An electromagnetic domain confrontation training method and device for unmanned aerial vehicles based on game theory

Abstract

A method and device for UAV electromagnetic domain confrontation training based on game theory. This method connects UAV entities or digital models and anti-UAV equipment entities or digital models to a virtual electromagnetic environment platform and selects the types of entities or digital models and training scenarios; sets the UAV entity or digital model as the first player and defines its pure strategy space; sets the anti-UAV equipment entity or digital model as the second player and defines its pure strategy space; under the selected training scenario, sequentially selects their pure strategies in the order of first the UAV and then the anti-UAV equipment, and the referee or computer selects the pure strategy space and assigns a utility function; and cycles through in sequence; after stopping the training, the referee or computer outputs the optimal strategy and provides confrontation training suggestions. The present invention can achieve the interactive linkage between virtual entities and physical entities in the electromagnetic domain, greatly reducing the personnel training and confrontation costs.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic spectrum, and particularly relates to a method and device for UAV electromagnetic domain confrontation training based on game theory. Background Art

[0002] In recent years, unmanned aerial vehicles (UAVs) have been widely used in fields such as agriculture, aerial photography, fire fighting, and logistics, providing specific impetus for economic development and the improvement of people's living quality.

[0003] Currently, due to the different levels of difficulty in operating various civilian UAVs and their susceptibility to wireless electromagnetic environment interference, a large number of newly recruited UAV pilots operate expensive UAVs, which are easily damaged or destroyed, resulting in significant economic losses.

[0004] Therefore, how to design and develop a technical solution for UAV electromagnetic domain confrontation training in a virtual environment has become an urgent problem to be solved. Summary of the Invention

[0005] To this end, the present invention provides a method and device for UAV electromagnetic domain confrontation training based on game theory. Through a large-scale virtual electromagnetic environment platform with high fidelity and low latency, the interaction and linkage between virtual entities and physical entities in the electromagnetic domain are realized. UAV pilots or operators of anti-UAV equipment select pure strategy spaces, and the referee / computer assigns a utility function based on equipment parameters and training scenarios. Both the attacking and defending sides can use the utility function to guide their next actions or games. After the end, the referee / computer gives the optimal strategy under the conditions of pure strategy or mixed strategy equilibrium. Under this confrontation training method, the capabilities of UAV pilots or operators of anti-UAV equipment are enhanced, and the costs of personnel training, cultivation, and confrontation are greatly reduced.

[0006] To achieve the above object, the present invention provides the following technical solution: A method for UAV electromagnetic domain confrontation training based on game theory, comprising:

[0007] Connect a UAV entity or a UAV digital model, and an anti-UAV equipment entity or an anti-UAV equipment digital model to a virtual electromagnetic environment platform; select the type of entity or digital model in the virtual electromagnetic environment platform and select a training scenario;

[0008] Set the UAV entity or the UAV digital model as the first player; define the pure strategy space of the first player and determine the navigation and communication mode states of the first player;

[0009] Set the anti-UAV equipment entity or the anti-UAV equipment digital model as the second player; define the pure strategy space of the second player and determine the strategy of the second player to interfere with navigation or communication;

[0010] Under the selected training scenario, control the first player to select the pure strategy of the first player. The referee or computer selects the pure strategy space of the second player according to the equipment parameters and the training scenario, and assigns a utility function. Control the second player to select the pure strategy of the second player. The referee or computer selects the pure strategy space of the first player according to the equipment parameters and the training scenario, and assigns the utility function. Repeat this process in turn until the goal of the first player or the second player is achieved, or the training time is reached, and then stop the training.

[0011] After stopping the training, the referee or computer outputs the optimal strategy under the condition of pure strategy or mixed strategy equilibrium, providing countermeasure training suggestions for the operator.

[0012] As an optimal solution of a game theory-based UAV electromagnetic domain countermeasure training method, during the process of selecting a training scenario from the virtual electromagnetic environment platform, it also includes selecting the corresponding hydrological and meteorological environment for the training scenario.

[0013] As an optimal solution of a game theory-based UAV electromagnetic domain countermeasure training method, during the process of defining the pure strategy space of the first player, the pure strategy space of the first player is defined as:

[0014] S1=(s 11 , s 12 , …, s 1n )

[0015] In the formula, S1 is the pure strategy space of the first player, representing the navigation and communication mode states selected by the UAV, including but not limited to: S 11 represents integrated navigation / air-ground UHF band communication; S 12 represents integrated navigation / air-ground C band communication; S 13 represents integrated navigation / autonomous flight mode; S 14 represents pure inertial navigation / air-satellite Ku band communication.

[0016] As an optimal solution of a game theory-based UAV electromagnetic domain countermeasure training method, during the process of defining the pure strategy space of the second player, the pure strategy space of the second player is defined as:

[0017] S2=(s 21 , s 22 , …, s 2m )

[0018] In the formula, S2 is the pure strategy space of the second player, representing the strategy of the anti-UAV equipment to interfere with navigation or communication, including but not limited to: S 21Indicates interference with satellite navigation / non-interference with aircraft-ground UHF band communication; S 22 Indicates interference with satellite navigation / interference with aircraft-ground C band communication; S 23 Indicates spoofing of satellite navigation / non-interference with communication; S 24 Indicates laser kinetic weapon irradiation / non-interference with communication.

[0019] As an optimal solution of a game theory-based electromagnetic domain confrontation training method for unmanned aerial vehicles, during the process of the referee or computer assigning a utility function according to the equipment parameters and the training scenario, the expression of the utility function is:

[0020] U = (u 11 , u 12 , -, u 1n , u 21 , u 22 , -, u 2m )

[0021] In the formula, u 1i , i = {1, 2,... n} represents the utility function of the first player when choosing an action in its pure strategy space and being affected by the action chosen by the second player in its pure strategy space; u 2j , j = {1, 2,... m} represents the utility function of the second player when choosing an action in its pure strategy space and being affected by the action chosen by the first player in its pure strategy space.

[0022] The present invention also provides a game theory-based electromagnetic domain confrontation training device for unmanned aerial vehicles. Based on the above game theory-based electromagnetic domain confrontation training method for unmanned aerial vehicles, it includes:

[0023] A virtual electromagnetic environment platform access module, which is used to connect an unmanned aerial vehicle entity or an unmanned aerial vehicle digital model, and an anti-unmanned aerial vehicle equipment entity or an anti-unmanned aerial vehicle equipment digital model to the virtual electromagnetic environment platform; select the entity or digital model type in the virtual electromagnetic environment platform and select a training scenario;

[0024] A first player pure strategy space definition module, which is used to set an unmanned aerial vehicle entity or an unmanned aerial vehicle digital model as the first player; define the pure strategy space of the first player and determine the navigation and communication mode states of the first player;

[0025] A second player pure strategy space definition module, which is used to set an anti-unmanned aerial vehicle equipment entity or an anti-unmanned aerial vehicle equipment digital model as the second player; define the pure strategy space of the second player and determine the strategy of the second player to interfere with navigation or communication;

[0026] An adversarial training module, which is used to control the first player to select the pure strategy of the first player in the selected training scenario. The referee or computer selects the pure strategy space of the second player according to the equipment parameters and the training scenario, and assigns a utility function; controls the second player to select the pure strategy of the second player, and the referee or computer selects the pure strategy space of the first player according to the equipment parameters and the training scenario, and assigns the utility function; and loops in sequence until the goal of the first player or the second player is achieved, or the training time is reached, and then stops the training.

[0027] An optimal strategy and training advice output module, which is used to output the optimal strategy under the pure strategy or mixed strategy equilibrium condition by the referee or computer after the training is stopped, and provide adversarial training advice for the operator.

[0028] As a preferred solution of an unmanned aerial vehicle electromagnetic domain adversarial training device based on game theory, in the virtual electromagnetic environment platform access module, during the process of selecting a training scenario from the virtual electromagnetic environment platform, it also includes selecting the corresponding hydrological and meteorological environment for the training scenario.

[0029] As a preferred solution of an unmanned aerial vehicle electromagnetic domain adversarial training device based on game theory, in the first player pure strategy space definition module, during the process of defining the pure strategy space of the first player, the pure strategy space of the first player is defined as:

[0030] S1=(s 11 , s 12 , …, s 1n )

[0031] In the formula, S1 is the pure strategy space of the first player, representing the navigation and communication mode states selected by the unmanned aerial vehicle, including but not limited to: S 11 represents integrated navigation / air-ground UHF band communication; S 12 represents integrated navigation / air-ground C band communication; S 13 represents integrated navigation / autonomous flight mode; S 14 represents pure inertial navigation / air-satellite Ku band communication.

[0032] As a preferred solution of an unmanned aerial vehicle electromagnetic domain adversarial training device based on game theory, in the second player pure strategy space definition module, during the process of defining the pure strategy space of the second player, the pure strategy space of the second player is defined as:

[0033] S2=(s 21 , s 22 , …, s 2m )

[0034] In the formula, \(S_2\) is the pure strategy space of the second player, representing the strategies of the anti-drone equipment to select interference navigation or communication, including but not limited to: \(S\) 21 represents interfering with satellite navigation / not interfering with the aircraft-ground UHF band communication; \(S\) 22 represents interfering with satellite navigation / interfering with the aircraft-ground C band communication; \(S\) 23 represents spoofing satellite navigation / not interfering with communication; \(S\) 24 represents irradiating with a laser kinetic energy weapon / not interfering with communication.

[0035] As an optimal solution of a drone electromagnetic domain confrontation training device based on game theory, in the confrontation training module, during the process of the referee or computer assigning a utility function according to the equipment parameters and the training scenario, the expression of the utility function is:

[0036] \(U=(u\) 11 , \(u\) 12 , …, \(u\) 1n , \(u\) 21 , \(u\) 22 , …, \(u\) 2m )

[0037] In the formula, \(u\) 1i , \(i = \{1, 2,... n\}\) represents the utility function of the first player when choosing an action in its pure strategy space and under the condition that the second player chooses an action in its pure strategy space; \(u\) 2j , \(j = \{1, 2,... m\}\) represents the utility function of the second player when choosing an action in its pure strategy space and under the condition that the first player chooses an action in its pure strategy space.

[0038] The present invention has the following advantages: Connecting a UAV entity or a UAV digital model and an anti-UAV equipment entity or an anti-UAV equipment digital model to a virtual electromagnetic environment platform; Selecting the entity or digital model type in the virtual electromagnetic environment platform and selecting a training scenario; Setting the UAV entity or the UAV digital model as the first player; Defining the pure strategy space of the first player and determining the navigation and communication mode states of the first player; Setting the anti-UAV equipment entity or the anti-UAV equipment digital model as the second player; Defining the pure strategy space of the second player and determining the strategy of the second player to interfere with navigation or communication; Under the selected training scenario, controlling the first player to select the pure strategy of the first player, and the referee or computer selects the pure strategy space of the second player according to the equipment parameters and the training scenario and assigns a utility function; Controlling the second player to select the pure strategy of the second player, and the referee or computer selects the pure strategy space of the first player according to the equipment parameters and the training scenario and assigns the utility function; Repeating in sequence until the goal of the first player or the second player is achieved, or the training time is reached, and stopping the training; After stopping the training, the referee or computer outputs the optimal strategy under the pure strategy or mixed strategy equilibrium condition to provide anti-training suggestions for the operator. Through a large-scale virtual electromagnetic environment platform with high fidelity and low latency, the present invention establishes a game strategy matrix of UAVs and anti-UAV equipment represented by navigation and communication (including three communication links between aircraft, between aircraft and stations, and between aircraft and satellites). The UAV pilot or the anti-UAV equipment operator can select the pure strategy space, and the referee / computer selects each feasible strategy space according to the equipment parameters and the training scenario and assigns a utility function. Both the attacking and defending sides can guide the next action according to the utility function, or the referee / computer gives the optimal strategy under the pure strategy or mixed strategy equilibrium condition after the game ends to guide the improvement of the business capabilities of the UAV pilot or the anti-UAV equipment operator. This method can be applied to the game simulation training of UAV pilots and anti-UAV equipment operators and has strong engineering practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can also be obtained based on the provided drawings.

[0040] The structures, proportions, sizes, etc. shown in this specification are only used to match the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the implementation conditions of the present invention. Therefore, they do not have substantial technical significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0041] Figure 1 It is a schematic flow chart of a method for UAV electromagnetic domain confrontation training based on game theory provided in Embodiment 1 of the present invention;

[0042] Figure 2 It is a schematic specific confrontation training flow chart of a method for UAV electromagnetic domain confrontation training based on game theory provided in Embodiment 1 of the present invention;

[0043] Figure 3 It is a schematic framework diagram of a large-scale virtual electromagnetic environment platform with high fidelity and low latency in a method for UAV electromagnetic domain confrontation training based on game theory provided in Embodiment 1 of the present invention;

[0044] Figure 4 It is a schematic diagram of a training scenario selected in a method for UAV electromagnetic domain confrontation training based on game theory provided in Embodiment 1 of the present invention;

[0045] Figure 5 It is a schematic diagram of the extensive form representation of a two-step game in a method for UAV electromagnetic domain confrontation training based on game theory provided in Embodiment 1 of the present invention;

[0046] Figure 6 It is a schematic diagram of a possible game tree result in a method for UAV electromagnetic domain confrontation training based on game theory provided in Embodiment 1 of the present invention;

[0047] Figure 7 It is a schematic architecture diagram of a device for UAV electromagnetic domain confrontation training based on game theory provided in Embodiment 2 of the present invention. Detailed implementation manners

[0048] The following specific embodiments illustrate the implementation manners of the present invention. Those familiar with this technology can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0049] Embodiment 1

[0050] See Figure 1and Figure 2 , Embodiment 1 of the present invention provides a method for UAV electromagnetic domain confrontation training based on game theory, including the following steps:

[0051] S1. Connect the UAV entity or UAV digital model, and the anti-UAV equipment entity or anti-UAV equipment digital model to the virtual electromagnetic environment platform; select the entity or digital model type in the virtual electromagnetic environment platform, and select the training scenario;

[0052] S2. Set the UAV entity or UAV digital model as the first player; define the pure strategy space of the first player, and determine the navigation and communication mode status of the first player;

[0053] S3. Set the anti-UAV equipment entity or anti-UAV equipment digital model as the second player; define the pure strategy space of the second player, and determine the strategy of the second player to interfere with navigation or communication;

[0054] S4. Under the selected training scenario, control the first player to select the pure strategy of the first player, and the referee or computer selects the pure strategy space of the second player according to the equipment parameters and the training scenario, and assigns a utility function; control the second player to select the pure strategy of the second player, and the referee or computer selects the pure strategy space of the first player according to the equipment parameters and the training scenario, and assigns the utility function; repeat in turn until the goal of the first player or the second player is achieved, or the training time is reached, and stop the training;

[0055] S5. After stopping the training, the referee or computer outputs the optimal strategy under the pure strategy or mixed strategy equilibrium condition, and provides anti-confrontation training suggestions for the operator.

[0056] In this embodiment, in step S1, connect the UAV entity or UAV digital model and the anti-UAV equipment entity or anti-UAV equipment digital model to the virtual electromagnetic environment platform; select the entity or digital model type in the virtual electromagnetic environment platform, and select the training scenario;

[0057] Among them, the framework of the virtual electromagnetic environment platform is as Figure 3As shown, the cloud server provides services for the scenario design, entity design, situation output, and effectiveness evaluation involved in both the attacking and defending sides (the client supporting the drone pilot and the client supporting the anti-drone equipment operator). It provides the status of the entity / digital models and scenario information of both sides through the network communication interface, and conducts interactive adjudication based on the interaction rules. During the interactive adjudication process, it makes a judgment on the game behavior according to the manual instructions and predetermined plans issued by the client supporting the drone pilot, and the action instructions at the lower end of the client supporting the anti-drone equipment operator. At the same time, it uses the simulation engine to obtain the corresponding entity models from the entity model library, conducts time management, and generates attitude playback files / interaction process record files, and conducts effectiveness evaluation based on the generated attitude playback files / interaction process record files. During the process of selecting a training scenario from the virtual electromagnetic environment platform, it also includes selecting flight simulation-related parameters corresponding to the training scenario, as well as hydrological and meteorological environment information, such as Figure 4 As shown, a feasible training scenario configuration is given. Users can select appropriate parameter configurations according to their own needs to meet different training and confrontation requirements.

[0058] In this embodiment, in step S2, the drone entity or the drone digital model is set as the first player; the pure strategy space of the first player is defined, and the navigation and communication mode status of the first player is determined.

[0059] Specifically, the drone entity or the drone digital model is set as the first player (i.e., player 1). During the process of defining the pure strategy space of the first player, the pure strategy space of the first player is defined as:

[0060] S1=(s 11 , s 12 , …, s 1n )

[0061] In the formula, S1 is the pure strategy space of the first player, representing the navigation and communication mode status selected by the drone, including but not limited to: S 11 represents integrated navigation / air-ground UHF band communication; S 12 represents integrated navigation / air-ground C band communication; S 13 represents integrated navigation / autonomous flight mode; S 14 represents pure inertial navigation / air-satellite Ku band communication.

[0062] In this embodiment, in step S3, the anti-drone equipment entity or the anti-drone equipment digital model is set as the second player; the pure strategy space of the second player is defined, and the strategy for interfering with navigation or communication of the second player is determined.

[0063] Specifically, the anti-drone equipment entity or the anti-drone equipment digital model is set as the second player (i.e., Player 2). In the process of defining the pure strategy space of the second player, the pure strategy space of the second player is defined as:

[0064] S2=(s 21 , s 22 , …, s 2m )

[0065] In the formula, S2 is the pure strategy space of the second player, representing the strategy of the anti-drone equipment to interfere with navigation or communication, including but not limited to: S 21 represents interfering with satellite navigation / not interfering with aircraft-ground UHF band communication; S 22 represents interfering with satellite navigation / interfering with aircraft-ground C band communication; S 23 represents spoofing satellite navigation / not interfering with communication; S 24 represents irradiating with a laser kinetic energy weapon / not interfering with communication.

[0066] In this embodiment, in step S4, in the selected training scenario, control the first player to select the pure strategy of the first player, and the referee or the computer selects the pure strategy space of the second player according to the equipment parameters and the training scenario, and assigns a utility function; control the second player to select the pure strategy of the second player, and the referee or the computer selects the pure strategy space of the first player according to the equipment parameters and the training scenario, and assigns the utility function; and cycle in turn until the goal of the first player or the second player is achieved, or the training time is reached, and stop the training;

[0067] Specifically, in the selected training scenario, based on the order of the drone first and then the anti-drone equipment, select their pure strategies in turn. The referee / computer selects the feasible strategy space for each step according to the equipment parameters and the training scenario, and assigns a utility function. The expression of the utility function is:

[0068] U=(u 11 , u 12 , -, u 1n , u 21 , u 22 , …, u 2m )

[0069] In the formula, u 1i , i={1,2,...n} represents the utility function of the first player when choosing an action in its pure strategy space and under the condition of the second player choosing an action in its pure strategy space; u 2j, where \(j = \{1, 2, \cdots, m\}\) represents the utility function of the second player when choosing an action in its pure strategy space under the condition that the first player chooses an action in its pure strategy space.

[0070] In this example, the first player chooses the pure strategy \(S\) 13 , and the second player chooses the pure strategy \(S\) 23 . The extensive form representation of the two-step game is as Figure 5 shown.

[0071] In this embodiment, in step S5, after the training stops, the referee or the computer outputs the optimal strategy under the condition of pure strategy or mixed strategy equilibrium, providing the operator with countermeasure training suggestions.

[0072] Specifically, when the goal of any one of the players is achieved or the training time (finite time) is reached, the training stops, and the extensive form game tree as Figure 6 shown is obtained. Among them, the operation results of the UAV pilot and the anti-UAV equipment operator are as Figure 6 in the \((s 13 , s 23 , \cdots, s 24 ) path. However, the optimal strategy given by the referee / computer under the condition of pure strategy or mixed strategy equilibrium is \((s 13 , s 21 , s 11 , s 22 , \cdots, s 24 ) path. Then, the countermeasure training suggestions for the UAV pilot or the anti-UAV equipment operator in this confrontation are given, and the operator can continue to re-execute step S1 or exit.

[0073] In summary, the present invention connects a UAV entity or a UAV digital model and an anti-UAV equipment entity or an anti-UAV equipment digital model to a virtual electromagnetic environment platform; selects the entity or digital model type in the virtual electromagnetic environment platform and selects a training scenario; sets the UAV entity or the UAV digital model as the first player; defines the pure strategy space of the first player and determines the navigation and communication mode states of the first player; sets the anti-UAV equipment entity or the anti-UAV equipment digital model as the second player; defines the pure strategy space of the second player and determines the strategy of the second player to interfere with navigation or communication; under the selected training scenario, controls the first player to select the pure strategy of the first player, and the referee or computer selects the pure strategy space of the second player according to the equipment parameters and the training scenario and assigns a utility function; controls the second player to select the pure strategy of the second player, and the referee or computer selects the pure strategy space of the first player according to the equipment parameters and the training scenario and assigns the utility function; and loops in sequence until the goal of the first player or the second player is achieved or the training time is reached, and then stops the training; after stopping the training, the referee or computer outputs the optimal strategy under the pure strategy or mixed strategy equilibrium condition to provide countermeasure training suggestions for the operator. Through a large-scale virtual electromagnetic environment platform with high fidelity and low latency, the present invention establishes a game strategy matrix of UAVs and anti-UAV equipment represented by navigation and communication (including three communication links between aircraft, between aircraft and ground stations, and between aircraft and satellites). The UAV pilot or the anti-UAV equipment operator can select the pure strategy space, and the referee / computer selects each feasible strategy space according to the equipment parameters and the training scenario and assigns a utility function. Both the attacking and defending sides can guide the next action according to the utility function or the referee / computer gives the optimal strategy under the pure strategy or mixed strategy equilibrium condition after the game ends, so as to guide the improvement of the business capabilities of the UAV pilot or the anti-UAV equipment operator. This method can be applied to the game simulation training of UAV pilots and anti-UAV equipment operators and has strong engineering practical value.

[0074] It should be noted that the method of the embodiments of the present disclosure can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In such a distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiments of the present disclosure, and these multiple devices will interact with each other to complete the described method.

[0075] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0076] Embodiment 2

[0077] See Figure 7 , Embodiment 2 of the present invention also provides a game theory-based UAV electromagnetic domain countermeasure training device, including:

[0078] A virtual electromagnetic environment platform access module 001, configured to connect a UAV entity or a UAV digital model, and an anti-UAV equipment entity or an anti-UAV equipment digital model to the virtual electromagnetic environment platform; select the entity or digital model type in the virtual electromagnetic environment platform, and select a training scenario;

[0079] A first player pure strategy space definition module 002, configured to set a UAV entity or a UAV digital model as the first player; define the pure strategy space of the first player, and determine the navigation and communication mode states of the first player;

[0080] A second player pure strategy space definition module 003, configured to set an anti-UAV equipment entity or an anti-UAV equipment digital model as the second player; define the pure strategy space of the second player, and determine the strategy of the second player to interfere with navigation or communication;

[0081] An adversarial training module 004, configured to, under the selected training scenario, control the first player to select the pure strategy of the first player, and the referee or the computer selects the pure strategy space of the second player according to the equipment parameters and the training scenario, and assigns a utility function; control the second player to select the pure strategy of the second player, and the referee or the computer selects the pure strategy space of the first player according to the equipment parameters and the training scenario, and assigns the utility function; and cycle in sequence until the goal of the first player or the second player is reached, or the training time is reached, and the training is stopped;

[0082] An optimal strategy and training advice output module 005, configured to, after the training is stopped, the referee or the computer outputs the optimal strategy under the pure strategy or mixed strategy equilibrium condition, and provides adversarial training advice for the operator.

[0083] In this embodiment, in the virtual electromagnetic environment platform access module 001, during the process of selecting a training scenario from the virtual electromagnetic environment platform, it further includes selecting the corresponding hydrological and meteorological environment for the training scenario.

[0084] In this embodiment, in the first player's pure strategy space definition module 002, during the process of defining the first player's pure strategy space, the first player's pure strategy space is defined as:

[0085] S1 = (s 11 , s 12 , …, s 1n )

[0086] Wherein, S1 is the first player's pure strategy space, representing the navigation and communication mode states selected by the UAV, including but not limited to: S 11 represents integrated navigation / air-to-ground UHF band communication; S 12 represents integrated navigation / air-to-ground C band communication; S 13 represents integrated navigation / autonomous flight mode; S 14 represents pure inertial navigation / air-to-satellite Ku band communication.

[0087] In this embodiment, in the second player's pure strategy space definition module 003, during the process of defining the second player's pure strategy space, the second player's pure strategy space is defined as:

[0088] S2 = (s 21 , s 22 , -, s 2m )

[0089] Wherein, S2 is the second player's pure strategy space, representing the strategies of the anti-UAV equipment to interfere with navigation or communication, including but not limited to: S 21 represents interfering with satellite navigation / without interfering with air-to-ground UHF band communication; S 22 represents interfering with satellite navigation / interfering with air-to-ground C band communication; S 23 represents spoofing satellite navigation / without interfering with communication; S 24 represents laser kinetic weapon irradiation / without interfering with communication.

[0090] In this embodiment, in the adversarial training module 004, during the process of the referee or the computer assigning a utility function according to the equipment parameters and the training scenario, the expression of the utility function is:

[0091] U = (u 11 , u 12 , -, u 1n , u 21 , u 22, -, u 2m )

[0092] where u 1i , i = {1, 2,... n} represents the utility function of the first player when choosing an action in its pure strategy space and under the condition of the second player choosing an action in its pure strategy space; u 2j , j = {1, 2,... m} represents the utility function of the second player when choosing an action in its pure strategy space and under the condition of the first player choosing an action in its pure strategy space.

[0093] It should be noted that for the information interaction, execution process, etc. between the above-mentioned system modules, since they are based on the same concept as the method embodiment in Embodiment 1 of the present application, the technical effects brought by them are the same as those of the method embodiment of the present application. For specific content, reference can be made to the description in the method embodiment shown above in the present application, and details will not be elaborated here.

[0094] Embodiment 3

[0095] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code of a method for training UAV electromagnetic domain confrontation based on game theory is stored, and the program code includes instructions for executing a method for training UAV electromagnetic domain confrontation based on game theory according to Embodiment 1 or any possible implementation manner thereof.

[0096] The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center integrating one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state drive (SSD)).

[0097] Embodiment 4

[0098] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0099] The processor and the memory communicate with each other through a bus; the memory stores program instructions executable by the processor, and the processor can execute a method for training UAV electromagnetic domain confrontation based on game theory according to Embodiment 1 or any possible implementation manner thereof by calling the program instructions.

[0100] Specifically, the processor can be implemented by hardware or software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor that realizes its functions by reading software code stored in a memory. The memory can be integrated in the processor or exist independently outside the processor.

[0101] In the above embodiments, they can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.).

[0102] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general computing system. They can be concentrated on a single computing system or distributed on a network composed of multiple computing systems. Optionally, they can be implemented by program code executable by the computing system. Thus, they can be stored in a storage system and executed by the computing system. And in some cases, the steps shown or described can be executed in a different order than here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module for implementation. In this way, the present invention is not limited to any specific combination of hardware and software.

[0103] Although the present invention has been described in detail above with general descriptions and specific embodiments, based on the present invention, some modifications or improvements can be made, which are obvious to those skilled in the art. Therefore, these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of the present invention claimed.

Claims

1. A UAV electromagnetic domain confrontation training method based on game theory, characterized in that: include: Connecting a physical drone or a digital drone model, and a physical anti-drone equipment or a digital anti-drone equipment model to a virtual electromagnetic environment platform; Selecting a physical or digital model type in the virtual electromagnetic environment platform and selecting a training scenario; The drone entity or the drone digital model is set as the first player; the pure strategy space of the first player is defined, and the navigation and communication mode states of the first player are determined; the pure strategy space of the first player is defined as: S1=(s 11 ,s 12 ,…,s 1n ) Where S1 is the pure strategy space of the first player, which represents the navigation and communication mode states selected by the drone, including S 11 Indicates integrated navigation / aircraft-ground UHF band communication; S 12 Indicates integrated navigation / aircraft-ground C-band communication; S 13 Indicates combined navigation / autonomous flight mode; S 14 Indicates pure inertial navigation / aircraft-satellite Ku-band communication; The anti-UAV equipment entity or the anti-UAV equipment digital model is set as the second player; the pure strategy space of the second player is defined, and the strategy of the second player to interfere with navigation or communication is determined; In the selected training scenario, the first player is controlled to select the pure strategy of the first player, and the referee or computer selects the pure strategy space of the second player according to the equipment parameters and the training scenario, and assigns the utility function; the second player is controlled to select the pure strategy of the second player, and the referee or computer selects the pure strategy space of the first player according to the equipment parameters and the training scenario, and assigns the utility function; the process is repeated in sequence until the goal of the first player or the second player is reached, or the training time is reached, and the training is stopped; After stopping the training, the referee or computer outputs the optimal strategy under the pure strategy or mixed strategy equilibrium conditions to provide adversarial training suggestions to the operator.

2. According to the game theory-based UAV electromagnetic domain confrontation training method of claim 1, it is characterized in that: The process of selecting a training scene from the virtual electromagnetic environment platform also includes selecting a hydrological and meteorological environment corresponding to the training scene.

3. The method for electromagnetic domain confrontation training of unmanned aerial vehicles based on game theory according to claim 2 is characterized in that: In the process of defining the pure strategy space of the second player, the pure strategy space of the second player is defined as: S2=(s 21 ,s 22 ,…,s 2m ) Where S2 is the pure strategy space of the second player, which represents the strategy of interference with navigation or communication selected by the anti-UAV equipment, including S 21 Indicates interference with satellite navigation / no interference with ground UHF band communications; S 22 Indicates interference with satellite navigation / jammer C-band communications; S 23 Indicates spoofing satellite navigation / not interfering with communications; S 24 Indicates that the laser kinetic weapon illuminated / did not interfere with communications.

4. The method for electromagnetic domain confrontation training of unmanned aerial vehicles based on game theory according to claim 3 is characterized in that: In the process where the referee or computer assigns the utility function according to the equipment parameters and the training scenario, the utility function is expressed as: U=(u 11 ,in 12 ,…,in 1n ,in 21 ,in 22 ,…,in 2m ) In the formula, u 1i , i = {1, 2, ... n} represents the utility function of the first player in his pure strategy space under the condition that the second player chooses an action in his pure strategy space; u 2j ,j={1,2,...m} represents the utility function of the second player's choice of action in his pure strategy space and under the condition that the first player's choice of action in his pure strategy space.

5. A UAV electromagnetic domain confrontation training device based on game theory, adopting a UAV electromagnetic domain confrontation training method based on game theory according to any one of claims 1 to 4, characterized in that: include: A virtual electromagnetic environment platform access module is used to connect the drone entity or drone digital model, and the anti-drone equipment entity or anti-drone equipment digital model to the virtual electromagnetic environment platform; select the entity or digital model type in the virtual electromagnetic environment platform, and select the training scenario; A first player pure strategy space definition module is used to set the drone entity or the drone digital model as the first player; define the pure strategy space of the first player, and determine the navigation and communication mode status of the first player; A second player pure strategy space definition module is used to set the anti-UAV equipment entity or the anti-UAV equipment digital model as the second player; define the second player pure strategy space, and determine the second player's strategy for interfering with navigation or communication; The adversarial training module is used to control the first player to select the pure strategy of the first player in the selected training scenario, and the referee or computer selects the pure strategy space of the second player according to the equipment parameters and the training scenario, and assigns the utility function; control the second player to select the pure strategy of the second player, and the referee or computer selects the pure strategy space of the first player according to the equipment parameters and the training scenario, and assigns the utility function; and repeat the steps in sequence until the goal of the first player or the second player is achieved, or the training time is reached, and the training is stopped; The optimal strategy and training suggestion output module is used for the referee or computer to output the optimal strategy under the pure strategy or mixed strategy equilibrium conditions after stopping the training, and provide adversarial training suggestions to the operator.

6. The UAV electromagnetic domain confrontation training device based on game theory according to claim 5 is characterized in that: In the virtual electromagnetic environment platform access module, in the process of selecting a training scene from the virtual electromagnetic environment platform, it also includes selecting a hydrological and meteorological environment corresponding to the training scene.

7. The UAV electromagnetic domain confrontation training device based on game theory according to claim 6 is characterized in that: In the first player's pure strategy space definition module, in the process of defining the first player's pure strategy space, the first player's pure strategy space is defined as: S1=(s 11 ,s 12 ,…,s 1n ) Where S1 is the pure strategy space of the first player, which represents the navigation and communication mode states selected by the drone, including S 11 Indicates integrated navigation / aircraft-ground UHF band communication; S 12 Indicates integrated navigation / aircraft-ground C-band communication; S 13 Indicates combined navigation / autonomous flight mode; S 14 Indicates pure inertial navigation / aircraft-satellite Ku-band communication.

8. The UAV electromagnetic domain confrontation training device based on game theory according to claim 7 is characterized in that: In the second player's pure strategy space definition module, in the process of defining the second player's pure strategy space, the second player's pure strategy space is defined as: S2=(s 21 ,s 22 ,…,s 2m ) Where S2 is the pure strategy space of the second player, which represents the strategy of interference with navigation or communication selected by the anti-UAV equipment, including S 21 Indicates interference with satellite navigation / no interference with ground UHF band communications; S 22 Indicates interference with satellite navigation / jammer C-band communications; S 23 Indicates spoofing satellite navigation / not interfering with communications; S 24 Indicates that the laser kinetic weapon illuminated / did not interfere with communications.

9. The UAV electromagnetic domain confrontation training device based on game theory according to claim 8 is characterized in that: In the adversarial training module, when the referee or computer assigns the utility function according to the equipment parameters and the training scenario, the utility function is expressed as: U=(u 11 ,in 12 ,…,in 1n ,in 21 ,in 22 ,…,in 2m ) In the formula, u 1i , i = {1, 2, ... n} represents the utility function of the first player in his pure strategy space under the condition that the second player chooses an action in his pure strategy space; u 2j ,j={1,2,...m} represents the utility function of the second player's choice of action in his pure strategy space and under the condition that the first player's choice of action in his pure strategy space.

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