Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision u...Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.展开更多
Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary...Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary information. Therefore, it is highly valuable to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the overall performance of the remote images, which are very useful for human visual perception and image processing task. Accordingly, in this paper, we first provide a comprehensive survey of the state of the art of multi-sensor image fusion methods in terms of three aspects: pixel-level fusion, feature-level fusion and decision-level fusion. An overview of existing fusion strategies is then introduced, after which the existing fusion quality measures are summarized. Finally, this review analyzes the development trends in fusion algorithms that may attract researchers to further explore the research in this field.展开更多
The influence of a disturbing gravity field on the impact points of long-range vehicles(LRVs)has become increasingly prominent,which is an important factor affecting the accuracy of impact point prediction(IPP).To ach...The influence of a disturbing gravity field on the impact points of long-range vehicles(LRVs)has become increasingly prominent,which is an important factor affecting the accuracy of impact point prediction(IPP).To achieve high-accuracy and fast IPP for LRVs under the influence of a disturbing gravity field,a data-driven multi-level IPP method is proposed to balance the prediction accuracy and real-time performance.At the first level,the impact point of the current flight state is predicted based on elliptical trajectory theory,and the impact deviation of the elliptical trajectory(ID-ET)is calculated.At the second and third levels,a neural network(NN)model is established to learn the ID-ET caused by the J2 term and re-entry aerodynamic drag as well as that caused by the disturbing gravity field.To improve the NN prediction performance,an auxiliary circle is applied to decouple the ID-ET.To reduce the difficulty of NN learning,a training strategy is designed based on the idea of curriculum learning,which improves training accuracy.At the same time,a hybrid sample generation strategy is proposed to improve the NN generalization ability.A detailed simulation experiment is designed to analyze the advantages and computational complexity of the proposed method.The simulation results showed that the proposed model has a high prediction accuracy,strong generalization ability,and good real-time performance under the influence of the disturbing gravity field and re-entry aerodynamic drag.Among the 317,360 samples contained in the training and test sets,the 3σ prediction error was 6.21 m.On an STM32F407 single-chip microcomputer,the IPP required 3.415 ms.The proposed method can provide support for the design of guidance algorithms and is applicable to engineering practice.展开更多
基金co-supported by the National Natural Science Foundation of China(No.62103432)the China Postdoctoral Science Foundation(No.284881)the Young Talent fund of University Association for Science and Technology in Shaanxi,China(No.20210108)。
文摘Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.
文摘Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary information. Therefore, it is highly valuable to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the overall performance of the remote images, which are very useful for human visual perception and image processing task. Accordingly, in this paper, we first provide a comprehensive survey of the state of the art of multi-sensor image fusion methods in terms of three aspects: pixel-level fusion, feature-level fusion and decision-level fusion. An overview of existing fusion strategies is then introduced, after which the existing fusion quality measures are summarized. Finally, this review analyzes the development trends in fusion algorithms that may attract researchers to further explore the research in this field.
基金co-supported by the National Natural Science Foundation of China(Grant No.62103432)the China Postdoctoral Science Foundation(Grant No.284881)the Young Talent fund of the University Association for Science and Technology in Shaanxi,China(Grant No.20210108).
文摘The influence of a disturbing gravity field on the impact points of long-range vehicles(LRVs)has become increasingly prominent,which is an important factor affecting the accuracy of impact point prediction(IPP).To achieve high-accuracy and fast IPP for LRVs under the influence of a disturbing gravity field,a data-driven multi-level IPP method is proposed to balance the prediction accuracy and real-time performance.At the first level,the impact point of the current flight state is predicted based on elliptical trajectory theory,and the impact deviation of the elliptical trajectory(ID-ET)is calculated.At the second and third levels,a neural network(NN)model is established to learn the ID-ET caused by the J2 term and re-entry aerodynamic drag as well as that caused by the disturbing gravity field.To improve the NN prediction performance,an auxiliary circle is applied to decouple the ID-ET.To reduce the difficulty of NN learning,a training strategy is designed based on the idea of curriculum learning,which improves training accuracy.At the same time,a hybrid sample generation strategy is proposed to improve the NN generalization ability.A detailed simulation experiment is designed to analyze the advantages and computational complexity of the proposed method.The simulation results showed that the proposed model has a high prediction accuracy,strong generalization ability,and good real-time performance under the influence of the disturbing gravity field and re-entry aerodynamic drag.Among the 317,360 samples contained in the training and test sets,the 3σ prediction error was 6.21 m.On an STM32F407 single-chip microcomputer,the IPP required 3.415 ms.The proposed method can provide support for the design of guidance algorithms and is applicable to engineering practice.
