126 results about "Air combat" patented technology

The present invention relates to the field of machine learning, and relates to a system for recognizing aircraft maneuvers on the basis of deep learning. The system is aimed at tasks of understanding tactical actions in air combat games, and performs operations of detection, recognition, analysis, etc., for independent and continuous maneuvering actions of an aircraft. The recognition and analysis system comprises a data collection module, a data pre-processing module, a data normalization module, a model training module, a model prediction module, and a result visualization module. Aircraft maneuver trajectory data is collected by means of an aircraft maneuver trajectory collection apparatus in a sample data collection module, and then the collected aircraft maneuver trajectory data is preprocessed in the data preprocessing module and the data normalization module, so that the aircraft maneuver trajectory data meets requirements during model training and prediction. For new continuous-maneuver samples, each independent maneuver in the samples and corresponding start and end time are predicted by using a continuous-maneuver recognition model which is obtained by means of training; and finally, the result visualization module generates recognition visualization results of predicted maneuver samples.

The invention belongs to the field of combat management, and particularly relates to an air real-time combat management system based on hybrid intelligence, which is characterized in that a cross-domain thermodynamic perception situation map and a cross-domain fusion situation map are generated through sensing module multi-modal target data through situation fusion, threat analysis and target sorting; the hierarchical autonomous decision-making module analyzes combat requirements by using an enhanced hierarchical and hierarchical subsystem, constructs and generates a sub-target sequence and an interactive network through target decomposition and a constraint node network, and generates an optimal tactical control instruction set in combination with resource prediction allocation and tactical evaluation; the cross-layer intervention module dynamically adjusts an autonomous decision permission weight and an instruction according to a task risk, a communication state and a decision score, and realizes the control right of a person in a decision ring by using the instruction of a commander as the highest intervention decision instruction of each layer; the analogue simulation module integrates the subsystems and the man-machine constraint boundary, and performs collaborative decision simulation until the standard is reached; and integrated management of real-time sensing of the air combat situation, intelligent hierarchical decision under dynamic constraint, man-machine permission flexible adjustment and collaborative simulation verification is realized.

The invention provides an air battle intelligent decision-making method and device based on course reinforcement learning, equipment and a medium, and relates to the field of reinforcement learning. Comprising the steps of determining corresponding combat advantage indexes based on multiple groups of initial state data; sorting the initial state data based on a combat advantage index, and storing the sorted initial state data into a difficulty-guided state pool; updating the initial state data in the progressive sliding buffer area from the difficulty-guided state pool according to a difficulty increasing mode; dynamically formulating an intermediate task target until a final task target is formulated; sampling current initial state data from the progressive sliding buffer area, and inputting the current initial state data into a strategy network of the SAC model to obtain a current action; and the SAC model is trained at least based on the intermediate task target, the current action and the current initial state data until the final task target is reached, and the trained SAC model is obtained for making an intelligent decision, so that the decision-making ability and the adaptive ability of the aircraft in a complex environment are improved.

The invention discloses an unsupervised unmanned aerial vehicle air combat situation assessment method based on a double attention mechanism and a BiGRU, and belongs to the technical field of crossing of machine learning and modern military. The method comprises the following steps: firstly, designing a clustering method, providing a parameter adaptive density grid clustering method based on a fuzzy theory to process a large-scale target, and establishing an air combat information sample set with time and tactical meanings to facilitate subsequent rapid evaluation; on the basis, an encoder-decoder structure taking BiGRU as a trunk is designed to extract depth information of a historical state and a subsequent flight state of a target in two directions, and a double attention mechanism suitable for time sequence data is improved, so that the model can better pay attention to key parts and key features of an input sequence. According to the method, the generalization ability and the practical application value of an air combat situation evaluation model can be improved, the processing ability and the evaluation precision of situation data are improved, the influence of irrelevant parts in high-dimensional and long-sequence time data on an evaluation result is reduced, and the modern air combat requirement is met.

The invention provides an air combat strategy generation method based on lightweight sequence modeling imitation learning. According to the training method, an air combat decision model based on rules can be quickly migrated into a parameter model in a neural network form. The method comprises the following steps: firstly, constructing a rule-based air combat decision model, and generating high-quality training data through adversarial simulation; and then, the training data is converted into a state-action sequence pair, and the state-action sequence pair is input into a lightweight sequence modeling network based on a Transform architecture for learning and training. According to the method, decision knowledge of a rule model is converted into a neural network model through an imitation learning method, and efficient utilization and flexible generalization of expert knowledge in air combat decision model construction are realized; the model designed by the method can accurately reproduce the decision behavior of the rule model through a small amount of training, and compared with a reinforcement learning method, the strategy construction speed is higher and the strategy performance is more stable.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle path planning method based on deep reinforcement learning. The method comprises the following steps: constructing an unmanned aerial vehicle-missile confrontation environment model for providing reward calculation, termination judgment and a visual interface so as to provide training data and evaluate the path planning performance of a reinforcement learning strategy; an Actor-Critic deep neural network model based on strategy gradient is constructed, the unmanned aerial vehicle state information is taken as input, the maneuvering acceleration is taken as output, and adaptive learning of the unmanned aerial vehicle maneuvering strategy is realized; in the reinforcement learning process, mechanisms such as dominant function normalization, generalized dominant estimation and gradient cutting are introduced, and the training stability and convergence speed are improved; by adopting a layered reward mechanism and an improved PPO algorithm, the method effectively solves the problems of difficulty in training convergence, poor strategy stability and low energy utilization rate of a traditional deep reinforcement learning method in a complex air combat environment, realizes autonomous decision making and optimal avoidance path planning of the unmanned aerial vehicle under a high confrontation condition, and improves the unmanned aerial vehicle experience. And the avoidance success rate and the training efficiency are obviously improved.

The invention discloses an aircraft cluster multi-task scheduling system based on a cooperative game and a working method thereof. The system comprises a simulation environment module, a hierarchical strategy network module, a centralized value network module and a training and execution module. The simulation environment module is used for constructing a multi-agent air combat confrontation environment and generating states, rewards and interaction data required by training; the hierarchical strategy network comprises a shared space-time representation encoder f theta (.), a high-layer strategy network pi H (aHz) and a low-layer strategy network pi L (aLz; aH); the centralized value network is used for receiving global state information in a training stage, estimating the overall return of our agent cluster, calculating a dominant function, and realizing the optimization of the network by minimizing the value loss; and the training and execution module optimizes a centralized value network parameter and hierarchical strategy network parameters theta H and theta L by using a global state St in a centralized training stage, and outputs an air combat decision ai according to local observation independent decisions of each agent in a distributed execution stage.

The invention belongs to the technical field of unmanned fighter fighting, and provides a multi-agent air combat cooperative control method and device based on a state machine-large model. The system comprises an air confrontation environment, an unmanned fighter plane intelligent body, an action finite-state machine, a team tactical state machine and a language large model decision module. The behavior of the unmanned fighter is cooperatively controlled by an action state machine (including take-off, combat and servicing states) and a team tactics state machine (including no tactics, fire concentration, surrounding and cross support), and the team tactics is taken as the criterion during conflict. The large language model is used for reasoning the overall tactics before the battle and predicting the tactical state of the enemy. And in a non-Nash equilibrium state, the system can be traced back to a key node to reset tactics and repeatedly deduce until optimal cooperation is achieved. According to the method, the autonomous decision robustness and tactical cooperation efficiency of the unmanned aerial vehicle cluster in the denial airspace are improved, and a new paradigm of air combat intelligent control is constructed. The invention discloses a multi-agent air combat cooperative control method and device based on a state machine-large model.

The invention discloses a multi-aircraft air combat cooperation method, system and device based on hierarchical deep reinforcement learning, a storage medium, equipment and a computer program product. The method comprises the following steps: acquiring environment perception information and external action decision information sent from an external aircraft; inputting the environment perception information and the external action decision information into a tactical decision network to obtain flight state expectation information of a local aircraft; and inputting the state expectation information and the environment perception information into a control decision network to obtain local action decision information of the local aircraft. The external action information is introduced to enhance the observation content, a hierarchical network structure is adopted to improve the abstract ability of the decision, and closed-loop control from strategy to execution is realized through upper and lower layer collaboration, so that role allocation among aircrafts is more accurate, the collaboration behavior is more efficient, the task completion effect in the multi-aircraft collaboration air combat is improved, and the system performance is improved. And risks of combat faults and resource waste are reduced.

The invention relates to the field of optical imaging and computer vision, discloses a large-view-field binocular image combination dynamic parameter adjusting system, and aims to solve the problems that a traditional head-up display is limited in view field and monochromatic display and information response are lagged. The system comprises a left and right eye image acquisition and preprocessing unit, a target positioning and trajectory prediction unit, a binocular parallax compensation and registration unit, a dynamic fusion display control unit, a color enhancement and information superposition unit, an eye movement tracking feedback unit and a parameter adaptive adjustment unit. Through binocular wide-angle acquisition, three-dimensional target prediction, sub-pixel-level image registration, panoramic stitching fusion, semantic color coding and dynamic parameter adjustment of eye movement driving, 120-degree * 60-degree distortionless panoramic display, multi-source information intelligent layered superposition and visual focus area adaptive enhancement are realized. According to the method, the air combat situation sensing efficiency and the weapon response speed are improved, and the method has high real-time performance (delay of lt, 80ms), strong environmental adaptability and engineering integration.

The invention relates to the field of intelligent decision-making of a big language model, in particular to a decision-to-behavior issuing method in an air combat process based on a big language model, which comprises the following steps: S1, collecting data for fine tuning: data sources comprise actual combat flight logs, simulation experiment data and expert rule data; s2, classifying and processing data for fine tuning: classifying and processing the original data collected in the step S1 to form a data format required by fine tuning of the large language model; s3, fine tuning of the large language model: selecting a proper large language model and a fine tuning tool, and performing fine tuning by using fine tuning data; and S4, issuing a decision to a large language model: for the situation information and a decision instruction, giving unmanned aerial vehicle control behaviors and parameters by the large language model. According to the method, the whole process from decision making to behavior issuing in the air combat process is intelligently upgraded by utilizing the powerful reasoning ability and high operability of the large language model (LLM), and the method has the advantages of low operation threshold and high applicability.

The invention provides an air combat decision-making method based on a position weight speed updating particle swarm algorithm, which comprises the following steps of: firstly, performing problem simplification on a complex air combat scene, establishing a success rate model of our-party and enemy aircraft attack and coping, and establishing and analyzing an index system in the air combat scene by adopting an analytic hierarchy process; the method comprises the following steps: firstly, obtaining an aircraft attack success influence factor and a corresponding weight, then obtaining a simplified objective function in combination with a multi-objective optimization model and a linear weighting method, and finally, introducing position weight information and an optimization speed updating strategy on the basis of a traditional PSO algorithm, and proposing a PW-PSO algorithm. And the air combat decision is solved based on the improved particle swarm optimization (PW-PSO). Through the scheme of the invention, the convergence speed of the algorithm can be effectively improved, the decision-making efficiency is improved, more accurate attack to enemy targets and effective avoidance of own damage are realized, and the reliability and the real-time performance of air combat decision making are remarkably improved.

The invention relates to an aircraft missile avoidance decision-making method based on LSTM and PPO, and the method comprises the following steps: 1, collecting data, and 2, carrying out the normalization processing and feature fusion of the observation information of an aircraft and a missile through a long and short term memory network according to the data collected in simulation, and building the time sequence features of the aircraft and the missile. And step 3, establishing a hierarchical model based on a depth deterministic strategy algorithm in deep reinforcement learning, and controlling the rod amount to realize specific flight control. 4, respectively establishing communication between the client side and the server side according to the defined model, and 5, repeating the steps 1-4 until a PPO algorithm training ending condition is reached, and terminating the training. According to the scheme, the flexibility and the real-time performance of decision making are improved, the dependence on traditional modeling and expert knowledge is reduced, and a more intelligent solution is provided for modern air war.

The invention provides a hierarchical decision-making air combat confrontation system and method based on attack area game, and the system comprises a top-layer tactical design module which guides a first-party aircraft in two air combat confrontation parties to adopt a strategy according to a set tactical, and obtains an event triggering type finite-state machine; the high-level state switching module carries out state switching according to the event triggering type finite-state machine according to the occurred event, and updates the state of the first-party aircraft; the middle-layer situation assessment module obtains the state and observation information of a first-party aircraft from a simulation environment or a real environment, measures the relative advantages between the aircrafts of the two confrontation parties, obtains situation assessment results of the two confrontation parties, and carries out target allocation; and the underlying attack area game module takes the situation assessment results of the two confrontation parties as input according to the target distribution result, and completes the output of the optimal confrontation strategy of the first party through the analysis game of the relative situation. The method can achieve the confrontation game in a complex air combat scene, is high in decision-making efficiency, is high in interpretability, and is high in confrontation winning rate.

The invention relates to the technical field of artificial intelligence, and provides an unmanned aerial vehicle air combat aircraft action decision-making method based on near-end strategy optimization and a gating circulation unit, and the method comprises the steps: constructing a three-dimensional unmanned aerial vehicle air combat simulation environment; an Actor-Critic neural network architecture based on a GRU is constructed, and iterative training is performed on the neural network through a large amount of self-adversarial and learning. According to the scheme provided by the invention, the time sequence feature perception capability is enhanced, and the intelligent agent can better capture and understand the time sequence dependency relationship of the flight state in the air combat environment by introducing the GRU network, such as inferring the target intention or predicting the future situation from the continuous state change, so that a more prospective decision is made. And the method adapts to a complex dynamic environment, wherein the unmanned aerial vehicle air combat environment has high dynamic and uncertainty. According to the method, the unmanned aerial vehicle can gradually adapt to the complex environment and learn an effective confrontation strategy through an online interaction and learning mechanism of reinforcement learning and the processing capability of the GRU on the dynamic sequence.

This invention discloses a distributed MIMO radar resource allocation method for integrated reconnaissance, interception, and communication, belonging to the technical field of radar resource allocation. It aims to solve the performance balance problem under multi-task coordination in distributed MIMO radar air combat scenarios. An integrated reconnaissance, interception, and communication adversarial scenario is constructed, using the lower bound of position estimation error (SPEB), missile detection probability, and inter-node communication capacity as performance evaluation indicators for reconnaissance, interception, and communication, respectively. Then, with the goal of minimizing the total system transmit power, a model is constructed by combining relevant performance constraints and node task rules. Finally, the model is transformed into a mixed-integer second-order cone programming problem, and the non-convex terms are convexified using the McCormick envelope method. A numerical solution is obtained based on the CVX solver in MATLAB, achieving joint optimization of node task allocation and power allocation. This invention reduces the total transmit power while satisfying three types of performance requirements, achieving efficient waveform resource scheduling and improving the practicality and robustness of radar countermeasures.

The invention discloses a load balancing-oriented airborne missile group multipath reliable routing method, which belongs to the technical field of air combat platform group network communication, and comprises multipath routing generation and multipath routing service forwarding of a QoS (Quality of Service)-based comprehensive state model, the route searching comprises the step of searching a first route between a given source node and a target node pair through an OLSR (Open Link Scheduling Route) protocol; all links on the known route are eliminated, and route searching is carried out again; and eliminating the link with the maximum load on the known route, and searching the route again. According to the airborne missile group multipath reliable routing method oriented to load balancing, multipath routing is realized by properly increasing protocol overhead, so that network load balancing is realized. Meanwhile, under the condition of enemy interference, a multi-path routing mode is adopted to replace an interfered link, and reliable transmission of core information of the backbone node is ensured.

This invention discloses an intelligent air combat decision-making method combining imitation learning and reinforcement learning, belonging to the field of air combat. The method includes: processing battlefield situation information through an intelligent air combat decision-making model to obtain decision results for guiding the aircraft. The pre-trained intelligent air combat decision-making model is obtained through the following steps: for coarse-grained sparse expert policy data, a behavior cloning algorithm is used to train a neural network architecture for imitation learning and reinforcement learning to obtain a policy network Q1; the policy network Q1 is used as the initial network in a generative adversarial imitation learning algorithm to perform imitation learning on fine-grained dense expert policies to obtain a policy network Q2; the policy network Q2 is used as the initial network in a reinforcement learning algorithm framework for decision network training, and the policy gradient method is used to train the network until convergence to obtain the intelligent air combat decision-making model. This invention is based on the ability to effectively improve sample utilization and reduce cumulative error.

The present invention relates to a cooperative hunting method for unmanned aerial vehicles of a multi-degree-of-freedom model of multi-agent reinforcement learning. Since a multi-agent reinforcement learning algorithm is used to study the hunting problem of multiple unmanned aerial vehicles, it reflects a more intelligent autonomous decision-making than a traditional mathematical model method or a single-agent reinforcement learning method. At the same time, in the present invention, a multi-unmanned aerial vehicle encirclement strategy deduction method based on reinforcement learning is established, and a multi-degree-of-freedom unmanned aerial vehicle model cluster confrontation strategy is formulated. Due to the use of a multi-degree-of-freedom unmanned aerial vehicle model, a more complex and accurate model update optimization is constructed, which makes up for the shortcomings of the existing method in the multi-agent system air combat confrontation method in complex scenarios and improves the accuracy of the air combat model.

The present application relates to a kind of reinforcement learning model training methods for unmanned aerial vehicle air combat decision-making, including several training rounds, in each training round, including: (1) setting network architecture and network parameters;(2) obtain input data, and input to the reinforcement learning model of current training round, obtain output data;(3) according to the decision data output by reinforcement learning model, the reward function of current training round is calculated, reward function is obtained by the superposition of basic reward and predicted gain reward, wherein, the predicted gain reward is determined by decision difference, the decision difference is the difference between the decision data output by the reinforcement learning model and the pre-determined large language model for the input data;(4) according to the reward function of current training round, adjust the network parameters of reinforcement learning model, obtain the initial network parameters of next training round;(5) return (1) execute next training round until reach the preset stop condition.

The present invention relates to a multi-aircraft air combat decision-making method based on general experience game reinforcement learning. The state space vector of the fighter aircraft is selected as the input of the intelligent agent structure. The intelligent agent includes action and evaluation neural networks, which are LSTM neural networks and fully connected neural networks. The payoff matrix is calculated, and the Nash equilibrium under the current payoff matrix is solved to optimize the new strategy of the k-th fighter aircraft at present. The reinforcement learning algorithm is used for solving, and new intelligent agents are generated and added to the corresponding strategy sets, and the process is repeated until the algorithm reaches the specified number of training iterations. After the entire game framework is trained to the specified number of iterations, the latest trained intelligent agent is extracted, relevant information is input, and maneuvering actions can be output to complete one-step decision-making. The present invention can form complex maneuvering actions and cooperative tactics. Compared with the intelligent agents trained by general multi-intelligent agent reinforcement learning algorithms, the decision-making among multiple aircraft is more collaborative, which also demonstrates the effectiveness of the method of the present invention.

The invention discloses a multi-unmanned aerial vehicle air combat game payment matrix dimensionality reduction method under incomplete information, and belongs to the field of multi-unmanned aerial vehicle air combat dimensionality reduction game decision. An interval matrix game under incomplete information is converted into a matrix game with parameters, then an equivalent determined value matrix game is constructed, an effective convex hull vertex of a row or column vector set of a matrix is found out by using a convex hull vertex algorithm based on a reward value, and a matrix after dimension reduction is established according to the effective convex hull vertex. And then a mapping function is used to establish a relationship between Nash equilibrium of the matrix game before and after dimension reduction, a solution of an equivalent matrix can be obtained by solving the Nash equilibrium of the matrix after dimension reduction, and a Nash equilibrium solution and expected benefits of the interval matrix game are obtained through transformation. Therefore, the problems that the number of the unmanned aerial vehicles is increased, so that dimensionality of a matrix game is rapidly increased, and sensor information is uncertain due to a complex air combat environment are solved.

The application discloses a beyond-visual-range air combat decision-making method based on dynamic fire field and artificial potential field fusion, and relates to the field of aerospace. The method comprises the following steps: establishing a beyond-visual-range air combat simulation environment, modeling enemy and friendly camps in the air combat; grouping the enemy camp in real time; constructing a time-varying killing performance model of a missile to determine a three-view dynamic fire field of an aircraft-missile-target so as to establish an artificial potential field of combat groups, and determining the potential field gradient generated by each combat group in the grouping result; improving a particle updating formula in a particle swarm optimization algorithm by using the potential field gradient generated by the combat groups; solving a target function to determine a maneuvering decision of a friendly aircraft. The improved particle swarm optimization algorithm is obtained by improving the particle updating formula in the particle swarm optimization algorithm by using the potential field gradient generated by all the combat groups in the grouping result. The application can improve the coping ability, autonomous decision-making ability and coping ability in a complex battlefield environment of the beyond-visual-range air combat.

The invention relates to a method for operating an air combat system (2), in which a plurality of data units (16, 16a-f) are connected to one another via a system network (20) in an operational air combat unit (4, 4a-d). In order to improve the reliability of the target guidance of a guided missile (4a), it is proposed that several coupling units (30, 30a-g) of a data pool network (26a-d) additional to the system network (20) each connect at least one data unit (16, 16a-f) to a data core (28) of the data pool network (26a-d).

The invention provides a complex task-oriented agent training method and system, and relates to the technical field of reinforcement learning, and the method comprises the steps: constructing a hierarchical decision framework according to a complex task in an unmanned aerial vehicle air combat confrontation scene, so as to enable the complex task to be decomposed into a plurality of subtasks, and enabling a plurality of agents to execute the subtasks respectively; independently training each layer of agent under the hierarchical decision framework; and after the independent training is completed, part of agents are extracted from different frameworks according to task characteristics to form a joint optimization combination, and joint optimization is performed on the agents in the combination by using a reinforcement learning algorithm. According to the method, in complex scenes such as high-fidelity air battle games and the like, the performance of the intelligent agent in complex game tasks and the autonomous game confrontation ability of the intelligent agent are improved by deeply researching the layered local joint optimization technology.

The present invention discloses a method, system, device and medium for optimizing the training of unmanned aerial vehicle (UAV) air combat decision-making. The method presets a variety of enemy maneuver strategies for enemy UAVs; among them, various enemy maneuver strategies are related but have different complexities; through a deep reinforcement learning model, the UAV of our side is trained to make maneuver decisions against enemy maneuver strategies from simple to complex. The present invention can improve the training effect of the UAV of our side and obtain better maneuver strategies.

The application discloses an aircraft air combat deduction review iteration system, comprising a scenario editing module, a constraint configuration module and a simulation deduction module, wherein the scenario editing module comprises a quick scenario unit and a self-defined scenario unit, the quick scenario unit is used for quickly loading a saved combat scenario, the self-defined scenario unit comprises an environment scenario and a combat unit scenario, the constraint configuration module comprises one-key configuration constraints and self-defined configuration constraints, a set of scenario constraint schemes are generated through the scenario editing module and the constraint configuration module, the simulation deduction module comprises a batch simulation submodule and a data recording submodule, the batch simulation submodule performs simulation deduction based on the configured scenario constraint schemes, human intervention factors can be added at any time during the simulation deduction, a large amount of simulation data is generated for analysis, and the data recording submodule is used for recording data during the simulation deduction of the batch simulation submodule and reviewing the simulation deduction according to needs.

The application discloses a kind of real-time solving method of tactical control distance in air combat simulation environment, and is obtained by generating and training real-time solving model, and the model training process includes: constructing the three-dimensional motion model of beyond-visual-range air combat and air-to-air missile and its constraint model and setting simulation limit condition to obtain offline solving model;With each group of sample motion data, the maximum initial search range of the boundary of tactical control distance is used to obtain the corresponding tactical control distance value by enemy missile and aircraft motion simulation, and then a sample database is constructed;Based on sparse auto-encoding network, an initial real-time solving model is constructed, and a target real-time solving model is obtained by network training using the sample database.The corresponding tactical control distance value is obtained by inputting each group of motion data to be tested into the target real-time solving model, and then the boundary of tactical control distance can be obtained.The application first proposes the concept of tactical control distance and provides theoretical guidance for air combat decision through quantitative model, with high solving accuracy and strong real-time performance.

The application discloses a near-distance air combat pilot task performance prediction method, which predicts the task performance of a near-distance air combat pilot through a task performance prediction model, and the construction method of the task performance prediction model comprises the following steps: acquiring physiological state data, aircraft control data and air combat situation data of the pilot; extracting physiological state deep features, aircraft control deep features and air combat situation deep features from the physiological state data, the aircraft control data and the air combat situation data respectively; inputting the physiological state deep features, the aircraft control deep features and the air combat situation deep features into a cross-modal gated attention fusion module to fuse the physiological state deep features, the aircraft control deep features and the air combat situation deep features, so as to obtain cross-modal fusion features; and inputting the cross-modal fusion features into a full-connection predictor module to output a predicted pilot task performance grade, thereby realizing the prediction of the pilot task performance.

The application discloses a dynamic dimension air combat situation information fusion method based on a Set Transformer, belongs to the field of air combat situation information fusion, and comprises the following steps: fixed dimension and dynamic dimension air combat situation information categories are respectively defined and divided, dynamic dimension information categories in the air combat situation information are respectively expressed as set data, a Set Transformer neural network structure is defined, set data is respectively input into the Set Transformer to obtain set features with a fixed number and dimension, fixed dimension information in the air combat situation information is spliced with the set features with the fixed number and dimension output by the Set Transformer, and the spliced set features are taken as the input of subsequent machine learning technology; the air combat situation information fusion method provided by the application avoids the defect that a traditional feedforward neural network can only process fixed dimension input, realizes lossless and efficient processing of dynamic dimension input, and facilitates expansion of an upper algorithm between any number of formation battles.