基于灰色理论的地空导弹设计保障能力评估

设计保障能力是装备保障能力的重要组成部分.建立了某地空导弹武器系统的设计保障能力指标模型,为新装备的设计研制提供了保障性参数.提出了一种新的权值计算方法,通过实例建立该地空导弹武器系统的设计保障能力指标评价体系,应用灰色系统评估理论对其进行评估.实验结果表明该评估方法可行、实用.     

基于SNR的烟幕干扰效果仿真

烟幕对红外等电磁波具有吸收、反射和散射特性,可导致导引头上接收的能量大大衰减,从而降低导弹的命中概率.针对外场烟幕性能测试受气象条件和环境因素的影响较大,对准确评估抗红外烟幕遮蔽性能存在较大困难的情况,结合气象条件从烟幕透过率的变化引起导引头信噪比变化的角度,建立了烟幕干扰效果的评估模型,通过计算机仿真,得出了烟幕对红外制导导弹的干扰效果.通过试验对其中的一些结论进行了实验验证,结果表明计算结果能够与实验数据较好吻合.     

舰空导弹协同攻击目标分配方法研究

根据双舰编队反导队形的配置和舰空导弹的战术技术性能,提出了针对多批目标的实时火力分配决策方法,根据当前态势和火力单元特性对需分配的目标进行静态实时火力分配,系统不断循环直至目标分配完毕,最后用实例进行了分析,计算结果表明了该方法的有效性.     

不同腐蚀状态不锈钢管焊缝区 碱性腐蚀的电位噪声特征

利用电化学电位噪声方法检测了不同腐蚀状态304与316两种不镑钢管焊缝区试样在室温碱液中的腐蚀行为,并采用电化学噪声时域谱、频域谱进行了分析,结果表明:不同腐蚀状态焊缝试样的腐蚀电位及噪声时城谱特征不同,可利用电位噪声频城谱的高频斜率K值定性判定是否发生局部腐蚀,同时可根据频城谱中发生事件特殊频率fn区分材料的腐蚀状态...     

水下目标辐射噪声高精度仿真方法

在对水下高速运动目标实测辐射噪声数据频谱特征充分分析的基础上,基于能量聚点理论建立了一种“平台型λ水平能量聚点”辐射噪声连续谱模型.利用该模型实现了对水下目标辐射噪声的仿真.首先,对离散化的λ取值分别计算其能量聚点频率区间,然后用谐波叠加法生成频带内的时域信号,将所有频带的信号进行叠加就得到辐射噪声的时域信号,与传统的...     

让每一位教师做最好的自己——对教师专业化成长的再思考

教师的成长关系着学生的成长、学校的成长。本文从教师专业成长的实践探索出发,从学生视角对教师角色进行再确认,从个体视角提出＂做最好的自己＂的专业成长目标,深入阐述了教师管理理念、打造教师成长空间及实现教师成长的途径。     

交互式翻译教学模式的构建及其绩效研究

交互式翻译法要求教师根据自身的特点、学习者的个体差异和教学情景,采用灵活、多变的多种教学方法进行教学。教师应该鼓励学生更多地参与到教学中,通过师生互动形成良好的学习氛围,促进学生翻译知识的自觉吸收。本文尝试构建交互式翻译教学模式并研究其绩效。     

舰空导弹引战配合可视化仿真分析方法

提出了一种更加直观的舰空导弹引战配合分析方法,充分利用飞行试验数据和弹道仿真数据,通过引战配合数学仿真计算和可视化仿真,复现了弹目交会时战斗部破片杀伤目标的过程,并在某型舰空导弹飞行试验引战配合分析中得到了应用.     

非平稳噪声背景下DOA分步估计方法研究

针对相干和非相干信源同时存在的情况,结合斜投影理论和互相关矢量Toeplitz矩阵重构(CVTR)的方法,提出一种新的信源DOA分步估计方法。该方法把相干信源和非相干信源分开分辨,对相干信源用CVTR方法来恢复为满秩,避免了常规平滑算法阵列孔径损失大、运算量大的缺点;用斜投影算子而非差分方法对信源进行分离,不受相关矩阵须为Toeplitz结构的限制,使得算法可适用于任意阵列结构形式。这种分步分辨思路可有效增强信源过载能力,同时在互相关矢量Toeplitz矩阵重构过程中,可把非平稳噪声协方差矩阵转换成白噪声结构,使得算法对非平稳噪声有较好的适应能力。     

Signal‐to‐interference‐plus‐noise ratio estimation for wireless communication systems: Methods and analysis

The Signal‐to‐Interference‐plus‐Noise Ratio (SINR) is an important metric of wireless communication link quality. SINR estimates have several important applications. These include optimizing the transmit power level for a target quality of service, assisting with handoff decisions and dynamically adapting the data rate for wireless Internet applications. Accurate SINR estimation provides for both a more efficient system and a higher user‐perceived quality of service. In this paper, we develop new SINR estimators and compare their mean squared error (MSE) performance. We show that our new estimators dominate estimators that have previously appeared in the literature with respect to MSE. The sequence of transmitted bits in wireless communication systems consists of both pilot bits (which are known both to the transmitter and receiver) and user bits (which are known only by the transmitter). The SINR estimators we consider alternatively depend exclusively on pilot bits, exclusively on user bits, or simultaneously use both pilot and user bits. In addition, we consider estimators that utilize smoothing and feedback mechanisms. Smoothed estimators are motivated by the fact that the interference component of the SINR changes relatively slowly with time, typically with the addition or departure of a user to the system. Feedback estimators are motivated by the fact that receivers typically decode bits correctly with a very high probability, and therefore user bits can be thought of as quasipilot bits. For each estimator discussed, we derive an exact or approximate formula for its MSE. Satterthwaite approximations, noncentral F distributions (singly and doubly) and distribution theory of quadratic forms are the key statistical tools used in developing the MSE formulas. In the case of approximate MSE formulas, we validate their accuracy using simulation techniques. The approximate MSE formulas, of interest in their own right for comparing the quality of the estimators, are also used for optimally combining estimators. In particular, we derive optimal weights for linearly combining an estimator based on pilot bits with an estimator based on user bits. The optimal weights depend on the MSE of the two estimators being combined, and thus the accurate approximate MSE formulas can conveniently be used. The optimal weights also depend on the unknown SINR, and therefore need to be estimated in order to construct a useable combined estimator. The impact on the MSE of the combined estimator due to estimating the weights is examined. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004