Unmanned aerial vehicle group collaborative sea area dynamic target searching method based on ant colony algorithm

Abstract

The invention discloses an unmanned aerial vehicle group collaborative sea area dynamic target searching method based on an ant colony algorithm, and belongs to the technical field of unmanned aerial vehicles. The method comprises the following steps: determining a target search sea area corresponding to a to-be-tracked dynamic target, and meshing the target search sea area; establishing an unmanned aerial vehicle state model and a radar detection probability model for each unmanned aerial vehicle in the unmanned aerial vehicle group; establishing a target probability model; constructing a target function based on the target probability model and the radar detection probability model; constructing a corresponding ant colony for each unmanned aerial vehicle; based on the target function, the unmanned aerial vehicle state model and exchange of pheromones among ant colonies, an ant colony algorithm is used for solving an optimal path for searching of the unmanned aerial vehicle group in the target search sea area, and Bezier curve path smooth optimization is carried out on the optimal path. According to the invention, efficient and accurate sea area dynamic target search is realized.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) technology, and in particular relates to a method for UAV swarm collaborative dynamic target search in marine areas based on an improved ant colony algorithm. Background Technology

[0002] Currently, maritime target search is characterized by a large search area, tight mission time, and high target uncertainty. Employing a multi-UAV collaborative dynamic target search approach can significantly improve the ability to search for maritime targets. However, when multiple UAVs collaborate in maritime searches, a lack of scientific and efficient trajectory planning algorithms will lead to a decline in the collaborative efficiency of the multi-UAVs, making it difficult to realize their advantages, and may even result in collisions.

[0003] Unmanned aerial vehicles (UAVs) are widely used in various fields due to their advantages such as small size, high maneuverability, good concealment, and high positioning accuracy. Multi-UAV collaboration, compared to single-UAV collaboration, can improve the quality and efficiency of mission completion, shorten mission completion time, and increase the probability of mission success. However, when multiple UAVs collaborate to search for targets in a sea area, the lack of a scientific and efficient trajectory planning algorithm will lead to a decline in the efficiency of multi-UAV collaboration, making it difficult to realize its advantages, and may even result in collisions. Furthermore, most traditional algorithms are only suitable for searching static targets with fixed locations, and suffer from slow search speed and poor results.

[0004] In the multi-UAV search path planning problem, most traditional algorithms are only applicable to static targets with fixed locations, and suffer from slow search speed and poor results. Therefore, there are still some problems to be solved in cooperative target search for UAV swarms: (1) How to collaboratively apply unmanned aerial vehicle (UAV) swarms to dynamic target search in marine areas; (2) How to achieve efficient search, strong optimization ability and fast convergence speed of UAV swarm collaborative dynamic target search. Summary of the Invention

[0005] This invention proposes a method for dynamic target search in the sea area by UAV swarm collaboration based on an improved ant colony algorithm, which solves the technical problems mentioned above.

[0006] The first aspect of this invention proposes a method for dynamic target search in marine areas by unmanned aerial vehicle (UAV) swarms based on an improved ant colony algorithm, the method comprising: Step S1: Determine the target search area corresponding to the dynamic target to be tracked, and grid the target search area; Step S2: Establish a UAV state model and a radar detection probability model for each UAV in the UAV swarm. The UAV state model is used to characterize the UAV's heading, and the radar detection probability model is used to characterize the probability that the radar configured on the UAV will detect a dynamic target. Step S3: Establish a target probability model that represents the probability of the dynamic target to be tracked appearing in each grid of the target search area; Step S4: Construct an objective function based on the target probability model and the radar detection probability model; construct a corresponding ant colony for each UAV; based on the objective function, the UAV state model, and the communication of pheromones among the ant colonies, use the ant colony algorithm to solve the optimal path for the UAV swarm to search in the target search area, and perform Bézier curve path smoothing optimization on the optimal path.

[0007] Preferably, in step S1, the target search area where the target may appear is set as... The sea area and The unit is km, according to interval Divide the square grid in this way.

[0008] Preferably, in step S2, the UAV state model is as follows: ,in, The heading of the drone during the search process is represented by numbers 0 to 7, which represent due north, northeast, due east, southeast, due south, southwest, due west, and northwest, respectively.

[0009] Preferably, the radar detection probability model is established as follows: Where d is the distance between the UAV and the dynamic target to be tracked. R is the detection index of the UAV's airborne radar, and R is the maximum detection range of the UAV.

[0010] Preferably, in step S3, the target probability model is established as follows: in, For dynamic targets to appear at grid coordinates The probability of the grid. The network coordinates are The heat value of the grid is calculated as follows: within a historical period, the target's position in the grid at coordinates […]. The measure of the probability of occurrences within a circle with a radius of 5 km and a grid center is normalized and converted into a probability. The probability is then expressed as the network coordinates. The heat value of the grid.

[0011] Preferably, in step S4, an objective function is constructed based on the target probability model and the radar detection probability model, wherein the objective function is: Where J is the objective function, Search the target sea area; For grid coordinates, Coordinates of the drone n Number of times the target is imaged. N1 Total number of imaging attempts for the target For the dynamic target to be tracked in The probability of occurrence d The distance between the drone and the moving target to be tracked. This refers to the detection index of the UAV's airborne radar. R This represents the maximum detection range of the drone. Let n be the radar detection probability during the nth imaging. This is the radar detection probability expression.

[0012] Preferably, the ant colony algorithm solves the problem in parallel for each ant colony. When solving for one ant colony: Perform pheromone initialization and initialization of the starting position of each ant, including: Pheromones are distributed within each grid cell, and the initial value of the pheromone within each grid cell is... ; Randomly initialize the starting position of the ant. The ant's search range is the grid adjacent to its current grid that meets the maneuvering conditions. The grid that meets the maneuvering conditions is the grid that the ant can reach by moving straight, turning 45 degrees to the left, or turning 45 degrees to the right. Determine the ant's search path, including: Determine the first a ants of various species b From the current grid u Transfer to grid v probability : In the formula, For the first a ants of various species b In the current grid u The complete set of grids corresponding to the search range. Indicates at time t Grid v Inner a The amount of pheromones remaining in an ant colony For drones in the grid v Search revenue within the grid is equal to the search revenue of the grid. v The probability of a dynamic target appearing in the tracking phase. Indicates except the first a Apart from one ant colony, other ant colonies are in the grid. v The maximum value of residual pheromones in the body. The relative importance of one's own population experience; To determine the relative importance of search revenue, This represents the inhibition coefficient of other ant colony pheromones. For the first a The next step for each ant colony is to allow the selection of a grid set; update the pheromone, including: After all ants in the entire ant colony have completed one iteration, the ant in grid v is... a The pheromone strength of a population is updated using the following formula: In the formula, and The grids before and after the update are shown separately. v Inner a The intensity value of pheromone content in an ant colony. The pheromone evaporation coefficient, For grid v The internal pheromone intensity update value is defined as: In the formula, m The number of ants contained in each population. In the first t After the first search, the second a The first population b Only ants on the grid v The pheromones left inside are defined as: In the formula, For pheromone intensity, For the first a The first population b Only ants in the first t The sum of probabilities of the dynamic target to be tracked appearing within the grid traversed after each search. and They are the first a The first population b Only ants in the first t Within the grid traversed after the first search, the [number]th [unit / item] a The total amount of pheromones in one population and the average total amount of pheromones in other populations.

[0013] A second aspect of this invention proposes a drone swarm cooperative dynamic target search device for the sea area based on an improved ant colony algorithm, the device comprising: Initialization module: Configured to determine the target search area corresponding to the dynamic target to be tracked, and to grid the target search area; The first modeling module is configured to establish a UAV state model and a radar detection probability model for each UAV in the UAV swarm. The UAV state model is used to characterize the UAV's heading, and the radar detection probability model is used to characterize the probability that the radar configured on the UAV will detect a dynamic target. The second modeling module is configured to build a target probability model that represents the probability of a dynamic target being tracked appearing in each grid of the target search area. Path determination module: Configured to construct an objective function based on the target probability model and radar detection probability model; construct a corresponding ant colony for each UAV; based on the objective function, UAV state model, and pheromone communication between ant colonies, use the ant colony algorithm to solve the optimal path for the UAV swarm to search in the target search area, and perform Bézier curve path smoothing optimization on the optimal path.

[0014] A third aspect of the present invention provides an electronic device, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method as described above.

[0015] A fourth aspect of the present invention provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method described above.

[0016] This invention constructs a target probability model based on the detection performance of unmanned aerial vehicles (UAVs), designs the optimal path for UAV swarm collaborative dynamic target search in the sea area based on the ant colony algorithm, and performs smooth optimization of the optimal path based on Bézier curves. This achieves efficient search, strong optimization capability, and high-speed convergence of UAV swarm collaborative dynamic target search in the sea area. The realization of efficient and accurate dynamic target search in the sea area has significant theoretical and practical value. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the steps of the UAV swarm collaborative dynamic target search method for the sea area based on the improved ant colony algorithm provided by the present invention.

[0018] Figure 2 This is a schematic diagram of the target area environment model provided by the present invention.

[0019] Figure 3 This is a schematic diagram of the drone status provided by the present invention.

[0020] Figure 4 This is a schematic diagram illustrating the implementation of the ant colony algorithm for cooperative path optimization provided by the present invention.

[0021] Figure 5 This is a schematic diagram illustrating the smoothing implementation of Bézier curves provided by the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0023] like Figure 1 As shown, a method for dynamic target search in marine areas by unmanned aerial vehicle (UAV) swarms based on ant colony algorithm is presented. The method includes: Step S1: Determine the target search area corresponding to the dynamic target to be tracked, and grid the target search area; Step S2: Establish a UAV state model and a radar detection probability model for each UAV in the UAV swarm. The UAV state model is used to characterize the UAV's heading, and the radar detection probability model is used to characterize the probability that the radar configured on the UAV will detect a dynamic target. Step S3: Establish a target probability model that represents the probability of the dynamic target to be tracked appearing in each grid of the target search area; Step S4: Construct an objective function based on the target probability model and the radar detection probability model; construct a corresponding ant colony for each UAV; based on the objective function, the UAV state model, and the communication of pheromones among the ant colonies, use the ant colony algorithm to solve the optimal path for the UAV swarm to search in the target search area, and perform Bézier curve path smoothing optimization on the optimal path.

[0024] like Figure 2 As shown, in step S1, the target search area where the target may appear is set as... The sea area, according to the interval The grid is divided into squares using a specific method. In the target search area E, the coordinates of each grid point can be represented as... ,in , And set the grid point in the lower left corner of the target search area E as the origin of the coordinate system. For ease of subsequent explanation, this invention sequentially numbers the discretized grid of the target search area E, and denotes each grid point as... Therefore, it is easy to know that the grid number With grid coordinates There is a one-to-one correspondence, as shown in the following formula: In step S2, the UAV state model is as follows: ,in, The heading of the drone during the search process is represented by numbers 0 to 7, which represent due north, northeast, due east, southeast, due south, southwest, due west, and northwest, respectively.

[0025] Furthermore, the radar detection probability model is established as follows: Where d is the distance between the UAV and the dynamic target to be tracked. R is the detection index of the UAV's airborne radar, and R is the maximum detection range of the UAV.

[0026] In this invention, the UAV's onboard radar images the target search area at fixed intervals, and the radar analysis system detects whether there is a target within the field of view based on the images. Generally, the closer the UAV is to the target and the more times it takes pictures, the greater the probability of detecting the target.

[0027] Assume that during the UAV reconnaissance process, the target is imaged a total of N1 times, and the imaging distances are respectively... If any one of the N1 images detects the target, it means the target has been found. The probability that the UAV has found the target based on these N1 images is: Furthermore, in step S3, the target probability model is established as follows: in, For dynamic targets to appear at grid coordinates The probability of the grid. The network coordinates are The heat value of the grid is calculated as follows: within a historical period, the target's position in the grid at coordinates […]. The measure of the probability of occurrences within a circle with a radius of 5 km and a grid center is normalized and converted into a probability. The probability is then expressed as the network coordinates. The heat value of the grid.

[0028] In step S4, an objective function is constructed based on the target probability model and the radar detection probability model, wherein the objective function is: Where J is the objective function, Search the target sea area; For grid coordinates, Coordinates of the dronen Number of times the target is imaged. N1 Total number of imaging attempts for the target For the dynamic target to be tracked in The probability of occurrence d The distance between the drone and the moving target to be tracked. This refers to the detection index of the UAV's airborne radar. R This represents the maximum detection range of the drone. Let n be the radar detection probability during the nth imaging. This is the radar detection probability expression.

[0029] This invention assigns each drone to an ant colony and places it at a corresponding starting point. The drone searches and moves within the area where the target may exist. Each ant colony maintains its own pheromone structure, and the initial values ​​of the pheromones depend on the probability values ​​of the target appearing at each point, obtained from the target probability model. Simultaneously, each ant needs to maintain a taboo region based on the grid information it has already visited to reduce the probability of subsequent ants searching the same grid. Furthermore, ant colonies need to communicate with each other through pheromone exchange to achieve cooperative path optimization. The overall structure of the algorithm is as follows: Figure 4 As shown.

[0030] In this invention, after the drone swarm reaches the starting point, it can choose to move to adjacent feasible grids. The coordinates of the drones in the grid are: The forward direction will be based on this grid. As input, the direction of the next grid can only be straight, 45 degrees left turn, or 45 degrees right turn, that is: In step S4, a corresponding ant colony is constructed for each UAV; based on the objective function, the UAV state model, and the communication of pheromones among the ant colonies, the ant colony algorithm is used to solve the optimal path for the UAV swarm to search in the target search area.

[0031] Assume there is n If multiple drones are used to perform a collaborative search mission in a target sea area, then each drone corresponds to one ant colony, and each colony contains... m The process begins by assigning initial pheromone values ​​to all grid cells in the target search area. Then, all ants are placed at their respective initial points within the population and begin moving within the search area. Each ant selects adjacent feasible grid cells according to state transition rules during its movement, until all ants have completed one path search, thus completing one cycle. After each cycle, the global pheromone level is updated based on the paths traversed by each ant; for grid cells not visited, only pheromone evaporation occurs. This process is repeated until the optimal target search path is obtained.

[0032] The ant colony algorithm solves the problem in parallel for each ant colony. When solving for one ant colony: Perform pheromone initialization and initialization of the starting position of each ant, including: Pheromones are distributed within each grid cell, and the initial value of the pheromone within each grid cell is... ; Randomly initialize the starting position of the ant. The ant's search range is the grid adjacent to its current grid that meets the maneuvering conditions. The grid that meets the maneuvering conditions is the grid that the ant can reach by moving straight, turning 45 degrees to the left, or turning 45 degrees to the right. Determine the ant's search path, including: Determine the first a ants of various species b From the current grid u Transfer to grid v probability : In the formula, For the first a ants of various species b In the current grid u The complete set of grids corresponding to the search range. Indicates at time t Grid v Inner a The amount of pheromones remaining in an ant colony For drones in the grid v Search revenue within the grid is equal to the search revenue of the grid. v The probability of a dynamic target appearing in the tracking phase. Indicates except the first a Apart from one ant colony, other ant colonies are in the grid. v The maximum value of residual pheromones in the body. The relative importance of one's own population experience; To determine the relative importance of search revenue, This represents the inhibition coefficient of other ant colony pheromones. Provide the grid set that the ants of the a-th population can choose next; update the pheromone, including: After all ants in all ant colonies have completed one iteration, the pheromone intensity of the a-th ant colony within grid v is updated using the following formula: In the formula, and The grids before and after the update are shown separately. v Inner a The intensity value of pheromone content in an ant colony. The pheromone evaporation coefficient, For grid v The internal pheromone intensity update value is defined as: In the formula, m The number of ants contained in each population. In the first t After the first search, the second a The first population b Only ants on the grid v The pheromones left inside are defined as: In the formula, For pheromone intensity, For the first a The first population b Only ants in the first t The sum of probabilities of the dynamic target to be tracked appearing within the grid traversed after each search. and They are the first a The first population b Only ants in the first t Within the grid traversed after the first search, the [number]th [unit / item] a The total amount of pheromones in one population and the average total amount of pheromones in other populations.

[0033] In this invention, from the current grid u Transfer to grid v The probability of a node being a match is determined by the cost of the edge between the two nodes, the strength of the pheromone in its own population, and the strength of the pheromone in other populations. This is the inhibition coefficient of other ant colony pheromones. The larger the value, the stronger the inhibition effect, which can avoid invalid searches caused by overlapping search areas between different drones. For the first a The first population b Only ants in the first t The sum of probabilities of the dynamic target to be tracked appearing within the grid traversed after each search represents the search probability of that path. The higher the search probability, the greater the enhancement value. This represents the degree of overlap between the path and the paths searched by other ant colonies. The larger the value, the smaller the overlap, and the greater the enhancement value.

[0034] In this invention, in step S4, the optimal path is optimized using Bézier curve path smoothing, wherein: By using Bézier curves to smooth the inflection points in the optimal path, a smooth optimal path is obtained, making the path smoother and improving the working efficiency of the UAV.

[0035] Bézier curves depend on determining the number of control points for that section of the curve. A vertex can be defined A curve of degree polynomial. Then, a fourth-order Bézier curve can be defined in the plane through five vertices between every two path coordinates. The parametric equations of the points of the fourth-order Bézier curve are: Among them A schematic diagram of a fourth-order Bézier curve is shown below. Figure 5 As shown in the figure. and The coordinates can be obtained using the improved ant colony algorithm described above. If we assume... and The distance between them is Then we can obtain ,Will Substituting the parametric equations of the fourth-order Bézier curve into the equations, we can obtain... ,set up and The distance between them is Then we can obtain .

[0036] The above calculations yield the following results. The coordinates of 5 vertices contain 3 independent variables, namely: A unique fourth-order Bézier curve is determined using these three independent variables. The three parameters are optimized by setting the curve curvature, minimizing the difference between the maximum and minimum curvature. This method can then be used to smooth the obtained optimal path.

[0037] The apparatus provided for carrying out the present invention will be described below. The specific implementation process and technical effects are as described above and will not be repeated below.

[0038] Optionally, embodiments of the present invention provide a drone swarm cooperative dynamic target search device for the sea area based on an improved ant colony algorithm, the device comprising: Initialization module: Configured to determine the target search area corresponding to the dynamic target to be tracked, and to grid the target search area; The first modeling module is configured to establish a UAV state model and a radar detection probability model for each UAV in the UAV swarm. The UAV state model is used to characterize the UAV's heading, and the radar detection probability model is used to characterize the probability that the radar configured on the UAV will detect a dynamic target. The second modeling module is configured to build a target probability model that represents the probability of a dynamic target being tracked appearing in each grid of the target search area. Path determination module: Configured to construct an objective function based on the target probability model and radar detection probability model; construct a corresponding ant colony for each UAV; based on the objective function, UAV state model, and pheromone communication between ant colonies, use the ant colony algorithm to solve the optimal path for the UAV swarm to search in the target search area, and perform Bézier curve path smoothing optimization on the optimal path.

[0039] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0040] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more digital signal processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0041] The modules described above can be connected or communicate with each other via wired or wireless connections. Wired connections may include metal cables, optical fibers, hybrid cables, or any combination thereof. Wireless connections may include connections via LAN, WAN, Bluetooth, ZigBee, or NFC, or any combination thereof. Two or more modules can be combined into a single module, and any module can be divided into two or more units. Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here.

[0042] It should be noted that these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Furthermore, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Additionally, these modules can be integrated together to form a System-on-a-Chip (SOC).

[0043] The electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, Near Field Communication (NFC), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0044] The present invention also provides a program product, such as a computer-readable storage medium, including a program that, when executed by a processor, is used to perform the above-described method embodiments.

[0045] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0046] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0047] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0048] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for collaborative dynamic target search in marine areas by unmanned aerial vehicle (UAV) swarms based on an improved ant colony algorithm, characterized in that, The methods include: Step S1: Determine the target search area corresponding to the dynamic target to be tracked, and grid the target search area; Step S2: Establish a UAV state model and a radar detection probability model for each UAV in the UAV swarm. The UAV state model is used to characterize the UAV's heading, and the radar detection probability model is used to characterize the probability that the radar configured on the UAV will detect a dynamic target. Step S3: Establish a target probability model that represents the probability of the dynamic target to be tracked appearing in each grid of the target search area; Step S4: Construct an objective function based on the target probability model and the radar detection probability model; construct a corresponding ant colony for each UAV; based on the objective function, the UAV state model, and the communication of pheromones among the ant colonies, use the ant colony algorithm to solve the optimal path for the UAV swarm to search in the target search area, and perform Bézier curve path smoothing optimization on the optimal path.

2. The method as described in claim 1, characterized in that, In step S1, the target search area where the target may appear is set as... The sea area and The unit is km, according to interval Divide the square grid in this way.

3. The method as described in claim 1, characterized in that, In step S2, the UAV state model is as follows: ,in, The heading of the drone during the search process is represented by numbers 0 to 7, which represent due north, northeast, due east, southeast, due south, southwest, due west, and northwest, respectively.

4. The method as described in claim 3, characterized in that, The radar detection probability model is established as follows: Where d is the distance between the UAV and the dynamic target to be tracked. R is the detection index of the UAV's airborne radar, and R is the maximum detection range of the UAV.

5. The method as described in claim 4, characterized in that, In step S3, the target probability model is established as follows: in, For dynamic targets to appear at grid coordinates The probability of the grid. The network coordinates are The heat value of the grid is calculated as follows: within a historical period, the target's position in the grid at coordinates […]. The measure of the probability of occurrences within a circle with a radius of 5 km and a grid center is normalized and converted into a probability. The probability is then expressed as the network coordinates. The heat value of the grid.

6. The method as described in claim 5, characterized in that, In step S4, an objective function is constructed based on the target probability model and the radar detection probability model, wherein the objective function is: Where J is the objective function, Search the target sea area; For grid coordinates, Coordinates of the drone n Number of times the target is imaged. N1 Total number of imaging attempts for the target For the dynamic target to be tracked in The probability of occurrence d The distance between the drone and the moving target to be tracked. This refers to the detection index of the UAV's airborne radar. R This represents the maximum detection range of the drone. Let n be the radar detection probability during the nth imaging. This is the radar detection probability expression.

7. The method as described in claim 6, characterized in that, The ant colony algorithm solves the problem in parallel for each ant colony. When solving for one ant colony: Perform pheromone initialization and initialization of the starting position of each ant, including: Pheromones are distributed within each grid cell, and the initial value of the pheromone within each grid cell is... ; Randomly initialize the starting position of the ant. The ant's search range is the grid adjacent to its current grid that meets the maneuvering conditions. The grid that meets the maneuvering conditions is the grid that the ant can reach by moving straight, turning 45 degrees to the left, or turning 45 degrees to the right. Determine the ant's search path, including: Determine the first a ants of various species b From the current grid u Transfer to grid v probability : In the formula, For the first a ants of various species b In the current grid u The complete set of grids corresponding to the search range. Indicates at time t Grid v Inner a The amount of pheromones remaining in an ant colony For drones in the grid v Search revenue within the grid is equal to the search revenue of the grid. v The probability of a dynamic target appearing in the tracking phase. Indicates except the first a Apart from one ant colony, other ant colonies are in the grid. v The maximum value of residual pheromones in the body. The relative importance of one's own population experience; To determine the relative importance of search revenue, This represents the inhibition coefficient of other ant colony pheromones. For the first a The next step for each ant colony is to allow the selection of a grid set; update the pheromone, including: After all ants in the entire ant colony have completed one iteration, the ant in grid v is... a The pheromone strength of a population is updated using the following formula: In the formula, and The grids before and after the update are shown separately. v Inner a The intensity value of pheromone content in an ant colony. The pheromone evaporation coefficient, For grid v The internal pheromone intensity update value is defined as: In the formula, m The number of ants contained in each population. In the first t After the first search, the second a The first population b Only ants on the grid v The pheromones left inside are defined as: In the formula, For pheromone intensity, For the first a The first population b Only ants in the first t The sum of probabilities of the dynamic target to be tracked appearing within the grid traversed after each search. and They are the first a The first population b Only ants in the first t Within the grid traversed after the first search, the [number]th [unit / item] a The total amount of pheromones in one population and the average total amount of pheromones in other populations.

8. A drone swarm cooperative dynamic target search device for marine areas based on an improved ant colony algorithm, characterized in that, The device includes: Initialization module: Configured to determine the target search area corresponding to the dynamic target to be tracked, and to grid the target search area; The first modeling module is configured to establish a UAV state model and a radar detection probability model for each UAV in the UAV swarm. The UAV state model is used to characterize the UAV's heading, and the radar detection probability model is used to characterize the probability that the radar configured on the UAV will detect a dynamic target. The second modeling module is configured to build a target probability model that represents the probability of a dynamic target being tracked appearing in each grid of the target search area. Path determination module: Configured to construct an objective function based on the target probability model and radar detection probability model; construct a corresponding ant colony for each UAV; based on the objective function, UAV state model, and pheromone communication between ant colonies, use the ant colony algorithm to solve the optimal path for the UAV swarm to search in the target search area, and perform Bézier curve path smoothing optimization on the optimal path.

9. An electronic device, characterized in that, The device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method as described in any one of claims 1-7.

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