Unmanned aerial vehicle cooperative area coverage flight path optimization method and device and electronic equipment

Abstract

The invention provides an unmanned aerial vehicle cooperative area coverage flight path optimization method and device and electronic equipment, and relates to the technical field of unmanned aerial vehicle flight path optimization, and the method comprises the steps: analyzing and obtaining flight path control parameters of each unmanned aerial vehicle in a flight path segment in an unmanned aerial vehicle formation, the flight path control parameters comprise a flight distance, a yaw angle variation and a maximum relative position distance between the two unmanned aerial vehicles; acquiring the interception probability of the passive radar system on the radio frequency signal of the unmanned aerial vehicle; establishing a radio frequency stealth-oriented track optimization model based on the track control parameters and the interception probability; improving a particle swarm optimization algorithm; and solving the track optimization model based on an improved particle swarm optimization algorithm. The radio frequency stealth performance of the unmanned aerial vehicle formation is effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of unmanned aerial vehicle (UAV) trajectory optimization, and more specifically, to a method, device and electronic equipment for optimizing the trajectory of an unmanned aerial vehicle (UAV) in a coordinated regional coverage manner. Background Art

[0002] In regional coverage missions, multi-UAV formations are highly maneuverable and robust, and can quickly collect intelligence on the target area and respond in real time to environmental changes. With the in-depth application of electromagnetic spectrum warfare, UAVs need to avoid being detected and located by radio frequency signals when performing search missions.

[0003] In the context of regional coverage missions, most existing studies have only focused on improving search efficiency or enhancing the ability to avoid obstacles such as complex natural environments, while research on improving the RF stealth performance of drone formations is still lacking. In fact, with the continuous iteration of concepts and technologies related to electromagnetic spectrum warfare, the airborne radar carried by drones has become a key equipment for electromagnetic empowerment, while also providing an opportunity window for drones to be attacked by electronic reconnaissance, active interference, and anti-radiation weapons. If effective countermeasures are not taken, it will restrict the long flight time and high maneuverability of drones, affect the completion of regional coverage missions, and even endanger flight safety. Therefore, when there is a threat of electronic warfare equipment in the mission area, the coordinated trajectory planning of multiple drones cannot unilaterally pursue the highest search efficiency, but also needs to consider its own RF stealth capabilities. Summary of the invention

[0004] The embodiments of the present application provide a method, device and electronic equipment for optimizing the trajectory of cooperative regional coverage of unmanned aerial vehicles to solve the problems existing in the prior art.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0006] According to a first aspect of an embodiment of the present application, a method for optimizing a trajectory of a UAV cooperative regional coverage is provided, comprising: Analyze and obtain the track control parameters of each UAV in the UAV formation within the track segment, wherein the track control parameters include the flight distance, the yaw angle change, and the maximum relative position distance between two UAVs; Obtain the probability of interception of the UAV RF signal by the passive radar system; Establishing a track optimization model for radio frequency stealth based on the track control parameters and the intercept probability; Improved particle swarm optimization algorithm; The trajectory optimization model is solved based on the improved particle swarm optimization algorithm.

[0007] In some embodiments of the present application, based on the above solution, the flight distance is obtained as follows: Determine the flight distance using formula (1); ; (1) in, represents the minimum track length, Indicates the upper limit of flight distance. express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, Indicates the total time, Represents the total number of drones.

[0008] In some embodiments of the present application, based on the above solution, the yaw angle change is obtained as follows: Using formula (2) to determine the yaw angle change; ; (2) in, express Moment Drone The yaw angle, express Moment Drone The yaw angle, Indicates the upper limit of the yaw angle change.

[0009] In some embodiments of the present application, based on the above solution, the maximum relative position distance between the two drones is obtained as follows: Using formula (3), determine the maximum relative position distance between the two drones; ; (3) in, Indicates the safe distance between two drones. express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, Indicates the upper limit of the safe distance between two drones. Represents the total number of drones.

[0010] In some embodiments of the present application, based on the above solution, obtaining the probability of intercepting the UAV radio frequency signal by the passive radar system includes: All drones are considered as equivalent radiation sources with known radiation power; Under the condition that the performance parameters of the warning radar are known, formula (4) is used to confirm Always-on radar About Us The intercept probability of the RF signal ; ; (4) in, represents the complementary error function, For drones exist Equivalent radiated power at the moment; is the transmitting antenna gain; For drones exist The wavelength of the transmitted signal at the time; For warning radar The false alarm probability of is the receiving antenna gain; is the receiver signal processing gain; is the Boltzmann constant; is the receiver noise temperature; is the receiver bandwidth; is the receiver noise factor; Indicates drone With warning radar The distance between Indicates the time, Indicates the radar number.

[0011] In some embodiments of the present application, based on the aforementioned solution, the track optimization model for radio frequency stealth is established based on the track control parameter and the intercept probability, including: Obtain the expression formula for the flight distance, the expression formula for the change in yaw angle, the expression formula for the maximum relative position distance between two UAVs, and the expression formula for the probability of intercepting the UAV radio frequency signal by the passive radar system; The track optimization model is established based on the expression formula of the flight distance, the expression formula of the yaw angle change, the expression formula of the maximum relative position distance between the two UAVs, and the expression formula of the passive radar system's interception probability of the UAV radio frequency signal. : ; (5) Among them, the vector , and Respectively represent The number of drones in the drone formation at any moment Axis coordinates, Axis coordinates and yaw angle; is the preset intercept probability threshold, which characterizes the formation's radio frequency stealth performance; Represents the objective function.

[0012] In some embodiments of the present application, based on the above scheme, the expression of the objective function is as follows: ; (6) in, Indicates the total area of ​​the target area; Indicates that all airborne radars in the formation are The scan area generated at the moment is The effective search area at time.

[0013] In some embodiments of the present application, based on the above scheme, the improved particle swarm optimization algorithm includes: Initialize the solution set using chaotic sequence; Introduced Levy flight optimization update mode; Design a dynamic inertia weight function.

[0014] According to a second aspect of an embodiment of the present application, a UAV cooperative regional coverage trajectory optimization device is provided, comprising: A first acquisition unit is used to analyze and acquire the track control parameters of each UAV in the UAV formation within the track segment, wherein the track control parameters include the flight distance, the yaw angle change, and the maximum relative position distance between two UAVs; The second acquisition unit is used to obtain the interception probability of the passive radar system for the UAV radio frequency signal; An establishing unit, configured to establish a track optimization model for radio frequency stealth based on the track control parameter and the intercept probability; Improvement unit, used to improve the particle swarm optimization algorithm; The solving unit is used to solve the trajectory optimization model based on the improved particle swarm optimization algorithm.

[0015] According to a third aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer executes the method according to the first aspect.

[0016] According to a fourth aspect of the embodiments of the present application, there is provided an electronic device, including a memory and a processor; The memory is used to store computer instructions; The processor is used to call the computer instructions stored in the memory so that the electronic device executes the method as described in the first aspect.

[0017] The technical solution of the present application first analyzes the trajectory control parameters of multi-UAV collaboration, takes the probability of RF signal interception as an indicator for evaluating RF stealth performance, and constructs a trajectory optimization model for RF stealth on the basis of satisfying trajectory control constraints and interception probability threshold limits; then, by introducing a chaotic sequence initialization solution set and a Levy flight mechanism to update the particle state, the particle swarm optimization algorithm is improved in combination with a dynamic inertia weight function, thereby realizing the solution of the collaborative area coverage trajectory optimization model, and effectively improving the RF stealth performance of the UAV formation.

[0018] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 A schematic diagram of a flow chart of a method for optimizing a UAV cooperative regional coverage trajectory according to an embodiment of the present application is shown; Figure 2 A schematic diagram of a multi-machine collaborative search situation according to an embodiment of the present application is shown; Figure 3 A schematic diagram of track control and maximum scanning area according to an embodiment of the present application is shown; Figure 4A block diagram of a UAV cooperative area coverage trajectory optimization device according to an embodiment of the present application is shown; Figure 5 A block diagram of an electronic device according to an embodiment of the present application is shown; Figure 6 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0020] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0021] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0022] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0023] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0024] It should be noted that the “plurality” mentioned in this application refers to two or more.

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Some embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0027] Combining the existing research foundation, this application studies the problem of multi-UAV collaborative trajectory planning for RF stealth on the basis of traditional area coverage search tasks. First, for the problem of multi-UAV collaboration, the trajectory control parameters are analyzed and a passive radar detection model is established. The front-end interception probability of the passive radar system for the UAV RF signal is used as the evaluation index of RF stealth performance; then, with the constraints of satisfying dynamic restrictions, flight safety, communication distance and a pre-set interception probability threshold, an optimization model is constructed for the purpose of improving search efficiency; finally, the improved particle swarm optimization algorithm is used to solve the optimization model.

[0028] For details, see Figure 1 , shows a flow chart of a method for optimizing the trajectory of a UAV collaborative regional coverage according to an embodiment of the present application.

[0029] like Figure 1 As shown, a method for optimizing the trajectory of cooperative regional coverage of unmanned aerial vehicles is presented, which specifically includes steps S100 to S500.

[0030] refer to Figure 1 , step S100, analyzing and obtaining the track control parameters of each UAV in the UAV formation within the track segment, wherein the track control parameters include the flight distance, the yaw angle change, and the maximum relative position distance between two UAVs.

[0031] In some feasible embodiments, based on the above solution, the flight distance is obtained as follows: Determine the flight distance using formula (1); ; (1) in, represents the minimum track length, Indicates the upper limit of flight distance. express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, Indicates the total time, Represents the total number of drones.

[0032] It should be noted that the flight distance is greater than or equal to the minimum track length and less than the upper limit of the flight distance.

[0033] In some feasible embodiments, based on the above solution, the yaw angle change is obtained as follows: Using formula (2) to determine the yaw angle change; ; (2) in, express Moment Drone The yaw angle, express Moment Drone The yaw angle, Indicates the upper limit of the yaw angle change.

[0034] In some feasible embodiments, based on the above solution, the maximum relative position distance between the two drones is obtained as follows: Using formula (3), determine the maximum relative position distance between the two drones; ; (3) in, Indicates the safe distance between two drones. express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, Indicates the upper limit of the safe distance between two drones. Represents the total number of drones.

[0035] It should be noted that the maximum relative position distance between two UAVs should be able to ensure that any two UAVs will not collide or lose connection during the flight, so the maximum relative position distance between the two UAVs can also be considered as the flight safety and communication constraint of the UAV formation.

[0036] Exemplarily, the specific process of step S100 is as follows: Consider the problem of multi-machine cooperative area coverage in a two-dimensional plane: Assume UAVs perform collaborative search missions in the target area. It is known that there are Passive warning radar, enemy and friendly situation Figure 2 For ease of description, the formation drones are numbered as , and number the target area warning radar as In addition, to simplify the system model, the following assumptions are further given: (1) The UAVs in the formation are homogeneous and carry airborne radars, and the communication conditions of the formation are ideal (radar search information is intact after communication); (2) The warning radar intelligence in the target area is a priori information, that is, the number of radars, deployment locations and performance parameters are known; (3) The information contained in the target area is evenly distributed, and the amount of information contained in the scanned area does not change dynamically with time or environment.

[0037] Assumptions k Moment Drone Located at point , the rectangular coordinate system X The axis is set to the earth axis, and the current yaw angle is defined as (the angle between the fuselage axis and the earth's axis), satisfying In fact, the scanning coverage is not only affected by the motion state of the UAV, but also constrained by the maximum search distance of the airborne radar. The airborne radar k The maximum search distance at time is , the radiation range is (a sector area with the nose direction as the axis of symmetry), the geometric relationship of the above parameters is as follows Figure 3 shown.

[0038] Since the trajectory planning problem under multi-machine coordination requires a lot of computation, solving the complete formation trajectory will face the problems of insufficient computing power and reduced real-time performance. Therefore, this application optimizes the trajectory control strategy at multiple discrete moments in order to obtain a rough trajectory of the UAV formation. In this application, it is assumed that the time step between adjacent moments is , and its value range can be configured according to computer performance and task requirements.

[0039] Constrained by its own aerodynamic performance, the UAV is in a There are upper limits on the flight distance and yaw angle change over time, which are expressed as constants. and To ensure the flight safety of each drone and the entire formation, the flight attitude cannot be changed directly during the flight, and the flight distance in all track segments must be greater than the minimum track length. Therefore, in any track segment, the following conditions must be met:

[0040] In addition, the smaller the change in yaw angle between adjacent track segments, the smoother the flight process, while rapid changes in direction may cause the drone to lose balance.

[20] Therefore, assuming that in a Drones in time The yaw angle change that generates the track segment is subject to the maximum yaw angle change constraint: ; In addition to the flight safety of the drones themselves, when multiple drones are working together, they should also consider the possibility of collision and loss of connection due to long distances. and Assume that the relative position distance at any time is greater than the safety distance , thus effectively preventing collisions caused by maneuvers. To ensure the sustainability of the communication process, the maximum relative position distance between the two drones is limited to , so that the communication domain covers the flight formation. Based on this, the safety and communication constraints of formation flight can be described as: .

[0041] Continue to refer Figure 1 , step S200, obtaining the interception probability of the passive radar system for the UAV radio frequency signal.

[0042] In some feasible embodiments, based on the above solution, obtaining the probability of intercepting the UAV radio frequency signal by the passive radar system includes: All drones are considered as equivalent radiation sources with known radiation power; Under the condition that the performance parameters of the warning radar are known, formula (4) is used to confirm Always-on radar About Us The intercept probability of the RF signal ; ; (4) in, represents the complementary error function, For drones exist Equivalent radiated power at the moment; is the transmitting antenna gain; For drones exist The wavelength of the transmitted signal at the time; For warning radar The false alarm probability of is the receiving antenna gain; is the receiver signal processing gain; is the Boltzmann constant; is the receiver noise temperature; is the receiver bandwidth; is the receiver noise factor; Indicates drone With warning radar The distance between Indicates the time, Indicates the radar number.

[0043] For example, based on the above example, the specific process of step S200 is as follows: In the process of performing multi-aircraft coordinated area coverage missions, if the RF signal of a single drone is intercepted by the enemy, it may cause itself or even the entire formation to be attacked by anti-radiation weapons or electronic interference. In fact, the front-end interception probability of passive radar for RF signals can be used to measure the low interception performance of drones, and then reflect the RF stealth capability of the flight formation. It can be calculated as: ; in, is the radiated power; is the transmitting antenna gain; is the carrier wavelength of the transmitted signal; is the false alarm probability of the warning radar; is the receiving antenna gain; is the receiver signal processing gain; is the Boltzmann constant; is the receiver noise temperature; is the receiver bandwidth; is the receiver noise factor; Represents the distance between the drone and the warning radar; complementary error function Calculated as: ; In addition, to simplify the space electromagnetic model, all drones are considered as equivalent radiation sources with known radiation power. Under the condition that the performance parameters of the warning radar are known, k +1 Always-on Radar About Us The predicted intercept probability of the RF signal can be calculated as: ; in, express k +1 Moment Drone With warning radar Distance between: .

[0044] Continue to refer Figure 1 , step S300, establishing a track optimization model for radio frequency stealth based on the track control parameters and the intercept probability.

[0045] In some feasible embodiments, based on the above solution, the establishing of a track optimization model for radio frequency stealth based on the track control parameters and the intercept probability includes: Obtain the expression formula for the flight distance, the expression formula for the change in yaw angle, the expression formula for the maximum relative position distance between two UAVs, and the expression formula for the probability of intercepting the UAV radio frequency signal by the passive radar system; The track optimization model is established based on the expression formula of the flight distance, the expression formula of the yaw angle change, the expression formula of the maximum relative position distance between the two UAVs, and the expression formula of the passive radar system's interception probability of the UAV radio frequency signal. : ; (5) Among them, the vector , and Respectively represent The number of drones in the drone formation at any moment Axis coordinates, Axis coordinates and yaw angle; is the preset intercept probability threshold, which characterizes the formation's radio frequency stealth performance; Represents the objective function.

[0046] Exemplarily, based on the above example, the specific process of step S300 is as follows: From the perspective of multi-aircraft collaborative area coverage tasks, under ideal conditions, it is only necessary to maximize the search area to achieve optimal mission performance. However, in the context of electronic warfare, we should not only consider the effectiveness of completing the area coverage task, but also ensure that it is not discovered by the enemy, so as to ensure the safety of formation flight. Based on this, the present application constructs a track optimization model for RF stealth, and the optimization direction is: to maximize the regional coverage rate of the UAV formation under the premise of meeting the low interception probability requirements and flight control constraints. Combined with the expression formulas for the flight distance, the yaw angle change, the maximum relative position distance between the two UAVs, and the passive radar system’s interception probability of the UAV radio frequency signal given in the above examples, the track optimization model can be described as:

[0047] Among them, the vector , and Respectively represent k The number of drones in the formation at any time X Axis coordinates, Y Axis coordinates and yaw angle, can be expanded into , , ; is the preset intercept probability threshold, which represents the formation's RF stealth performance; objective function for: ; in, Indicates the total area of ​​the target area; Indicates that all airborne radars in the formation are i The scan area generated at the moment is k The effective search area at the moment (as of k The union area of ​​all scan areas at the moment, excluding i The area of ​​the remaining area of ​​the intersection area of ​​the scan area after the time).

[0048] Continue to refer Figure 1 , step S400, improving the particle swarm optimization algorithm.

[0049] It should be noted that the design idea of ​​the particle swarm optimization algorithm is to let particles move along the solution space to find the optimal solution. The particle swarm optimization algorithm uses each particle as an alternative solution, and the dimension of the particle represents the number of variables. i Each particle mainly follows the historical optimal solution found by itself and the current global optimal solution shared by the population sports.

[0050] Therefore, the particles i The velocity and position iteration equations are: ; ; in, ; Represents particles i The velocity vector of Represents particles i The position vector of represents the inertia weight; is the cognitive impact factor; is the social impact factor; and is a pseudo-random number between 0 and 1 that follows a normal distribution.

[0051] In some feasible embodiments, based on the above scheme, the improved particle swarm optimization algorithm includes: Initialize the solution set using chaotic sequence; Introduced Levy flight optimization update mode; Design a dynamic inertia weight function.

[0052] It is understandable that, although the traditional particle swarm optimization algorithm has many advantages, in practical applications, the diversity of the particle swarm may decrease rapidly as the number of iterations increases, making it easy to fall into local extremes. Therefore, in order to enhance the global search capability of the algorithm, the embodiment of the present application improves the traditional particle swarm optimization algorithm from the above three aspects.

[0053] Specifically, the specific process of initializing the solution set using chaotic sequences is as follows: The traditional particle swarm optimization algorithm uses a random initialization mechanism, which is prone to slow convergence or premature particle problems. Chaotic sequences are highly random and have no rules to follow, and theoretically can traverse the solution space. Therefore, in order to improve population diversity and reduce the probability of falling into local extreme values, the initial population is optimized using a chaotic operator so that the initial solution set can be randomly distributed in the entire solution space. Specifically, the chaotic sequence is generated using Logistic mapping.

[33] : ; in, represents the control parameter; Represents chaotic variables go through n The result after iterations satisfies .when and When , the generated sequence will be completely chaotic, that is, the chaotic variable The trajectory of the iteration After this, the entire solution space can be covered.

[0054] Specifically, the specific process of introducing the Levy flight optimization update mode is as follows: Solve the trajectory optimization model When solving a class of non-convex optimization problems, the particle swarm optimization algorithm can easily fall into a local optimum due to the increased complexity of the solution domain caused by multiple nonlinear constraints. As a powerful tool to avoid premature particle maturation, the step size of Levy flight obeys a non-Gaussian random distribution, which enables it to have a stronger long-distance jumping ability than Brownian motion. By making reasonable use of the long-distance jumping ability of Levy flight, particles can be prompted to jump out of local extreme points and achieve global search.

[0055] Based on this, by calculating the current number of iterations and preset iteration threshold For comparison, If the optimal value obtained by the particle search has not been updated, the update mode is switched to the Levy flight mode. Due to the complexity of the Levy distribution, the Mantegna algorithm is used to simulate the Levy distribution: ; in, represents a random step length; is a constant, usually satisfying ; and All obey the zero-mean normal distribution, specifically: , , standard deviation It can be calculated as: ; in, represents the standard gamma function.

[0056] Therefore, the combined particles i The velocity and position iteration equations, the particle state update process in the Levy flight mode can be described as: ; ; .

[0057] Specifically, the specific process of designing the dynamic inertia weight function is as follows: particle i The inertia weight of the velocity iteration equation It is mainly used to balance the global optimization and local search capabilities of the particle swarm optimization algorithm. It is usually set to a fixed value in traditional particle swarm optimization algorithms. Designed as a dynamic function of the number of iterations: ; in, and Represent the upper and lower boundaries of the inertia weight respectively. According to the above formula, as the number of iterations increases, the inertia weight gradually decreases, so that the approximate position of the optimal solution can be quickly located in the initial stage, and the local position can be accurately searched in the later stage of the search to improve the solution accuracy.

[0058] Continue to refer Figure 1 , step S500, solving the trajectory optimization model based on the improved particle swarm optimization algorithm.

[0059] Exemplarily, the specific process of step S500 is as follows: Step 510: Initialize using chaotic sequence The initial position and velocity of each particle; Step 520: According to the optimization objective function Calculate particle fitness; Step 530: Update the global optimal particle and the individual optimal particle; Step 540: Iteratively update particle position and velocity; if , execute the particle swarm optimization update mode, according to the particle i The velocity and position iteration equations and the dynamic function of the iteration number update the particle position and velocity. ; Else, execute the Levy flight update mode, update the particle position and velocity according to the particle state update process in the Levy flight mode and the dynamic function of the number of iterations, and let ; Step 550: If the result converges or reaches the maximum number of iterations, end the iteration and output the global optimal particle; otherwise, , jump to step 520.

[0060] In summary, this application proposes a method for optimizing the trajectory of cooperative regional coverage of UAVs. Under the conditions of satisfying the trajectory control constraints of the formation and the probability threshold of the RF signal being intercepted, the regional coverage rate of the formation cooperative search is maximized by optimizing the position and heading of the UAVs in continuous trajectory segments. After that, the optimization model is solved by combining the improved particle swarm optimization algorithm, and simulation comparisons are performed according to the number of UAVs in the formation and the requirements of RF stealth performance. Finally, the designed solver is simulated and compared with multiple optimization algorithms under the framework of the search performance driven model. The simulation shows that the proposed method can effectively improve the RF stealth performance of the UAV formation.

[0061] The following describes an apparatus embodiment of the present application, which can be used to execute a method for optimizing the trajectory of a drone coordinated regional coverage in the above-mentioned embodiment of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the above-mentioned method embodiment of the present application.

[0062] Reference Figure 4 As shown, according to an embodiment of the present application, a UAV cooperative regional coverage trajectory optimization device 400 includes: The first acquisition unit 401 is used to analyze and acquire the track control parameters of each UAV in the UAV formation within the track segment, wherein the track control parameters include the flight distance, the yaw angle change, and the maximum relative position distance between two UAVs; The second acquisition unit 402 is used to obtain the interception probability of the passive radar system for the UAV radio frequency signal; An establishing unit 403 is used to establish a track optimization model for radio frequency stealth based on the track control parameter and the intercept probability; An improving unit 404, used for improving a particle swarm optimization algorithm; The solving unit 405 is used to solve the track optimization model based on the improved particle swarm optimization algorithm.

[0063] like Figure 5 As shown, an embodiment of the present application also provides an electronic device 400, including a memory 510, a processor 520, and a computer program 511 stored in the memory 510 and executable on the processor. When the processor 520 executes the computer program 511, the steps of the above-mentioned method for optimizing the trajectory of cooperative regional coverage of unmanned aerial vehicles are implemented.

[0064] Since the electronic device introduced in this embodiment is a device used to implement a drone collaborative area coverage trajectory optimization device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0065] During the specific implementation process, when the computer program 511 is executed by a processor, any implementation method in the embodiments corresponding to the first aspect can be implemented.

[0066] Figure 6 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0067] It should be noted that Figure 6 The computer system 600 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0068] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage part 608 to the random access memory (RAM) 603, such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in the random access memory 603. The central processing unit 601, the read-only memory 602 and the random access memory 603 are connected to each other through a bus 604. The input / output (I / O) interface 605 is also connected to the bus 604.

[0069] The following components are connected to the input / output interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed so that a computer program read therefrom is installed into the storage section 608 as needed.

[0070] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part 609, and / or installed from a removable medium 611. When the computer program is executed by the central processing unit 601, various functions defined in the system of the present application are executed.

[0071] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0072] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0073] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0074] As another aspect, the present application further provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes a method for optimizing the trajectory of a UAV cooperative regional coverage as described in the above embodiment.

[0075] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements a method for optimizing the trajectory of a UAV cooperative regional coverage as described in the above embodiment.

[0076] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0077] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation method of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation method of the present application.

[0078] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary techniques in the art that are not disclosed in the present application. It should be understood that the present application is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for optimizing the trajectory of UAV cooperative regional coverage, characterized in that: include: Analyze and obtain the track control parameters of each UAV in the UAV formation within the track segment, wherein the track control parameters include the flight distance, the yaw angle change, and the maximum relative position distance between two UAVs; Obtain the probability of interception of the UAV RF signal by the passive radar system; Establishing a track optimization model for radio frequency stealth based on the track control parameters and the intercept probability; Improved particle swarm optimization algorithm; The trajectory optimization model is solved based on the improved particle swarm optimization algorithm.

2. The method according to claim 1, characterized in that The flight distance is obtained as follows: Determine the flight distance using formula (1); ； （1） in, represents the minimum track length, Indicates the upper limit of flight distance. express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, Indicates the total time, Represents the total number of drones.

3. The method according to claim 2, characterized in that The method for obtaining the yaw angle variation is as follows: Using formula (2) to determine the yaw angle change; ； （2） in, express Moment Drone The yaw angle, express Moment Drone The yaw angle, Indicates the upper limit of the yaw angle change.

4. The method according to claim 3, characterized in that The maximum relative position distance between the two drones is obtained as follows: Using formula (3), determine the maximum relative position distance between the two drones; ；（3） in, Indicates the safe distance between two drones. express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, express Moment Drone of Axis coordinates, Indicates the upper limit of the safe distance between two drones. Represents the total number of drones.

5. The method according to claim 4, characterized in that The method of obtaining the probability of intercepting the radio frequency signal of the UAV by the passive radar system includes: All drones are considered as equivalent radiation sources with known radiation power; Under the condition that the performance parameters of the warning radar are known, formula (4) is used to confirm Always-on radar About Us The intercept probability of the RF signal ; ；（4） in, represents the complementary error function, For drones exist The equivalent radiated power at the moment; is the transmitting antenna gain; For drones exist The wavelength of the transmitted signal at the time; For warning radar The false alarm probability; is the receiving antenna gain; is the receiver signal processing gain; is the Boltzmann constant; is the receiver noise temperature; is the receiver bandwidth; is the receiver noise factor; Indicates drone With warning radar The distance between Indicates the time, Indicates the radar number.

6. The method according to claim 5, characterized in that The establishing of a track optimization model for radio frequency stealth based on the track control parameters and the interception probability includes: Obtain the expression formula for the flight distance, the expression formula for the change in yaw angle, the expression formula for the maximum relative position distance between two UAVs, and the expression formula for the probability of intercepting the UAV radio frequency signal by the passive radar system; The track optimization model is established based on the expression formula of the flight distance, the expression formula of the yaw angle change, the expression formula of the maximum relative position distance between the two UAVs, and the expression formula of the passive radar system's interception probability of the UAV radio frequency signal. : ；（5） Among them, the vector , and Respectively represent The number of drones in the drone formation at any moment Axis coordinates, Axis coordinates and yaw angle; is the preset intercept probability threshold, which characterizes the formation's radio frequency stealth performance; Represents the objective function.

7. The method according to claim 6, characterized in that The expression of the objective function is as follows: ；（6） in, Indicates the total area of ​​the target region; Indicates that all airborne radars in the formation are The scan area generated at the moment is The effective search area at time.

8. The method according to claim 6, characterized in that The improved particle swarm optimization algorithm comprises: Initialize the solution set using chaotic sequence; Introduced Levy flight optimization update mode; Design a dynamic inertia weight function.

9. A UAV collaborative regional coverage track optimization device, characterized in that: include: A first acquisition unit is used to analyze and acquire the track control parameters of each UAV in the UAV formation within the track segment, wherein the track control parameters include the flight distance, the yaw angle change, and the maximum relative position distance between two UAVs; The second acquisition unit is used to obtain the interception probability of the passive radar system for the UAV radio frequency signal; An establishing unit, configured to establish a track optimization model for radio frequency stealth based on the track control parameter and the intercept probability; Improvement unit, used to improve the particle swarm optimization algorithm; The solving unit is used to solve the trajectory optimization model based on the improved particle swarm optimization algorithm.

10. An electronic device, characterized in that: including memory and processor; The memory is used to store computer instructions; The processor is used to call the computer instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-8.

Images