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空战中目标状态信息的不确定性、目标与我机相对态势是影响机载传感器资源分配问题的重要影响因素.针对此问题,提出一种基于模糊贝叶斯网(FBN)的空战传感器资源管理方法,以空战传感器资源管理中涉及的影响因素因果关系作为建网依据,将目标信息增量、目标威胁、飞行员指令作为证据变量驱动网络进行概率推理,从而获取空战传感器资源的分配结果.仿真结果表明,与传统方法相比,方法的自适应变间隔采样策略能够根据目标威胁及飞行员指令影响,管理空战态势不同阶段的传感器资源以满足空战作战任务需求. 相似文献
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针对现有超视距空战威胁估计模型的不足,提出了一种新的基于模糊逻辑的超视距空战威胁估计模型。该模型以机载中远程空空导弹攻击区、雷达搜索区性能参数以及双方战术几何关系、相对运动趋势作为评估对象进行模糊化,设计了角度优势、距离优势推理模块,并以二者的加权和作为当前空战态势的优势指数。模型较为全面反应了空战态势与参战飞机的作战能力,可为超视距多机协同攻击多目标空战时的目标分配、战术选择提供参考。仿真结果验证了模型的有效性。 相似文献
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无人机(UAV)集群协同态势感知(SA)一致性是无人机集群协同作战的关键,对一致性进行评估至关重要。针对态势感知过程中面临的强不确定性等问题,在一致性评估指标体系的基础上,提出了基于证据推理规则的态势感知一致性评估方法,将各类指标信息统一至置信结构,并充分考虑指标的权重与可靠度。采用扰动分析法对复杂战场环境进行模拟,提出考虑扰动的态势感知一致性评估方法,并对算法进行了总结。最后,通过仿真算例和对比研究验证了所提方法在不确定性表达、一致性评估和无人机集群的环境适应性研究等方面的有效性。 相似文献
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已有的空中格斗控制方法未综合考虑基于专家知识的态势评估及通过连续性速度变化控制空战格斗的问题。基于深度确定性策略梯度(DDPG)强化学习算法,在态势评估函数作为强化学习奖励函数的基础上,设计综合考虑飞行高度上下限、飞行过载以及飞行速度上下限的强化学习环境;通过全连接的载机速度控制网络与环境奖励网络,实现DDPG算法与学习环境的交互,并根据高度与速度异常、被导弹锁定时间以及格斗时间设计空战格斗结束条件;通过模拟一对一空战格斗,对该格斗控制方法在环境限制学习、态势评估得分以及格斗模式学习进行验证。结果表明:本文提出的空战格斗控制方法有效,能够为自主空战格斗进一步发展提供指导。 相似文献
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在目标识别决策系统中,多探测器多源信息融合的模糊性和不确定性以及各探测周期所得信息的冲突互斥会造成目标识别决策不精准。为解决这一问题,提出基于云模型和改进D-S (Dempster-Shafer)证据理论的目标识别决策方法。首先,将目标识别准确性这一语言评价值划分为不同评价区间等级,以不同评价等级标准云为参照将各探测器各探测周期所得信息转化为云决策矩阵,得出各周期各等级隶属度,进而构建出基本概率分配函数(mass函数);其次,基于证据理论引入冲突度、差异度、离散度3类衡量冲突大小的参数,定义了一种新的证据冲突参数,同时改进证据冲突融合算法,对各探测器各周期证据体进行修正并融合;再次,结合各探测器权重加权得出各目标综合识别决策的mass函数对目标进行决策;最后,结合算例,验证该方法的适用性,并与其他方法相对比验证了本文方法的优越性。 相似文献
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An Application of Evidential Networks to Threat Assessment 总被引:1,自引:0,他引:1
Benavoli A. Ristic B. Farina A. Oxenham M. Chisci L. 《IEEE transactions on aerospace and electronic systems》2009,45(2):620-639
Decision makers operating in modern defence theatres need to comprehend and reason with huge quantities of potentially uncertain and imprecise data in a timely fashion. An automatic information fusion system is developed which aims at supporting a commander's decision making by providing a threat assessment, that is an estimate of the extent to which an enemy platform poses a threat based on evidence about its intent and capability. Threat is modelled by a network of entities and relationships between them, while the uncertainties in the relationships are represented by belief functions as defined in the theory of evidence. To support the implementation of the threat assessment functionality, an efficient valuation-based reasoning scheme, referred to as an evidential network, is developed. To reduce computational overheads, the scheme performs local computations in the network by applying an inward propagation algorithm to the underlying binary join tree. This allows the dynamic nature of the external evidence, which drives the evidential network, to be taken into account by recomputing only the affected paths in the binary join tree. 相似文献
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智能诊断的定量推理机制和最优推理环境 总被引:1,自引:0,他引:1
描述和分析了一种新的基于智能信息处理的故障检诊方法,可以处理复杂动态系统故障检诊面临的复杂故障组合模式和时间特性。从推理机制的量化和推理环境的优选两方面完善了该理论,使其诊断能力、实时性和适用范围都得到提高。推理机制的量化是通过引入概率信息实现的。考虑到诊断过程中可能出现新的故障。又进一步引入了候选项集合扩张机制描述该现象,并提供了相关的概率计算公式。推理机制定量化使得诊断结果可以精确度量,检诊过程也可精确控制。推理环境优选则是通过引入熵信息实现的,由此提高了诊断效率。 相似文献
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《中国航空学报》2023,36(5):306-327
Evidential Reasoning (ER) rule, which can combine multiple pieces of independent evidence conjunctively, is widely applied in multiple attribute decision analysis. However, the assumption of independence among evidence is often not satisfied, resulting in ER rule inapplicable. In this paper, an Evidential Reasoning rule for Dependent Evidence combination (ERr-DE) is developed. Firstly, the aggregation sequence of multiple pieces of evidence is determined according to evidence reliability. On this basis, a calculation method of evidence Relative Total Dependence Coefficient (RTDC) is proposed using the distance correlation method. Secondly, as a discounting factor, RTDC is introduced into the ER rule framework, and the ERr-DE model is formulated. The aggregation process of two pieces of dependent evidence by ERr-DE is investigated, which is then generalized to aggregate multiple pieces of non-independent evidence. Thirdly, sensitivity analysis is carried out to investigate the relationship between the model output and the RTDC. The properties of sensitivity coefficient are explored and mathematically proofed. The conjunctive probabilistic reasoning process of ERr-DE and the properties of sensitivity coefficient are verified by two numerical examples respectively. Finally, the practical application of the ERr-DE is validated by a case study on the performance assessment of satellite turntable system. 相似文献
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为了降低在空中目标威胁评估任务中由于威胁评估结果的不准确性和传感器辐射所带来的潜在损失,提出了一种基于风险的多传感器管理方法。首先,基于部分可观马尔可夫决策过程建立了传感器管理模型;然后,给出了基于信息状态的威胁评估风险和传感器辐射风险的预测方法以量化潜在损失;接着,为获得更优的作战收益,以多步风险预测值为决策依据,以两种风险的加权和最小为优化目标建立了长期目标函数;最后,在求解目标函数时,将传感器管理问题转化为决策树搜索,设计了一种基于分支定界的标准代价搜索算法以快速获得高质量的管理方案。仿真实验表明,所提算法能够在搜索到高质量解的同时大幅减少计算时间和内存消耗;所提方法能够对风险进行准确预测,且相比于经典的传感器管理方法,所提方法具有更好的风险控制效果。 相似文献
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基于序列图像的自动目标识别算法 总被引:6,自引:0,他引:6
由于利用单幅二维图像进行三维目标识别存在识别的多义性,提出了一种基于二维序列图像的三维目标自动识别算法。首先以修正的Hu不变矩构造目标的图像识别特征,进而采用BP神经网络分类器构造关于目标融合识别的基本置信指派函数,以神经网络的训练误差构造证据理论不确定性度量,采用基于吸收法的DS证据理论实现高冲突证据的贯序式融合。对各姿态飞机图像识别的仿真表明,该算法对飞机的空间姿态变化具有很强的鲁棒性,能快速地准确识别飞机类型。此外,算法对先验性参数具有一定的鲁棒性。 相似文献
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Liang Qilian Cheng Xiuzhen 《IEEE transactions on aerospace and electronic systems》2008,44(3):1060-1069
We propose a knowledge-based ubiquitous and persistent sensor network (KUPS) for threat assessment, in which "sensor" is a broad characterization. It refers to diverse data or information from ubiquitous and persistent sensor sources such as organic sensors and human intelligence sensors. Our KUPS for threat assessment consists of two major steps: situation awareness using fuzzy logic systems (FLSs) and threat parameter estimation using radar sensor networks (RSNs). Our FLSs combine the linguistic knowledge from different intelligent sensors, and our proposed maximum-likelihood (ML) estimation algorithm performs target radar cross section (RCS) parameter estimation. We also show that our ML estimator is unbiased and the variance of parameter estimation matches the Cramer-Rao lower bound (CRLB) if the radar pulses follow the Swerling II model. Simulations further validate our theoretical results. 相似文献
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Study on UAV Path Planning Approach Based on Fuzzy Virtual Force 总被引:3,自引:2,他引:1
This article proposes a novel fuzzy virtual force (FVF) method for unmanned aerial vehicle (UAV) path planning in complicated environment. An integrated mathematical model of UAV path planning based on virtual force (VF) is constructed and the corresponding optimal solving method under the given indicators is presented. Specifically, a fixed step method is developed to reduce computational cost and the reachable condition of path planning is proved. The Bayesian belief network and fuzzy logic reasoning theories are applied to setting the path planning parameters adaptively, which can reflect the battlefield situation dynamically and precisely. A new way of combining threats is proposed to solve the local minima problem completely. Simulation results prove the feasibility and usefulness of using FVF for UAV path planning. Performance comparisons between the FVF method and the A* search algorithm demonstrate that the proposed approach is fast enough to meet the real-time requirements of the online path planning problems. 相似文献