针对目标数量多、目标构成复杂环境下雷达目标与敌我识别(Identification Friend or Foe,IFF)点迹关联不准确的问题,提出了一种基于DS(Dempster-Shafer)证据理论的关联方法。基于区间灰数模型完成雷达目标与IFF点迹的灰关联度计算,并据...针对目标数量多、目标构成复杂环境下雷达目标与敌我识别(Identification Friend or Foe,IFF)点迹关联不准确的问题,提出了一种基于DS(Dempster-Shafer)证据理论的关联方法。基于区间灰数模型完成雷达目标与IFF点迹的灰关联度计算,并据此生成DS证据理论中辨识框架的基本概率赋值;利用Dempster规则对证据进行组合,当证据之间存在冲突时采用改进Murphy方法对数据进行处理;最终通过概率转换方法完成关联判决,形成对目标敌我属性的判定。典型场景下的仿真结果表明,该方法能够实现雷达目标与IFF点迹的有效关联,通过多次询问及关联过程,可提升不同场景下的关联正确率。展开更多
最优化决策风格包含最优化目标和最优化策略两维度,两者表现出不同的适应功能。目前研究常依据总均分或单独的维度得分进行分析,这种方法未能充分揭示最优化决策者的复杂性。本文基于动机视角分析了最优化两维度的区别和联系,并由此提...最优化决策风格包含最优化目标和最优化策略两维度,两者表现出不同的适应功能。目前研究常依据总均分或单独的维度得分进行分析,这种方法未能充分揭示最优化决策者的复杂性。本文基于动机视角分析了最优化两维度的区别和联系,并由此提出以人为中心的最优化四象限理论模型。该模型将决策者区分为混合型、目标型、策略型和非最优化四种类型。依据该模型,本文从情绪适应和社会消费行为适应两个角度梳理和论述了不同类型最优化者的适应功能。最后本文讨论了最优化四象限模型的理论与实践价值,并建议将来应从最优化的维度交互、以人为中心视角、纵向分析、影响因素和神经基础等方面进一步探索,以深化对不同最优化类型适应功能的理解。The maximization decision-making style includes two dimensions: the maximization goal and the maximization strategy, each exhibiting distinct adaptive functions. Most studies often rely on scores of the overall or the specific dimension, a method that fails to fully capture the complexity of maximizers. This review analyzes the differences and connections between the two dimensions of maximization from the perspective of motivation, leading to the proposal of a person-centered maximization four-quadrant model. This model categorizes decision-makers into four types: mixed-type, goal-oriented, strategy-oriented, and non-maximizers. Based on this model, this review analyzes the adaptive functions of different types of maximizers from the perspectives of emotional adaptation and social consumption behavior adaptation. Finally, this review discusses the theoretical and practical value of the maximization four-quadrant model, and suggests that it should be further explored from the aspects of dimensional interaction of maximization, person-centered perspective, longitudinal analysis, influencing factors and neurological foundations, to enhance understanding of adaptive functions of different types of maximizers.展开更多
文摘针对目标数量多、目标构成复杂环境下雷达目标与敌我识别(Identification Friend or Foe,IFF)点迹关联不准确的问题,提出了一种基于DS(Dempster-Shafer)证据理论的关联方法。基于区间灰数模型完成雷达目标与IFF点迹的灰关联度计算,并据此生成DS证据理论中辨识框架的基本概率赋值;利用Dempster规则对证据进行组合,当证据之间存在冲突时采用改进Murphy方法对数据进行处理;最终通过概率转换方法完成关联判决,形成对目标敌我属性的判定。典型场景下的仿真结果表明,该方法能够实现雷达目标与IFF点迹的有效关联,通过多次询问及关联过程,可提升不同场景下的关联正确率。
文摘最优化决策风格包含最优化目标和最优化策略两维度,两者表现出不同的适应功能。目前研究常依据总均分或单独的维度得分进行分析,这种方法未能充分揭示最优化决策者的复杂性。本文基于动机视角分析了最优化两维度的区别和联系,并由此提出以人为中心的最优化四象限理论模型。该模型将决策者区分为混合型、目标型、策略型和非最优化四种类型。依据该模型,本文从情绪适应和社会消费行为适应两个角度梳理和论述了不同类型最优化者的适应功能。最后本文讨论了最优化四象限模型的理论与实践价值,并建议将来应从最优化的维度交互、以人为中心视角、纵向分析、影响因素和神经基础等方面进一步探索,以深化对不同最优化类型适应功能的理解。The maximization decision-making style includes two dimensions: the maximization goal and the maximization strategy, each exhibiting distinct adaptive functions. Most studies often rely on scores of the overall or the specific dimension, a method that fails to fully capture the complexity of maximizers. This review analyzes the differences and connections between the two dimensions of maximization from the perspective of motivation, leading to the proposal of a person-centered maximization four-quadrant model. This model categorizes decision-makers into four types: mixed-type, goal-oriented, strategy-oriented, and non-maximizers. Based on this model, this review analyzes the adaptive functions of different types of maximizers from the perspectives of emotional adaptation and social consumption behavior adaptation. Finally, this review discusses the theoretical and practical value of the maximization four-quadrant model, and suggests that it should be further explored from the aspects of dimensional interaction of maximization, person-centered perspective, longitudinal analysis, influencing factors and neurological foundations, to enhance understanding of adaptive functions of different types of maximizers.