基于粗糙集理论的飞行轨迹识别

针对飞行轨迹人工识别对专家经验依赖性强,识别准确率不稳定的问题,提出一种基于粗糙集理论的飞行轨迹识别方法。首先,对飞行轨迹进行特征提取,获取用于轨迹类别判定的条件属性和决策属性;其次,利用模糊自组织神经网络对飞行轨迹决策表离散化,采用启发式遗传约简算法对条件属性约简,消除了冗余属性对识别结果的影响;最后,利用实际飞参数据进行轨迹识别,结果证明该方法准确有效。     

Book review

La Conversion économique du secteur militaire, J. FONTANEL, Economica, Paris, 1994, pp. 112. Paperback. ISBN 2–7178–2650–5.     

基于复杂性的战场损伤定位流程评价模型研究

损伤定位流程制定的合理与否是衡量一个评估员水平高低的重要指标之一。对损伤定位流程的评价涉及到所用时间、工具、操作的难度等指标都可以用其复杂性来表示,目前鲜有关于损伤定位流程评价的研究,因此提出战场损伤定位流程的复杂性,并借鉴软件工程领域的程序复杂性量化方法——图熵法对其进行量化。经过数据验证,复杂性与评估时间之间存在较好的拟合关系,并用实例说明了模型的应用效果。     

金属射流欧姆加热效应的影响因素分析

为探究影响金属射流欧姆加热效应的因素,在被动电磁装甲系统等效电路模型的基础上,根据虚拟源点理论建立金属射流的作用时间模型,进一步明确金属射流在侵彻被动电磁装甲过程中每部分射流微元的作用时间;结合金属射流的比作用量模型,利用Matlab软件对金属射流的电流和比作用量波形随被动电磁装甲系统的电感、电容、电阻和充电电压的变化规律进行数值分析．仿真结果表明：随着系统电感的减小、电阻的减小、电容的增大和充电电压的增大,金属射流比作用量的峰值增大,有利于射流发生电爆炸．     

基于椭球模型的雷达/ESM联合定位算法

为了减少作战过程中雷达的开机时间,采用了ESM辅助的方法。在雷达关机条件下,利用多平台ESM对目标进行定位;当雷达开机时,采用雷达/ESM联合定位。本文首先分析了基于椭球模型的多平台ESM交叉定位算法,其次分析了雷达/ESM联合定位算法。通过仿真实验分析,这两种算法具有很高的定位精度;并分析了测量误差对定位精度的影响。该算法具有一定的应用价值     

采用压缩感知的卫星高频姿态确定方法

高频振荡通常会超过星载陀螺和星敏的测量频率范围,导致卫星姿态确定精度下降。因此,提出一种基于常规姿态传感器进行高频姿态确定的方法。将高频姿态抖动看作一个稀疏信号,通过压缩感知的方法将该信号从低频采样结果中恢复。利用低带宽姿态传感器和卡尔曼滤波器测量得到低频姿态数据并进行融合,再通过压缩感知算法从融合数据中恢复出高频姿态数据。通过仿真实验验证了该方法的有〖BHDWG8,WK10YQ,DK1*2,WK1*2D〗〖XCWP.TIF;%129%129〗听语音 聊科研与作者互动 效性。     

Visual-simulation region proposal and generative adversarial network based ground military target recognition

Ground military target recognition plays a crucial role in unmanned equipment and grasping the battlefield dynamics for military applications, but is disturbed by low-resolution and noisy-representation. In this paper, a recognition method, involving a novel visual attention mechanism-based Gabor region proposal sub-network (Gabor RPN) and improved refinement generative adversarial sub-network (GAN), is proposed. Novel central–peripheral rivalry 3D color Gabor filters are proposed to simulate retinal structures and taken as feature extraction convolutional kernels in low-level layer to improve the recognition accuracy and framework training efficiency in Gabor RPN. Improved refinement GAN is used to solve the problem of blurry target classification, involving a generator to directly generate large high-resolution images from small blurry ones and a discriminator to distinguish not only real images vs. fake images but also the class of targets. A special recognition dataset for ground military target, named Ground Military Target Dataset (GMTD), is constructed. Experiments performed on the GMTD dataset effectively demonstrate that our method can achieve better energy-saving and recognition results when low-resolution and noisy-representation targets are involved, thus ensuring this algorithm a good engineering application prospect.     

考虑目标微多普勒频移的反导雷达配置模型

针对采用微多普勒频移识别目标的反导雷达,对其配置问题进行研究,以便更好地发挥其目标识别能力。首先,利用雷达性能参数,计算出要探测某一弹道时的雷达可配置区;其次,分析目标微多普勒频移对目标识别可能性的影响,以雷达覆盖区和高概率识别区的最大化为目标建立雷达的配置模型;最后,通过仿真分析发现通过调节雷达覆盖区与高概率识别区的权重,可以得到针对不同作战需求时的雷达配置方案。     

Sequential learning versus no learning in Bayesian regression models

We consider two regression models: linear and logistic. The dependent variable is observed periodically and in each period a Bayesian formulation is used to generate updated forecasts of the dependent variable as new data is observed. One would expect that including new data in the Bayesian updates results in improved forecasts over not including the new data. Our findings indicate that this is not always true. We show there exists a subset of the independent variable space that we call the “region of no learning.” If the independent variable values for a given period in the future are in this region, then the forecast does not change with any new data. Moreover, if the independent variable values are in a neighborhood of the region of no learning, then there may be little benefit to wait for the new data and update the forecast. We propose a statistical approach to characterize this neighborhood which we call the “region of little learning.” Our results provide insights into the trade‐offs that exist in situations when the decision maker has an incentive to make an early decision based on an early forecast versus waiting to make a later decision based on an updated forecast. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 532–548, 2014     

基于Kalman滤波器的放大转发双向中继联合信道估计方法

针对遵从放大转发协议的双向中继网络的联合信道估计问题,提出了一种基于Kalman滤波器的新方法。首先根据联合信道构成特点将其划分为自干扰部分及传输部分,通过AR模型对这两部分自相关函数进行近似化处理,建立了联合信道时变过程的状态方程,结合接收的训练序列信号,给出了具有Kalman滤波器形式的估计方法。随后在证明了该方法的一致收敛性质的同时,列出了误差性能限所满足的Riccati方程表达式。仿真结果表明,新估计方法相比于最大似然方法在均方误差方面具有明显的性能优势。