pheromone map construction algorithm to build the pheromone map; when the upper limit of the iteration step of the second stage is reached, the second stage ends. Specific embodiment nine: The only difference between the ninth embodiment of the present invention and the eighth embodiment is that: The present invention provides a computer-readable storage medium on which a computer program is stored. The program is executed by a processor to implement a heterogeneous aircraft cluster collaborative capture and search method for a denial environment. Specific embodiment ten: The only difference between the tenth embodiment of the present invention and the ninth embodiment is that: The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements a heterogeneous aircraft cluster collaborative capture and search method for a denial environment when executing the computer program. Specific embodiment eleven: The only difference between the eleventh embodiment of the present invention and the tenth embodiment is that: A "target-extreme value" model is established for the target search problem of aircraft clusters. The aircraft's motion space is regarded as the independent variable space of the function, and the target to be searched is regarded as the extreme point of the function. The function value corresponding to each position in the space is related to the target signal distribution. The aircraft detects the target signal and calculates the function value, thereby transforming the target search problem into the problem of finding the extreme point of the function. A typical target signal function is as follows: Figure 2 shown.

[0040] Figure 2The figure shows a two-dimensional aircraft motion space. In practice, the target signal strength distribution is often three-dimensional, so the target signal value detected by the aircraft can be represented by a three-dimensional function, namely: (1) In the formula, the function is a continuous function related to the position of the aircraft, signal Represents the target signal strength that the aircraft can detect at coordinates (x, y, z). For aircraft moving in two-dimensional space, z=0.

[0041] The local precision search adopts a heuristic search strategy. The aircraft can only detect the target signal value and pheromones in the environment during the search process. The speed of the aircraft in each iteration step is V The update is performed based on the following three sub-items: (1) the aircraft inherits the speed of the previous step, specifically the speed of the previous step multiplied by the inertia coefficient; (2) the aircraft learns the individual historical information, specifically to obtain a speed from the current position to the individual historical optimal position (that is, the position with the largest target signal value that the aircraft has reached so far, and this variable will be continuously updated as the aircraft continues to reach new positions). This speed is adjusted by multiplying a self-learning factor; (3) the aircraft learns the pheromone, specifically to obtain a speed consistent with the direction of the vector pheromone read from the environment before each iteration. This speed is adjusted by multiplying a social learning factor. Through this speed update strategy, the aircraft continues to approach the target, and ultimately achieves as many aircraft as possible to converge at the target. Since the aircraft cluster can only detect target signals and pheromones in different positions during the search process, the aircraft itself cannot determine whether it has "found" the target. Therefore, a group of aircraft must converge to a certain position to determine that the aircraft has found the target, thereby achieving accurate search of the target.

[0042] Successfully applying the consensus initiative mechanism to target search missions requires that aircraft release their search experience into the environment in the form of pheromones for other aircraft to read, thereby achieving system-level coordination. In this process, designing an appropriate pheromone model is very important.

[0043] Inspired by the concept of vector, pheromone is modeled in vector form and named vector pheromone. Figure 3 The following figure shows a typical vector pheromone. The color of the fan-shaped area in the figure represents the target signal value detected by the aircraft. The darker the color, the greater the target signal value detected by the aircraft.

[0044] Assume that the blue aircraft moves from point A to point B along the blue dotted line and leaves a pheromone vector at point B. , expressed by the following formula: (2) Where, and Indicates the target signal values ​​detected by the blue aircraft at points A and B. and Represents the coordinates of point A and point B, represents the distance between point A and point B. In formula (2), It can be regarded as the growth rate of signal strength along the direction of aircraft movement, representing the vector Length. is a unit vector along the direction of the aircraft's motion, representing the vector The two together form the vector , which can be regarded as the experience gained by the aircraft in the previous movement process.

[0045] use To represent the unit vector, that is, in formula (2) , so formula (2) can also be rewritten as: (3) The target signal field is along the unit vector The directional derivative of a direction can be calculated using According to the definition of directional derivative, we can get: (4) Where, ρ =||Δ x ||2,Δ x is the difference in coordinates. If point A and point B are close enough, then: (5) Therefore, formula (4) can be rewritten as: (6) In practice, there is always some distance between point A and point B, so It can be approximately considered as the directional derivative of the target signal field at point B, that is: (7) From the relationship between directional derivatives and gradients, we can see that directional derivatives are the projections of gradients in that direction, namely: (8) Where, Represents the gradient of the signal field at point B, such as Figure 3 As shown by the arrow in . Combining the above formulas, we can get: (9) Similarly, the green aircraft moves along the green dotted line from point C to point B, leaving behind a vector pheromone at point B. It can also be written in a similar form. If an aircraft happens to move to point B along the direction of the signal gradient, then the vector pheromone it leaves at point B is approximately equal to the signal gradient at that point. When multiple aircraft leave their own vector pheromones at point B, the vector pheromones will be accumulated at point B. This accumulated vector pheromone is Figure 3 In Chinese In formula (9) and In the Cartesian coordinate system it can be expressed as: (10) (11) Where, and Represent the unit vectors of the horizontal axis and the vertical axis respectively. Therefore, the general form of the vector pheromone generated by the aircraft at position B can be expressed as: (12)

[0046] Considering that the pheromones generated by the aircraft from different directions need to be accumulated, the result can be expressed as: Therefore, when the aircraft passes the same location from multiple directions and generates corresponding vector pheromones, new vector pheromones can be accumulated, such as Figure 3 Vectors in The accumulated vector pheromone should theoretically be close to the gradient direction of the position, and its size is positively correlated with the gradient.

[0047] (13)

[0048] Step 3: The accumulation and dissipation of pheromones reflects the dynamic changes of the environment. However, unlike biological pheromones such as scents and hormones, data stored in electronic devices does not automatically accumulate or dissipate over time unless human-designed rules are followed. Therefore, to reflect dynamic environmental changes, pheromone accumulation and dissipation are performed during the aircraft's read and write operations. This method proposes three pheromone update mechanisms: cumulative, replacement, and integrated.

[0049] The update formula of pheromone in the cumulative update mechanism is as follows: (14) Where, Represents the pair stored in n The first m The vector pheromone obtained by the update operation is Indicates that in this update i The aircraft releases the vector pheromone in the pheromone carrier. Parameters and The advantages of this updating method are that it is more sensitive to the dynamic changes of the environment, but the disadvantage is that the accumulation of pheromones is slow. In order to reflect the dynamic changes of the environment or to obtain a certain amount of pheromones at a certain location, it takes a long time.

[0050] The accumulation formula of pheromones in the alternative updating mechanism is as follows: (15) Where, and The meaning represented is the same as that in formula (14). This pheromone update method is easy to implement. Its advantage is that the pheromone accumulation speed is fast. Its disadvantage is that it cannot accurately reflect the dynamic changes of the environment. It is suitable for static environments or dynamic environments with slow changes and small changes.

[0051] The advantages and disadvantages of the above two pheromone update mechanisms are obvious and complementary. Therefore, a comprehensive pheromone update mechanism is proposed by combining the two pheromone update mechanisms. The update formula is as follows: (16) Where, is an amplification factor, typically set to 1. This means that when the newly generated pheromone is significantly better than the existing pheromone at the current location, the new pheromone directly replaces the existing pheromone. When the newly generated pheromone is worse than the existing pheromone at the current location, the two are accumulated. This comprehensive pheromone update mechanism not only increases the speed of pheromone updates but also effectively reflects dynamic changes in the environment.

[0052] During the search process, the aircraft continuously detects the target signal strength and reads the pheromone carrier to obtain the vector pheromone. The aircraft's speed update mechanism is inspired by the basic particle swarm algorithm. In the basic particle swarm algorithm, particles The speed position update formula is: (17) (18) Where, and Respectively represent i The particle in k The velocity and position in the iteration, and represent the historical optimal position and global optimal position of the particle respectively, ω is the coefficient of inertia, and They represent the self-learning factor and the social learning factor, and are two independent random numbers, Δ t is the time step.

[0053] In the particle swarm optimization algorithm, the inertia weight ω It has an impact on the performance of the algorithm. ω When is large, the proportion of the original velocity in each component of the particle velocity is relatively large, and the particles are not easily attracted by the individual historical optimal position and the global optimal position, so they can explore a wider space, and the overall convergence speed of the particle swarm is slow; when ω When it is small, particles are more likely to be attracted by the individual historical optimal position and the global optimal position, and the convergence speed is faster, but it is easy to be deceived by the local optimal position, so that the algorithm cannot find the global optimal solution. ω Taking a larger value allows particles to search a wider space so that they are less likely to miss the global optimal solution. ω Taking a smaller value can speed up the convergence of the algorithm. The adjustment method is: (19) in, and are the initial and final values ​​of the inertia weight, t and T are the current number of iterations and the maximum number of iterations respectively.

[0054] Learning Factor and The value of also has a significant impact on the performance of the particle swarm algorithm. When the self-learning factor is larger than the social learning factor, the entire population will be too free in the search space and difficult to converge; conversely, the population will converge quickly and the algorithm will easily fall into a local optimal solution. Therefore, in the early stages of the algorithm, the self-learning factor is set to a larger value so that the entire population can fully search for the optimal solution in a larger space. In the later stages of the algorithm, the social learning factor is set to a larger value to speed up the convergence of the algorithm. The adjustment method is: (20) (twenty one) in, and The maximum and minimum values ​​of the self-learning factor set before the start, and is the maximum and minimum value of the social learning factor.

[0055] When searching for a static target, the global variable in equation (17) The aircraft system is required to have very good global communication capabilities, but this requirement is difficult to guarantee when the system scales up. Using vector pheromones to replace the global optimal position, the aircraft speed and position update formula is: (twenty two) (twenty three) Where, and Respectively represent i The aircraft in k The velocity and position in the iteration, Indicates aircraft i The best historical position of the aircraft itself, which is obtained from the aircraft's own search experience. Indicates the i The aircraft from n Vector pheromone read in pheromone carriers When searching for dynamic targets, the aircraft's historical optimal position is determined by the target signal value detected by the aircraft, which becomes unreliable in a dynamic environment and may even have a negative impact on the search mission, causing the aircraft to sometimes fail to find the target. Therefore, the self-learning parameters can be adjusted. Set to 0. In fact, although the aircraft's historical optimal position is unreliable, the aircraft's search experience in the recent period can still be used. For dynamic environments, the aircraft's speed and position update formula can be modified as follows: (twenty four) (25) Where, Indicates the i The aircraft in k In this way, the search experience of the aircraft in the previous step is still utilized, which can speed up the search speed of the aircraft in a dynamic environment.

[0056] A typical search process can be divided into two phases: the initial mapping phase and the search and tracking phase. During the initial mapping phase, the swarm of aircraft moves randomly, exploring as wide an area as possible. A pheromone map construction algorithm is used to construct an initial pheromone map, preparing for subsequent precise target search and tracking. Once the set upper limit of phase iterations is reached, the search and tracking phase begins. Searching involves the swarm of aircraft converging from an arbitrary state to the vicinity of a target, emphasizing the process of target discovery. Tracking, on the other hand, involves the swarm remaining near the target and following it after discovery. During this phase, the aircraft acquire the ability to update their velocity, and their movements are guided by the target search algorithm. The algorithm terminates after the set movement time has elapsed. Throughout the search process, the pheromone map construction algorithm continuously updates the pheromone map. Specific implementation method 12: A method for collaborative capture and search of heterogeneous aircraft clusters in a denial environment, comprising the following steps: Step S1: Initialize the position and velocity of the heterogeneous aircraft cluster and the target. Step S2: In the first stage, the heterogeneous aircraft cluster moves randomly, explores the search area as much as possible, and uses the pheromone map construction algorithm to build an initial pheromone map; The step S2 specifically includes the following steps: Step S2.1: The aircraft moves according to the initialized speed and direction; Step S2.2: During the movement of the aircraft, the aircraft detects the target signal strength of the grid in which it is located; Step S2.3: The aircraft uses the pheromone map construction algorithm to obtain the vector pheromone of the grid at that moment; Step S2.4: The aircraft updates the vector pheromone of the grid in which it is located according to the vector pheromone update mechanism; Step S2.5: The aircraft changes its motion state by means of an obstacle avoidance mechanism to avoid collision with other aircraft or obstacles.

[0058] Step S3: When the upper limit of the first phase iteration step is reached, the second phase begins. The heterogeneous aircraft cluster uses the target search algorithm to update the motion state and simultaneously uses the pheromone map construction algorithm to build the pheromone map. When the upper limit of the second phase iteration step is reached, the program ends.

[0059] The step S3 specifically includes the following steps: Step S3.1: The aircraft reads the vector pheromone of the grid where it is located; Step S3.2: The aircraft updates its motion state using the target search algorithm; Step S3.3: While the aircraft is in motion, it detects the target signal strength of the grid in which it is located; Step S3.4: The aircraft uses the pheromone map construction algorithm to obtain the vector pheromone of the grid at that moment; Step S3.5: The aircraft updates the vector pheromone of the grid in which it is located according to the vector pheromone update mechanism; Step S3.6: The aircraft adjusts its motion state based on the obstacle avoidance algorithm to prevent collision with other aircraft or obstacles.

[0060] The present invention improves the search efficiency and capture success rate of aircraft clusters for targets; The present invention forms an effective distributed collaborative perception and decision-making mechanism; The present invention constructs a pheromone map and combines vector pheromone and particle swarm algorithm to update the motion state of the aircraft, so as to realize indirect communication and cooperative control of the aircraft cluster in a denied environment; The present invention can meet the requirements of future intelligent and networked aircraft cluster combat and monitoring.

[0061] The above is merely a preferred embodiment of the method for collaboratively capturing and searching a swarm of heterogeneous aircraft in a denied environment. The scope of protection for the method is not limited to the aforementioned embodiments; all technical solutions based on this concept fall within the scope of protection of the present invention. It should be noted that improvements and variations that do not depart from the principles of the present invention, as readily apparent to those skilled in the art, should also be considered within the scope of protection of the present invention.

Claims

1. A collaborative search and capture method for heterogeneous aircraft swarms in a denial environment, characterized by: The method comprises the following steps: Step 1: Initialize the position and velocity of the heterogeneous aircraft cluster and the target; Step 2: Entering the first stage: The heterogeneous aircraft cluster moves randomly, explores the search area, and uses the pheromone map construction algorithm to build the initial pheromone map; Step 3: When the upper limit of the iteration step of the first stage is reached, the second stage begins: the heterogeneous aircraft cluster uses the target search algorithm to update the motion state, and at the same time uses the pheromone map construction algorithm to build the pheromone map; when the upper limit of the iteration step of the second stage is reached, the second stage ends.

2. The method according to claim 1, wherein: The step 2 is specifically as follows: The target signal value detected by the aircraft is expressed by a three-dimensional function, namely: （1） In the formula, the function is a continuous function related to the position of the aircraft, signal Represents the target signal strength that the aircraft can detect at the coordinate (x, y, z); The blue aircraft moves from point A to point B along the blue dotted line, leaving a pheromone vector at point B. , expressed by the following formula: （2） Where, and Indicates the target signal values ​​detected by the blue aircraft at points A and B. and Represents the coordinates of point A and point B, Indicates the distance between point A and point B; use To represent the unit vector, that is, in formula (2) , formula (2) is rewritten as: （3） The target signal field is along the unit vector The directional derivative of the direction is According to the definition of directional derivative, we get: （4） Where, ρ =||Δ x ||2,Δ x is the difference in coordinates; when point A and point B are close, then: （5） Rewrite formula (4) as: （6） is the directional derivative of the target signal field at point B, that is: （7） From the relationship between directional derivatives and gradients, we can see that directional derivatives are the projections of gradients in that direction, namely: （8） Where, Represents the gradient of the signal field at point B. Combining the above formulas, we get: （9） The green aircraft moves along the green dotted line from point C to point B, leaving behind vector pheromones at point B. Written in the same form; If a spacecraft moves to point B along the direction of the signal gradient, the vector pheromone it leaves at point B is equal to the signal gradient at that point. When multiple spacecraft leave their own vector pheromones at point B, the vector pheromones will be accumulated at point B. The accumulated vector pheromones are expressed as , in formula (9) and In Cartesian coordinates it is represented as: （10） （11） Where, and Represent the unit vectors of the horizontal axis and the vertical axis respectively. The vector pheromone generated by the aircraft at position B is expressed as: （12） Considering that the pheromones generated by the aircraft from different directions need to be accumulated, the result is expressed as: （13） When the aircraft passes the same position from multiple directions and generates corresponding vector pheromones, new vector pheromones are accumulated. The accumulated vector pheromones should theoretically be close to the gradient direction of the position, and the size is positively correlated with the gradient.

3. The method according to claim 2, wherein: In formula (2), It is considered as the growth of signal strength along the direction of aircraft movement, representing the vector length; and is a unit vector along the direction of the aircraft's motion, representing the vector The direction of ; the two together form the vector , which is regarded as the experience gained by the aircraft in the previous movement process.

4. The method according to claim 3, wherein: The update formula of pheromone in the cumulative update mechanism is as follows: （14） Where, Represents the pair stored in n The first m The vector pheromone obtained by the update operation is Indicates that in this update i The aircraft releases the vector pheromone in the pheromone carrier; parameter and represent the pheromone dissipation coefficient and accumulation coefficient respectively.

5. The method according to claim 3, wherein: The accumulation formula of pheromones in the substitution update mechanism is as follows: （15） 6. The method according to claim 3, wherein: The pheromone update mechanism is adopted, and its update formula is as follows: （16） Where, It is an amplification coefficient. When the newly generated pheromone is obviously better than the existing pheromone at the current position, the new pheromone directly replaces the existing pheromone. When the newly generated pheromone is not as good as the existing pheromone at the current position, the two are accumulated.

7. The method according to claim 4, 5 or 6, characterized in that: The step 3 is specifically as follows: During the search process, the aircraft continuously detects the target signal strength and reads the pheromone carrier to obtain the vector pheromone. In the basic particle swarm algorithm, the particles The speed position update formula is: （17） （18） Where, and Respectively represent i The particle in k The velocity and position in the iteration, and represent the historical optimal position and global optimal position of the particle respectively, ω is the coefficient of inertia, and They represent the self-learning factor and the social learning factor, and are two independent random numbers, Δ t is the time step; The particle swarm algorithm with linear adjustment of inertia weight is used in the early stage of the algorithm. ω Take the maximum value to allow particles to search a wider space so that they are not likely to miss the global optimal solution. ω Take the minimum value to speed up the convergence of the algorithm. The adjustment method is: （19） in, and are the initial and final values ​​of the inertia weight, t and T are the current number of iterations and the maximum number of iterations respectively; In the early stage of the algorithm, the self-learning factor is maximized, and the entire population fully searches for the optimal solution in the largest space. In the later stage of the algorithm, the social learning factor is maximized to speed up the convergence of the algorithm. The adjustment method is: （20） （21） Where, and The maximum and minimum values ​​of the self-learning factor set before the start, and is the maximum and minimum value of the set social learning factor; When searching for a static target, the global variable The aircraft system is required to have very good global communication capabilities, and vector pheromones are used to replace the global optimal position. The speed and position update formula of the aircraft is: （22） （23） Where, and Respectively represent i The aircraft in k The velocity and position in the iteration, Indicates aircraft i Its own historical optimal position, parameters Indicates the i The aircraft from n The vector pheromone read in each pheromone carrier; For dynamic environments, the aircraft's velocity and position update formula is modified as follows: （24） （25） Where, Indicates the i The aircraft in k The vector pheromone generated in each iterative step can still use the search experience of the aircraft in the previous step to speed up the search speed of the aircraft in a dynamic environment.

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