In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training s...In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.展开更多
In recent years,reinforcement learning-based aerial combat strategy design has shown significant advantages in decision-making efficiency and flexibility.However,improper opponent strategy selection during training ma...In recent years,reinforcement learning-based aerial combat strategy design has shown significant advantages in decision-making efficiency and flexibility.However,improper opponent strategy selection during training may hinder the agent's ability to handle diverse and complex scenarios,reducing the generalization and performance of its strategies.To address this,this paper proposes"IOS-RAFS",a framework that dynamically adjusts opponent levels at different stages to provide a more diverse and adaptive environment.In the IOS-RAFS training framework,IOS refers to intelligent opponent selection,RA represents the"Rule-AI"dual strategy libraries,which are established to enhance the flexibility and diversity of opponent strategies,and FS represents a fuzzy logic-based method for switching between opponent strategy libraries,periodically selecting the strategy library for the next training phase based on training trends and model performance.Finally,the effectiveness of the proposed algorithm is validated through comparisons with the landmark methods in aerial combat,namely,the genetic fuzzy tree(GFT)algorithm and the expert-designed weapon engagement zones(WEZs)algorithm,demonstrating a significant improvement in rewards and achieving a win rate of at least 82.3%.展开更多
基金supported by the National Natural Science Foundation of China(62371049)。
文摘In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.
基金supported in part by the National Natural Science Foundation of China under Grant 62293513/62293510in part by the Natural Science Foundation of Tianjin under Grant 22JCZDCJ00810in part by the Fundamental Research Funds for the Central Universities
文摘In recent years,reinforcement learning-based aerial combat strategy design has shown significant advantages in decision-making efficiency and flexibility.However,improper opponent strategy selection during training may hinder the agent's ability to handle diverse and complex scenarios,reducing the generalization and performance of its strategies.To address this,this paper proposes"IOS-RAFS",a framework that dynamically adjusts opponent levels at different stages to provide a more diverse and adaptive environment.In the IOS-RAFS training framework,IOS refers to intelligent opponent selection,RA represents the"Rule-AI"dual strategy libraries,which are established to enhance the flexibility and diversity of opponent strategies,and FS represents a fuzzy logic-based method for switching between opponent strategy libraries,periodically selecting the strategy library for the next training phase based on training trends and model performance.Finally,the effectiveness of the proposed algorithm is validated through comparisons with the landmark methods in aerial combat,namely,the genetic fuzzy tree(GFT)algorithm and the expert-designed weapon engagement zones(WEZs)algorithm,demonstrating a significant improvement in rewards and achieving a win rate of at least 82.3%.
