To address the issue of incorrect fusion results caused by conflicting evidence due to inaccurate evidence and incomplete recognition frameworks in radar airborne target tactical intention recognition,a spatiotemporal...To address the issue of incorrect fusion results caused by conflicting evidence due to inaccurate evidence and incomplete recognition frameworks in radar airborne target tactical intention recognition,a spatiotemporal evidence fusion algorithm is proposed.To resolve the conflict evidence fusion problem caused by inaccurate evidence,the algorithm performs discounting of evidence from both spatial and temporal dimensions.Spatial discounting is influenced by both inter-evidence inconsistency and intra-evidence inconsistency,while temporal discounting is determined by time intervals and information entropy.For the problem of conflicting evidence fusion due to an incomplete recognition framework,an open recognition architecture based on dynamic composite focal elements is proposed.This approach allocates some conflicting information to temporary composite focal elements,avoiding excessive basic probability assignment(BPA)of the empty set after fusion,which can lead to deviations from the actual fusion results.Simulation experiments comparing various methods indicate that the proposed method can effectively improve target intention recognition accuracy and demonstrates good stability.展开更多
Online accurate recognition of target tactical intention in beyond-visual-range (BVR) air combat is an important basis for deep situational awareness and autonomous air combat decision-making, which can create pre-emp...Online accurate recognition of target tactical intention in beyond-visual-range (BVR) air combat is an important basis for deep situational awareness and autonomous air combat decision-making, which can create pre-emptive tactical opportunities for the fighter to gain air superiority. The existing methods to solve this problem have some defects such as dependence on empirical knowledge, difficulty in interpreting the recognition results, and inability to meet the requirements of actual air combat. So an online hierarchical recognition method for target tactical intention in BVR air combat based on cascaded support vector machine (CSVM) is proposed in this study. Through the mechanism analysis of BVR air combat, the instantaneous and cumulative feature information of target trajectory and relative situation information are introduced successively using online automatic decomposition of target trajectory and hierarchical progression. Then the hierarchical recognition model from target maneuver element, tactical maneuver to tactical intention is constructed. The CSVM algorithm is designed for solving this model, and the computational complexity is decomposed by the cascaded structure to overcome the problems of convergence and timeliness when the dimensions and number of training samples are large. Meanwhile, the recognition result of each layer can be used to support the composition analysis and interpretation of target tactical intention. The simulation results show that the proposed method can effectively realize multi-dimensional online accurate recognition of target tactical intention in BVR air combat.展开更多
In order to improve the accuracy of target intent recognition,a recognition method based on XGBoost(eXtreme Gradient Boosting)decision tree is proposed.This paper adopts relevant data and program of python to calculat...In order to improve the accuracy of target intent recognition,a recognition method based on XGBoost(eXtreme Gradient Boosting)decision tree is proposed.This paper adopts relevant data and program of python to calculate the probability of tactical intention.Then the sequence intention probability is obtained by applying Dempster-Shafer rule of combination.To verify the accuracy of recognition results,we compare the experimental results of this paper with the results in the literatures.The experiment shows that the probability of tactical intention recognition through this method is improved,so this method is feasible.展开更多
基金supported by the Key Research and Development Program of Shaanxi Province(2023-GHZD-33)the Open Project of the State Key Laboratory of Intelligent Game(ZBKF-23-05)the National Nature Science Foundation of China(62003267)。
文摘To address the issue of incorrect fusion results caused by conflicting evidence due to inaccurate evidence and incomplete recognition frameworks in radar airborne target tactical intention recognition,a spatiotemporal evidence fusion algorithm is proposed.To resolve the conflict evidence fusion problem caused by inaccurate evidence,the algorithm performs discounting of evidence from both spatial and temporal dimensions.Spatial discounting is influenced by both inter-evidence inconsistency and intra-evidence inconsistency,while temporal discounting is determined by time intervals and information entropy.For the problem of conflicting evidence fusion due to an incomplete recognition framework,an open recognition architecture based on dynamic composite focal elements is proposed.This approach allocates some conflicting information to temporary composite focal elements,avoiding excessive basic probability assignment(BPA)of the empty set after fusion,which can lead to deviations from the actual fusion results.Simulation experiments comparing various methods indicate that the proposed method can effectively improve target intention recognition accuracy and demonstrates good stability.
基金The authors gratefully acknowledge the support of the National Natural Science Foundation of China under Grant No.62076204 and Grant No.61612385in part by the Postdoctoral Science Foundation of China under Grants No.2021M700337in part by the Fundamental Research Funds for the Central Universities under Grant No.3102019ZX016.
文摘Online accurate recognition of target tactical intention in beyond-visual-range (BVR) air combat is an important basis for deep situational awareness and autonomous air combat decision-making, which can create pre-emptive tactical opportunities for the fighter to gain air superiority. The existing methods to solve this problem have some defects such as dependence on empirical knowledge, difficulty in interpreting the recognition results, and inability to meet the requirements of actual air combat. So an online hierarchical recognition method for target tactical intention in BVR air combat based on cascaded support vector machine (CSVM) is proposed in this study. Through the mechanism analysis of BVR air combat, the instantaneous and cumulative feature information of target trajectory and relative situation information are introduced successively using online automatic decomposition of target trajectory and hierarchical progression. Then the hierarchical recognition model from target maneuver element, tactical maneuver to tactical intention is constructed. The CSVM algorithm is designed for solving this model, and the computational complexity is decomposed by the cascaded structure to overcome the problems of convergence and timeliness when the dimensions and number of training samples are large. Meanwhile, the recognition result of each layer can be used to support the composition analysis and interpretation of target tactical intention. The simulation results show that the proposed method can effectively realize multi-dimensional online accurate recognition of target tactical intention in BVR air combat.
文摘In order to improve the accuracy of target intent recognition,a recognition method based on XGBoost(eXtreme Gradient Boosting)decision tree is proposed.This paper adopts relevant data and program of python to calculate the probability of tactical intention.Then the sequence intention probability is obtained by applying Dempster-Shafer rule of combination.To verify the accuracy of recognition results,we compare the experimental results of this paper with the results in the literatures.The experiment shows that the probability of tactical intention recognition through this method is improved,so this method is feasible.
