Autonomous cooperation of unmanned swarms is the research focus on“new combat forces”and“disruptive technologies”in military fields.The mechanism design is the fundamental way to realize autonomous cooperation.Fac...Autonomous cooperation of unmanned swarms is the research focus on“new combat forces”and“disruptive technologies”in military fields.The mechanism design is the fundamental way to realize autonomous cooperation.Facing the realistic requirements of a swarm network dynamic adjustment under the background of high dynamics and strong confrontation and aiming at the optimization of the coordination level,an adaptive dynamic reconfiguration mechanism of unmanned swarm topology based on an evolutionary game is designed.This paper analyzes military requirements and proposes the basic framework of autonomous cooperation of unmanned swarms,including the emergence of swarm intelligence,information network construction and collaborative mechanism design.Then,based on the framework,the adaptive dynamic reconfiguration mechanism is discussed in detail from two aspects:topology dynamics and strategy dynamics.Next,the unmanned swarms’community network is designed,and the network characteristics are analyzed.Moreover,the mechanism characteristics are analyzed by numerical simulation,focusing on the impact of key parameters,such as cost,benefit coefficient and adjustment rate on the level of swarm cooperation.Finally,the conclusion is made,which is expected to provide a theoretical reference and decision support for cooperative mode design and combat effectiveness generation of unmanned swarm operations.展开更多
利用无监督学习的一类支持向量机(One Class Support Vector Machine,OCSVM)和随机场景图像序列,构造滚动更新的像元分类模型,实现红外焦平面盲元的在线检测。根据正常像元和异常像元数量和灰度特征的差异,以随机图像序列作为输入数据,...利用无监督学习的一类支持向量机(One Class Support Vector Machine,OCSVM)和随机场景图像序列,构造滚动更新的像元分类模型,实现红外焦平面盲元的在线检测。根据正常像元和异常像元数量和灰度特征的差异,以随机图像序列作为输入数据,使用OCSVM建立单一类别的像元分类模型,灰度变化的像元归为一类,其他像元不属于此类。由于随机图像序列的滚动更新,OCSVM模型及支持向量也随之更新。统计支持向量的频次,高频次支持向量对应的像元聚为一类,即为异常像元。以320×256中波红外图像序列为例,说明了OCSVM模型进行盲元检测的过程,检测结果与黑体定标的结果一致。基于随机场景和OCSVM模型的盲元检测方法摆脱了定标黑体的约束,提高了盲元检测的灵活性。展开更多
面对高度复杂和多变的现代战场,如何快速直观地发现战场上的态势热点,降低认知负载是指挥员所面临的巨大挑战。针对战场时变条件下的态势热点发现问题,在分析热点形成机理的基础上,提出关注势理论,构建了符合人类认知特性的改进的Logis...面对高度复杂和多变的现代战场,如何快速直观地发现战场上的态势热点,降低认知负载是指挥员所面临的巨大挑战。针对战场时变条件下的态势热点发现问题,在分析热点形成机理的基础上,提出关注势理论,构建了符合人类认知特性的改进的Logistic时间衰减函数(improved Logistic time decay function,ILTDF),进而提出随时间衰减的加权核密度估计(weighted kernel density estimation decay over time,W-KDE-DOT)法以及基于关注势的战场态势热力图构建方法。实验结果表明,基于关注势理论的热力图构建方法,能够更准确地刻画出态势热点的时变特性,更符合战场态势热点发现客观规律,能够更好地为指挥决策人员准确把握战场态势热点提供支撑。展开更多
大量研究发现,社交网络结构对观点的传播动力学特性有显著影响。提出的基于邓巴数限制的网络生成算法--DNCSN(Social Network Generation Algorithm with the Constraint of Dunbar’s Number),具有较小的平均距离和较大的群聚系数,可...大量研究发现,社交网络结构对观点的传播动力学特性有显著影响。提出的基于邓巴数限制的网络生成算法--DNCSN(Social Network Generation Algorithm with the Constraint of Dunbar’s Number),具有较小的平均距离和较大的群聚系数,可以生成与真实网络节点度和节点强度的非线性关系较为接近的结果。基于DNCSN生成算法,可以更为有效地研究社交网络上的观点演化特性。通过调整社交网络中个体的观点“坚定度”和观点取信阈值参数,对比研究了回音室的形成数量变化。研究表明当网络的观点初值均匀分布,网络规模小于邓巴数字时,相比于同等规模的全联通网络,DNCSN网络结构能在节点取信阈值大于0.15时减少网络的回音室的形成数量;当网络规模增大时,DNCSN网络的回音室形成数量多于全联通网络;当取信阈值小于0.1时,全联通网络的回音室形成数量受网络规模影响,且网络规模越小回音室数量越少。展开更多
基金supported by the National Natural Science Foundation of China(71901217)the Key Primary Research Project of Primary Strengthening Program(2020-JCJQ-ZD-007).
文摘Autonomous cooperation of unmanned swarms is the research focus on“new combat forces”and“disruptive technologies”in military fields.The mechanism design is the fundamental way to realize autonomous cooperation.Facing the realistic requirements of a swarm network dynamic adjustment under the background of high dynamics and strong confrontation and aiming at the optimization of the coordination level,an adaptive dynamic reconfiguration mechanism of unmanned swarm topology based on an evolutionary game is designed.This paper analyzes military requirements and proposes the basic framework of autonomous cooperation of unmanned swarms,including the emergence of swarm intelligence,information network construction and collaborative mechanism design.Then,based on the framework,the adaptive dynamic reconfiguration mechanism is discussed in detail from two aspects:topology dynamics and strategy dynamics.Next,the unmanned swarms’community network is designed,and the network characteristics are analyzed.Moreover,the mechanism characteristics are analyzed by numerical simulation,focusing on the impact of key parameters,such as cost,benefit coefficient and adjustment rate on the level of swarm cooperation.Finally,the conclusion is made,which is expected to provide a theoretical reference and decision support for cooperative mode design and combat effectiveness generation of unmanned swarm operations.
文摘利用无监督学习的一类支持向量机(One Class Support Vector Machine,OCSVM)和随机场景图像序列,构造滚动更新的像元分类模型,实现红外焦平面盲元的在线检测。根据正常像元和异常像元数量和灰度特征的差异,以随机图像序列作为输入数据,使用OCSVM建立单一类别的像元分类模型,灰度变化的像元归为一类,其他像元不属于此类。由于随机图像序列的滚动更新,OCSVM模型及支持向量也随之更新。统计支持向量的频次,高频次支持向量对应的像元聚为一类,即为异常像元。以320×256中波红外图像序列为例,说明了OCSVM模型进行盲元检测的过程,检测结果与黑体定标的结果一致。基于随机场景和OCSVM模型的盲元检测方法摆脱了定标黑体的约束,提高了盲元检测的灵活性。
文摘面对高度复杂和多变的现代战场,如何快速直观地发现战场上的态势热点,降低认知负载是指挥员所面临的巨大挑战。针对战场时变条件下的态势热点发现问题,在分析热点形成机理的基础上,提出关注势理论,构建了符合人类认知特性的改进的Logistic时间衰减函数(improved Logistic time decay function,ILTDF),进而提出随时间衰减的加权核密度估计(weighted kernel density estimation decay over time,W-KDE-DOT)法以及基于关注势的战场态势热力图构建方法。实验结果表明,基于关注势理论的热力图构建方法,能够更准确地刻画出态势热点的时变特性,更符合战场态势热点发现客观规律,能够更好地为指挥决策人员准确把握战场态势热点提供支撑。
文摘大量研究发现,社交网络结构对观点的传播动力学特性有显著影响。提出的基于邓巴数限制的网络生成算法--DNCSN(Social Network Generation Algorithm with the Constraint of Dunbar’s Number),具有较小的平均距离和较大的群聚系数,可以生成与真实网络节点度和节点强度的非线性关系较为接近的结果。基于DNCSN生成算法,可以更为有效地研究社交网络上的观点演化特性。通过调整社交网络中个体的观点“坚定度”和观点取信阈值参数,对比研究了回音室的形成数量变化。研究表明当网络的观点初值均匀分布,网络规模小于邓巴数字时,相比于同等规模的全联通网络,DNCSN网络结构能在节点取信阈值大于0.15时减少网络的回音室的形成数量;当网络规模增大时,DNCSN网络的回音室形成数量多于全联通网络;当取信阈值小于0.1时,全联通网络的回音室形成数量受网络规模影响,且网络规模越小回音室数量越少。
