CN114860416B - Distributed multi-agent detection task allocation method and device in countermeasure scene - Google Patents

Distributed multi-agent detection task allocation method and device in countermeasure scene Download PDF

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CN114860416B
CN114860416B CN202210633921.6A CN202210633921A CN114860416B CN 114860416 B CN114860416 B CN 114860416B CN 202210633921 A CN202210633921 A CN 202210633921A CN 114860416 B CN114860416 B CN 114860416B
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task allocation
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CN114860416A (en
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刘华平
李阳
张新钰
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Tsinghua University
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
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Abstract

The application discloses a distributed multi-agent detection task allocation method and device in an countermeasure scene, wherein the method comprises the following steps: establishing a multi-agent detection efficiency function according to the detection task allocation weight, the target detector configuration carried by the multi-agent system, the detection capability, the disguise strategy of the target and the multi-agent detection task allocation strategy; establishing a distributed multi-agent detection task allocation model according to preset multi-agent detection capability constraints, target camouflage capability constraints and detection task constraints; and according to the gradient information, alternately solving a task allocation strategy and a corresponding target disguise strategy of the multi-agent system, and generating a distributed multi-agent detection task allocation result. Therefore, the technical problem that the target feature information acquisition amount is reduced due to the fact that the execution result of the detection task is easily influenced by the target camouflage strategy due to the fact that the potential camouflage process of the target is not fully considered by the distributed detection task distribution method in the related art is solved.

Description

Distributed multi-agent detection task allocation method and device in countermeasure scene
Technical Field
The application relates to the technical field of intelligent decision making of multi-agent systems, in particular to a distributed multi-agent detection task distribution method and device in an countermeasure scene.
Background
The task allocation is a key problem of the multi-agent in collaborative target detection, and good task allocation can fully exert the capability advantages of different agents, especially under the countermeasure scene, the target can disguise one kind of characteristic information of the target and cause partial detection means to fail, so that the accurate matching of the agent set and the detection task set according to the type and the capability of the detector carried by the agent is an important basis for acquiring more target characteristic information.
The task allocation method can be divided into two types, namely centralized type and distributed type according to the organization form of the multi-agent system. In the centralized task allocation method, a central node collects information of all agents, calculates and transmits task allocation results to all agents, and the central node can be a certain agent or a central base station in a multi-agent system. In the distributed task allocation method, each agent can independently decide and dynamically adjust the task allocation result according to the state information and the communication result, and compared with the centralized task allocation method, the distributed task allocation method has higher flexibility and stronger robustness and is less influenced by the scale of the agents.
However, the distributed detection task allocation method in the related art does not fully consider the potential camouflage countermeasure process of the target, and the execution result of the detection task is easily affected by the target camouflage strategy and needs to be improved.
Disclosure of Invention
The application provides a distributed multi-agent detection task distribution method and device in a countermeasure scene, which are used for solving the technical problem that the execution result of a detection task is easily influenced by a target camouflage strategy due to the fact that the potential camouflage countermeasure process of a target is not fully considered by the distributed detection task distribution method in the related art, so that the acquisition amount of target characteristic information is reduced.
An embodiment of a first aspect of the present application provides a distributed multi-agent detection task allocation method in an countermeasure scenario, including the following steps: establishing a multi-agent detection efficiency function according to the detection task allocation weight, the target detector configuration carried by the multi-agent system, the detection capability, the disguise strategy of the target and the multi-agent detection task allocation strategy; establishing a distributed multi-agent detection task allocation model according to preset multi-agent detection capability constraints, target camouflage capability constraints and detection task constraints; and based on the distributed multi-agent detection task allocation model, alternately solving a task allocation strategy and a corresponding target camouflage strategy of the multi-agent system according to gradient information to generate a distributed multi-agent detection task allocation result.
Optionally, in one embodiment of the present application, before solving the task allocation policy and the corresponding target camouflage policy of the multi-agent system, the method further includes: setting optimization parameters and termination conditions; and establishing an augmented Lagrangian function of the detection efficiency, and increasing a non-convergence penalty of the detection efficiency function.
Optionally, in one embodiment of the present application, the multi-agent detection efficacy function is:
wherein X is i Representing agent i versus Multi agent System completeEstimating office task allocation results, wherein B represents allocation weight parameters between an intelligent agent and a target, C represents a type of a target detector carried by an intelligent agent system, D represents a target camouflage strategy, E i Representing an M row N column matrix with columns 1 and the rest 0, Λ being the target detection capability parameter of the different detectors.
Optionally, in one embodiment of the present application, the generating a distributed multi-agent detection task allocation result includes: and solving the maximum value of the detection efficiency function under all camouflage strategies by using the multi-agent task allocation model.
Optionally, in an embodiment of the present application, the alternately solving the task allocation policy and the corresponding target disguising policy of the multi-agent system according to the gradient information includes: solving an optimal target disguising strategy under the current detection task allocation strategy; solving each intelligent agent optimal task allocation strategy under the current optimal target disguise strategy in parallel to solve the current detection task allocation strategy and dual parameters under the optimal target disguise strategy in parallel; and solving the original problem residual error and the dual problem residual error based on the dual parameters until the optimization is finished, so that the detection task allocation solution of the multi-agent system is an average value of all agent local detection task allocation solutions.
An embodiment of a second aspect of the present application provides a distributed multi-agent detection task allocation device in an countermeasure scenario, including: the first function building module is used for building a multi-agent detection efficiency function according to the detection task distribution weight, the target detector configuration and detection capability carried by the multi-agent system, the camouflage strategy of the target and the multi-agent detection task distribution strategy; the model building module is used for building a distributed multi-agent detection task distribution model according to preset multi-agent detection capability constraint, target camouflage capability constraint and detection task constraint; the distribution module is used for alternately solving the task distribution strategy of the multi-agent system and the corresponding target disguise strategy according to the gradient information based on the distributed multi-agent detection task distribution model to generate a distributed multi-agent detection task distribution result.
Optionally, in one embodiment of the present application, further includes: the setting module is used for setting optimization parameters and termination conditions; and the second function building module is used for building an extended Lagrangian function of the detection efficiency and increasing a non-convergence penalty of the detection efficiency function.
Optionally, in one embodiment of the present application, the multi-agent detection efficacy function is:
wherein X is i The method comprises the steps of representing estimation of global task allocation results of an intelligent agent i to a multi-intelligent system, wherein B represents allocation weight parameters between the intelligent agent and a target, C represents a type of a target detector carried by the intelligent agent system, D represents a target camouflage strategy and E i Representing an M row N column matrix with columns 1 and the rest 0, Λ being the target detection capability parameter of the different detectors.
Optionally, in one embodiment of the present application, the allocation module includes: and the first solving unit is used for solving the maximum value of the detection efficiency function under all camouflage strategies by utilizing the multi-agent task allocation model.
Optionally, in one embodiment of the present application, the allocation module further includes: the second solving unit is used for solving the optimal target disguising strategy under the current detection task allocation strategy; the third solving unit is used for parallelly solving the optimal task allocation strategy of each intelligent agent under the current optimal target camouflage strategy so as to parallelly solve the dual parameters under the current detection task allocation strategy and the optimal target camouflage strategy; and the distribution unit is used for solving the original problem residual error and the dual problem residual error based on the dual parameters until the optimization is finished, so that the detection task distribution solution of the multi-agent system is an average value of all agent local detection task distribution solutions.
An embodiment of a third aspect of the present application provides an electronic device, including: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the distributed multi-agent detection task allocation method in the countermeasure scene as described in the embodiment.
A fourth aspect of the present application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the distributed multi-agent detection task allocation method in an countermeasure scenario as described in the above embodiments.
According to the method and the device, reasonable distribution of multiple intelligent agents with different detection capacities can be achieved by utilizing the multiple intelligent agent detection efficiency function and the distributed multiple intelligent agent detection task distribution model according to detection task distribution weight, target detector configuration and detection capacity carried by the multiple intelligent agent system, a target camouflage strategy and the multiple intelligent agent detection task distribution strategy, so that the influence of target camouflage on the multiple intelligent agent detection efficiency is reduced to the maximum extent, and further, collection of target characteristic information under the countermeasure environment is maximized. Therefore, the technical problem that the target feature information acquisition amount is reduced due to the fact that the execution result of the detection task is easily influenced by the target camouflage strategy due to the fact that the potential camouflage process of the target is not fully considered by the distributed detection task distribution method in the related art is solved.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a distributed multi-agent detection task allocation method in an countermeasure scenario according to an embodiment of the present application;
FIG. 2 is a flow chart of a method of distributed multi-agent detection task allocation in an countermeasure scenario according to one embodiment of the present application;
fig. 3 is a schematic structural diagram of a distributed multi-agent detection task allocation device in an countermeasure scenario according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
The following describes a distributed multi-agent detection task allocation method and device in an countermeasure scenario according to an embodiment of the present application with reference to the accompanying drawings. Aiming at the technical problem that the acquisition amount of target characteristic information is reduced due to the fact that the execution result of a detection task is easily influenced by a target camouflage strategy due to the fact that a potential camouflage countermeasure process of a target is not fully considered through a distributed detection task distribution method in the related technology mentioned by the background center, the application provides a distributed multi-agent detection task distribution method in a countermeasure scene. Therefore, the technical problem that the target feature information acquisition amount is reduced due to the fact that the execution result of the detection task is easily influenced by the target camouflage strategy due to the fact that the potential camouflage process of the target is not fully considered by the distributed detection task distribution method in the related art is solved.
Specifically, fig. 1 is a flow chart of a distributed multi-agent detection task allocation method in an countermeasure scenario according to an embodiment of the present application.
As shown in fig. 1, the distributed multi-agent detection task allocation method in the countermeasure scene includes the following steps:
in step S101, a multi-agent detection performance function is established according to the detection task allocation weight, the target detector configuration carried by the multi-agent system, the detection capability, the camouflage strategy of the target, and the multi-agent detection task allocation strategy.
In the actual execution process, the embodiment of the application can establish the multi-agent detection efficiency function according to the detection task distribution weight, the target detector configuration and detection capability carried by the multi-agent system, the target camouflage strategy and the multi-agent detection task distribution strategy, so that the distributed multi-agent detection task distribution model is conveniently combined subsequently, the reasonable distribution of the multi-agents with different detection capabilities is realized, the influence of target camouflage on the multi-agent detection efficiency is reduced to the greatest extent, and the collection of target characteristic information under the countermeasure environment is maximized.
The multi-agent system is of a distributed topology structure, and only the detection task distribution result of the communication neighborhood agents can be obtained.
Optionally, in one embodiment of the present application, the multi-agent detection efficacy function is:
wherein X is i The method comprises the steps of representing estimation of global task allocation results of an intelligent agent i to a multi-intelligent system, wherein B represents allocation weight parameters between the intelligent agent and a target, C represents a type of a target detector carried by the intelligent agent system, D represents a target camouflage strategy and E i Representing an M row N column matrix with columns 1 and the rest 0, Λ being the target detection capability parameter of the different detectors.
Specifically, the detection efficiency function of the multi-agent detection task allocation established in the embodiment of the present application may be:
wherein X is i ∈[0,1] M×N Representing the estimation of global task allocation results of agent i to multi-agent system [ X ] i ] mn Representing the probability that target m is assigned to agent n;representing an assigned weight parameter between the agent and the target; c epsilon {0,1 }) S×N Representing the type of object detector carried by the agent system, [ C ]] sn =1 indicates that agent n carries a sensor s; d E [0,1 ]] S×M Representing a target camouflage strategy [ D ]] sm The object m is shown camouflaging the characteristic information itself which can be detected by the sensor s.
In step S102, a distributed multi-agent detection task allocation model is established according to the preset multi-agent detection capability constraint, the disguised capability constraint of the target, and the detection task constraint.
Further, the embodiment of the application can establish a distributed multi-agent detection task allocation model by combining preset multi-agent detection capability constraint, target camouflage capability constraint and detection task constraint.
Wherein, the multi-agent detection capability constraint refers to the same moment, and each agent can execute at most one detection task; the camouflage capability constraint of the target means that the target camouflage at most own class of characteristic information at the same time; detection task constraints refer to all targets being assigned to at least one agent; multi-agent consistency constraints refer to the global task allocation solutions of the individual agents being identical.
Specifically, the embodiment of the application can establish the constraint condition 0.ltoreq.X of the multi-agent detection capability i ≤1， And requires that each agent can perform at most one probing task;
establishing a disguising capability constraint condition D of the target is more than or equal to 0 and less than or equal to 1,and require the goal to disguise one kind of characteristic information of oneself at most;
establishing constraint conditions of detection tasksAnd requires that all targets be assigned to at least one agent.
Further, the embodiment of the application can estimate the detection task distribution result of each intelligent agent to the intelligent agent in the non-communication neighborhood, combine the detection task distribution results of the intelligent agent and the communication neighborhood to form the global estimation of the detection task of the multi-intelligent agent system, and increase the consistency constraint condition of the multi-intelligent agent system at the same timeAnd requires that the global estimates of the probe task assignments of all agents to the multi-agent system remain consistent.
In step S103, based on the distributed multi-agent detection task allocation model, the task allocation policies of the multi-agent system and the corresponding target disguise policies are alternately solved according to the gradient information, and a distributed multi-agent detection task allocation result is generated.
In the actual execution process, the embodiment of the application can distribute the multi-agent detection tasks according to the optimal solution based on the distributed multi-agent detection task distribution model, and alternately solve the task distribution strategy and the corresponding target camouflage strategy of the multi-agent system according to the gradient information, so as to generate a distributed multi-agent detection task distribution result, realize reasonable distribution of multi-agents with different detection capacities, reduce the influence of target camouflage on the multi-agent detection efficiency to the maximum extent, and further maximize the collection of target characteristic information under the countermeasure environment.
Optionally, in one embodiment of the present application, before solving the task allocation policy and the corresponding target disguising policy of the multi-agent system, the method further includes: setting optimization parameters and termination conditions; and establishing an augmented Lagrangian function of the detection efficiency, and increasing a non-convergence penalty of the detection efficiency function.
As a possible implementation manner, the embodiment of the application may set an optimization parameter and a termination condition, and establish an augmented lagrangian function of the detection efficiency, thereby increasing a non-convergence penalty term of the detection efficiency function, and alternately solving a task allocation policy and a corresponding target camouflage policy of the multi-agent system according to gradient information until the termination condition is satisfied.
In particular, embodiments of the present application may add a non-convergence penalty termThe penalty term penalizes allocation results that are not {0,1} to ensure that the distributed task allocation results converge to the integer set {0,1}.
Further, the augmented lagrangian function of the multi-agent detection efficacy function may be:
wherein g (X) i ) Assigning policy X to a target i H (D) is an indication function of a camouflage policy:
optionally, in one embodiment of the present application, generating the distributed multi-agent probe task allocation result includes: and solving the maximum value of the detection efficiency function under all camouflage strategies by utilizing the multi-agent task allocation model.
Further, solving the maximum value of the detection efficacy function under all camouflage strategies by using the multi-agent task allocation model may be:
0≤D≤1，
optionally, in one embodiment of the present application, the alternately solving the task allocation policy and the corresponding target disguising policy of the multi-agent system according to the gradient information includes: solving an optimal target disguising strategy under the current detection task allocation strategy; solving each intelligent agent optimal task allocation strategy under the current optimal target disguise strategy in parallel to solve the dual parameters of the current detection task allocation strategy and the optimal target disguise strategy in parallel; based on the dual parameters, solving the original problem residual error and the dual problem residual error until the optimization is finished, so that the detection task distribution solution of the multi-agent system is an average value of all agent local detection task distribution solutions.
Specifically, the embodiment of the application can initialize the optimization step length rho and initialize the multi-agent detection task parametersB, C, Λ, and setting up multi-agent communication topologyWherein V= {1, …, N } is a multi-agent set, ++> For multi-agent communication edge, initializing detection task allocation strategy { X } i } 0 Initializing a target camouflage strategy D 0 Setting an optimized termination condition E pri 、∈ dual 。
Further, the embodiment of the application can solve the optimal target disguising strategy under the current detection task allocation strategy:
further, the embodiment of the application can solve the optimal task allocation strategy of each agent under the current optimal target disguising strategy in parallel:
wherein,
further, the embodiment of the application can solve the dual parameters of the current detection task allocation strategy and the optimal target disguise strategy in parallel:
further, the embodiment of the application can solve the original problem residual errorDual problem residual->If at the same time satisfy->Is->And stopping the optimization, otherwise, repeating the process until the optimization time is consumed.
Further, the embodiment of the application may enable the detection task allocation solution of the multi-agent system to be an average value of all agent local detection task allocation solutions:
in summary, compared with the related art, the method and the device solve the problem that the camouflage capability of the target in the multi-target detection task affects the detection efficiency, have stronger robustness, are suitable for the multi-agent distributed detection task with the communication topology, have higher convergence rate, and can search reasonable detection task distribution results under the condition that the target is in a pseudo-load in a shorter time.
Specifically, with reference to fig. 2, a working principle of the distributed multi-agent detection task allocation method in the countermeasure scenario in the embodiment of the present application is described in detail in a specific embodiment.
As shown in fig. 2, an embodiment of the present application may include the following steps:
step S201: initializing parameters and setting termination conditions. The embodiment of the application can set multi-agent communication topologyInitializing a detection task allocation weight B, a target detector configuration parameter C carried by a multi-agent system, target detection capability parameters lambda of different detectors and a target camouflage strategy D 0 Multi-agent detection task allocation strategy>
Multi-agent communication topologyRepresenting the communication topology of a multi-agent system, wherein v= {1, …, N } is a multi-agent set, +.>Is a multi-agent communication edge set.
According to the embodiment of the application, according to the communication range of the current multi-agent system, if the distance between the agent i and the agent j is smaller than the communication distance threshold value, the agent i and the agent j are considered to be capable of continuous and stable communication, and the communication topology is considered in the applicationIs a connected graph.
It should be noted that the communication distance threshold may be set by those skilled in the art according to actual situations, and is not particularly limited herein.
The probing task assignment weight B represents the weight of the agent to perform probing tasks, where [ B ]] mn For scalar value, [ B ]] mn Not less than 0 and[B] mn weights indicating that agent n performs probing task mThe size of the detection task can be represented by the weight of the detection task executed by the intelligent agent, wherein the execution cost of the detection task is generated by factors such as the distance between the intelligent agent and the target, and the smaller the weight value is, the higher the execution cost is.
The target detector configuration parameter C represents the type of detector carried by different agents, such as infrared detectors, lidar, cameras, etc. If multiple intelligent systems share S-type detector, get [ C] sn ∈{0,1}，[C] sn =1 means that agent n has an s-type detector, otherwise 0.
The target detection capability parameter Λ of different detectors represents the acquisition capability of the detector for different feature information of the target, and Λ=diag (λ 1 ,…，λ S ) Wherein lambda is s The target detection capability of the s-type detector is represented, the influence of various complex meteorological conditions such as low visibility and night on the detector capability can be represented through the target detection capability parameter lambda, and the larger the parameter value is, the stronger the detection capability is represented.
The object disguise policy D represents the self-characteristic information type of the object disguise corresponding to the object detector type, wherein [ D ]] ms ∈[0,1]The feature information type representing the disguise of the object m is s, and under the disguise strategy, the object detector s cannot detect the object m. In the initialization of the target disguise policy D, embodiments of the present application may use intervals [0,1 ]]Evenly distributed sampling S x M dimension D 0 。
Multi-agent detection task allocation strategy { X ] i [ X ] represents the result of assignment of detection tasks by agent i i ] mn ∈[0,1]Representing the probability that any of the probes m deemed by agent i is assigned to agent n. The embodiment of the application can obtain the multi-agent detection task allocation strategy { X } i Initialization of interval [0,1 ]]Evenly distributed sampling on N mxn dimensions
Step S202: and optimizing a target camouflage strategy. The embodiment of the application can input the current detection task allocation strategyUpdating target disguise policy D k+1 . The target disguising strategy aims at minimizing the detection efficiency function and simultaneously meeting the constraint condition of the target disguising capacity, namely D is more than or equal to 0 and less than or equal to 1, and is more than or equal to 1>In combination with the augmented Lagrangian function, the current probing task allocation strategy can be +.>Substituting the target camouflage optimization function to obtain the target camouflage strategy at the moment k+1.
The target camouflage strategy optimization method is based on an alternate direction multiplier method, and the target camouflage strategy at the moment k+1 is solved iteratively. Specifically, embodiments of the present application may first initialize an optimal target camouflage strategy D at time k k Is of initial value, i.e. D k+1,0 ＝D k The method comprises the steps of carrying out a first treatment on the surface of the Secondly, alternately optimizing the local optimal values of the objective function and the constraint function of the camouflage strategy, wherein the initial value of the local optimal value of the objective function of the camouflage strategy isAnd updated by the following formula:
wherein,U d ＝0 M×N the initial value of the local optimum of the constraint function isAnd updated by the following formula:
prox is a near-end operator, and can be defined as follows:
again, embodiments of the present application may update step t+1
Finally, when the convergence errorAnd +.>For a sufficient time (usually 10 -3 ) Let the target camouflage strategy at time k+1 +.>
Step S203: optimizing the agent detection task allocation strategy. The embodiment of the application can input the target camouflage strategy D of the k+1 steps k+1 Parallel updating k+1 multi-agent detection task allocation strategyThe goal of the multi-agent detection task allocation strategy is to maximize the detection efficiency function while satisfying the multi-agent capability constraint condition 0.ltoreq.X i ≤1,/>Meeting detection task constraint condition |X m | ∞ =1 combining the target camouflage strategy D of k+1 steps with the augmented lagrangian function k+1 Substituting the detection task allocation strategy optimization function to obtain the detection task allocation strategy at the moment k+1.
The detection task allocation strategy optimization method can be based on an alternate direction multiplier method and overlappedSolving the detection task allocation strategy at the moment k+1. Specifically, for agent i, embodiments of the present application may first initialize an optimal probe task allocation policy at time kIs an initial value, i.e.)>Secondly, alternately optimizing the local optimal values of the objective function and the constraint function of the detection task allocation strategy, wherein the initial value of the local optimal value of the objective function allocated by the detection task is +.>And updated by the following formula:
wherein,
the initial value of the local optimal value of the constraint function of the task allocation strategy is detected asAnd may be updated by the following formula:
again, update step t+1
Finally, when the convergence errorAnd +.>For a sufficient time (usually 10 -3 ) Let detection task allocation strategy at time k+1 +.>
The embodiment of the application can solve the solution residual according to the following formulaDual problem residual->
If at the same time satisfyIs->And stopping optimizing, otherwise, jumping to the step until the optimizing time is consumed.
The embodiment of the application can output the optimal detection task allocation strategyCorresponding worst target camouflage strategy D * ＝ k 。
Step S204: and judging whether the optimal maximum step number is reached. If the maximum number of steps is reached, the process proceeds to step S206, and otherwise proceeds to step S205.
Step S205: and judging whether a consistency error and an optimality error threshold condition are met. If yes, the process proceeds to step S206, and otherwise, the process proceeds to step S203.
Step S206: and returning to the multi-agent detection task to distribute the optimal solution.
According to the distributed multi-agent detection task allocation method in the countermeasure scene, which is provided by the embodiment of the application, reasonable allocation of multi-agents with different detection capacities is realized by utilizing the multi-agent detection efficiency function and the distributed multi-agent detection task allocation model according to the detection task allocation weight, the target detector configuration and detection capacity carried by the multi-agent system, the disguise strategy of the target and the multi-agent detection task allocation strategy, so that the influence of target disguise on the multi-agent detection efficiency is reduced to the maximum extent, and the collection of target characteristic information under the countermeasure environment is further maximized. Therefore, the technical problem that the target feature information acquisition amount is reduced due to the fact that the execution result of the detection task is easily influenced by the target camouflage strategy due to the fact that the potential camouflage process of the target is not fully considered by the distributed detection task distribution method in the related art is solved.
Next, a distributed multi-agent detection task allocation device in an countermeasure scenario according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 3 is a block schematic diagram of a distributed multi-agent detection task allocation device in an countermeasure scenario in accordance with an embodiment of the present application.
As shown in fig. 3, the distributed multi-agent detection task allocation device 10 in the countermeasure scene includes: a first function building module 100, a model building module 200, and an assignment module 300.
Specifically, the first function building module 100 is configured to build a multi-agent detection performance function according to the detection task allocation weight, the target detector configuration and detection capability carried by the multi-agent system, the disguise policy of the target, and the multi-agent detection task allocation policy.
The model building module 200 is configured to build a distributed multi-agent detection task allocation model according to preset multi-agent detection capability constraints, target camouflage capability constraints, and detection task constraints.
The distribution module 300 is configured to generate a distributed multi-agent detection task distribution result based on the distributed multi-agent detection task distribution model and by alternately solving a task distribution strategy and a corresponding target disguise strategy of the multi-agent system according to the gradient information.
Optionally, in one embodiment of the present application, the distributed multi-agent detection task allocation device 10 in an countermeasure scenario further includes: a setting module and a second function building module.
The setting module is used for setting the optimization parameters and the termination conditions.
And the second function building module is used for building an extended Lagrangian function of the detection efficiency and increasing a non-convergence penalty of the detection efficiency function.
Optionally, in one embodiment of the present application, the multi-agent detection efficacy function is:
wherein X is i The method comprises the steps of representing estimation of global task allocation results of an intelligent agent i to a multi-intelligent system, wherein B represents allocation weight parameters between the intelligent agent and a target, C represents a type of a target detector carried by the intelligent agent system, D represents a target camouflage strategy and E i Representing an M row N column matrix with columns 1 and the rest 0, Λ being the target detection capability parameter of the different detectors.
Optionally, in one embodiment of the present application, the allocation module 300 includes: a first solving unit.
The first solving unit is used for solving the maximum value of the detection efficiency function under all camouflage strategies by utilizing the multi-agent task allocation model.
Optionally, in one embodiment of the present application, the allocation module 300 further includes: the system comprises a second solving unit, a third solving unit and an allocation unit.
The second solving unit is used for solving the optimal target camouflage strategy under the current detection task allocation strategy.
And the third solving unit is used for parallelly solving the optimal task allocation strategy of each intelligent agent under the current optimal target disguise strategy so as to parallelly solve the dual parameters under the current detection task allocation strategy and the optimal target disguise strategy.
And the distribution unit is used for solving the original problem residual error and the dual problem residual error based on the dual parameters until the optimization is finished, so that the detection task distribution solution of the multi-agent system is an average value of all agent local detection task distribution solutions.
It should be noted that the foregoing explanation of the embodiment of the distributed multi-agent detection task allocation method in the countermeasure scenario is also applicable to the distributed multi-agent detection task allocation device in the countermeasure scenario of the embodiment, and is not repeated herein.
According to the distributed multi-agent detection task distribution device in the countermeasure scene, which is provided by the embodiment of the application, reasonable distribution of multi-agents with different detection capacities can be realized by utilizing the multi-agent detection efficiency function and the distributed multi-agent detection task distribution model according to the detection task distribution weight, the target detector configuration and detection capacity carried by the multi-agent system, the disguise strategy of the target and the multi-agent detection task distribution strategy, so that the influence of target disguise on the multi-agent detection efficiency is reduced to the maximum extent, and the collection of target characteristic information under the countermeasure environment is further maximized. Therefore, the technical problem that the target feature information acquisition amount is reduced due to the fact that the execution result of the detection task is easily influenced by the target camouflage strategy due to the fact that the potential camouflage process of the target is not fully considered by the distributed detection task distribution method in the related art is solved.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
memory 401, processor 402, and a computer program stored on memory 401 and executable on processor 402.
The processor 402 implements the distributed multi-agent detection task allocation method in the countermeasure scenario provided in the above embodiment when executing a program.
Further, the electronic device further includes:
a communication interface 403 for communication between the memory 401 and the processor 402.
A memory 401 for storing a computer program executable on the processor 402.
Memory 401 may comprise high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 401, the processor 402, and the communication interface 403 are implemented independently, the communication interface 403, the memory 401, and the processor 402 may be connected to each other by a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (Peripheral Component, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 4, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a chip, the memory 401, the processor 402, and the communication interface 403 may complete communication with each other through internal interfaces.
The processor 402 may be a central processing unit (Central Processing Unit, abbreviated as CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), or one or more integrated circuits configured to implement embodiments of the present application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the distributed multi-agent detection task allocation method in an countermeasure scenario as described above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims (8)

1. The distributed multi-agent detection task allocation method in the countermeasure scene is characterized by comprising the following steps:
establishing a multi-agent detection efficiency function according to the detection task allocation weight, the target detector configuration carried by the multi-agent system, the detection capability, the disguise strategy of the target and the multi-agent detection task allocation strategy;
establishing a distributed multi-agent detection task allocation model according to preset multi-agent detection capability constraints, target camouflage capability constraints and detection task constraints;
based on the distributed multi-agent detection task allocation model, alternately solving a task allocation strategy and a corresponding target camouflage strategy of the multi-agent system according to gradient information to generate a distributed multi-agent detection task allocation result;
before solving the task allocation strategy and the corresponding target disguising strategy of the multi-agent system, the method further comprises the following steps:
setting optimization parameters and termination conditions;
establishing an augmented Lagrangian function of the detection efficiency, and increasing a non-convergence penalty term of the detection efficiency function;
wherein, the multi-agent detection efficacy function is:
wherein X is i The method comprises the steps of representing estimation of global task allocation results of an intelligent agent i to a multi-intelligent system, wherein B represents allocation weight parameters between the intelligent agent and a target, C represents a type of a target detector carried by the intelligent agent system, D represents a target camouflage strategy and E i Representing an M row N column matrix with columns 1 and the rest 0, Λ being the target detection capability parameter of the different detectors.
2. The method of claim 1, wherein generating a distributed multi-agent probe task allocation result comprises:
and solving the maximum value of the detection efficiency function under all camouflage strategies by using the multi-agent task allocation model.
3. The method according to claim 1 or 2, wherein the alternately solving the task allocation strategy and the corresponding target disguise strategy of the multi-agent system according to the gradient information comprises:
solving an optimal target disguising strategy under the current detection task allocation strategy;
solving each intelligent agent optimal task allocation strategy under the current optimal target disguise strategy in parallel to solve the current detection task allocation strategy and dual parameters under the optimal target disguise strategy in parallel;
and solving the original problem residual error and the dual problem residual error based on the dual parameters until the optimization is finished, so that the detection task allocation solution of the multi-agent system is an average value of all agent local detection task allocation solutions.
4. A distributed multi-agent detection task allocation device in an countermeasure scenario, comprising:
the first function building module is used for building a multi-agent detection efficiency function according to the detection task distribution weight, the target detector configuration and detection capability carried by the multi-agent system, the camouflage strategy of the target and the multi-agent detection task distribution strategy;
the model building module is used for building a distributed multi-agent detection task distribution model according to preset multi-agent detection capability constraint, target camouflage capability constraint and detection task constraint;
the distribution module is used for alternately solving a task distribution strategy and a corresponding target disguise strategy of the multi-agent system according to gradient information based on the distributed multi-agent detection task distribution model to generate a distributed multi-agent detection task distribution result;
the setting module is used for setting optimization parameters and termination conditions;
the second function building module is used for building an augmented Lagrangian function of the detection efficiency and increasing a non-convergence penalty of the detection efficiency function;
wherein, the multi-agent detection efficacy function is:
wherein X is i The method comprises the steps of representing estimation of global task allocation results of an intelligent agent i to a multi-intelligent system, wherein B represents allocation weight parameters between the intelligent agent and a target, C represents a type of a target detector carried by the intelligent agent system, D represents a target camouflage strategy and E i Representing an M row N column matrix with columns 1 and the rest 0, Λ being the target detection capability parameter of the different detectors.
5. The apparatus of claim 4, wherein the allocation module comprises:
and the first solving unit is used for solving the maximum value of the detection efficiency function under all camouflage strategies by utilizing the multi-agent task allocation model.
6. The apparatus of claim 4 or 5, wherein the distribution module further comprises:
the second solving unit is used for solving the optimal target disguising strategy under the current detection task allocation strategy;
the third solving unit is used for parallelly solving the optimal task allocation strategy of each intelligent agent under the current optimal target camouflage strategy so as to parallelly solve the current detection task allocation strategy and the dual parameters under the optimal target camouflage strategy;
and the distribution unit is used for solving the original problem residual error and the dual problem residual error based on the dual parameters until the optimization is finished, so that the detection task distribution solution of the multi-agent system is an average value of all agent local detection task distribution solutions.
7. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the distributed multi-agent detection task allocation method in an countermeasure scenario as claimed in any one of claims 1 to 3.
8. A computer-readable storage medium having stored thereon a computer program, wherein the program is executed by a processor for implementing the distributed multi-agent detection task allocation method in an countermeasure scenario according to any one of claims 1 to 3.
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