目标图像运动特征分析

在红外成像导弹导引头的信息处理中,目标图像特征研究是导引头技术研究中的一个重要问题。对各种潜在目标成像特征的详细研究是红外成像导引头信息处理研究的基础。在此项工作完成的基础上,可以实现对导引头目标识别和跟踪算法的评价,同时也可以辅助图像处理算法的选择和优化。在分析红外成像制导空空导弹工作原理的基础上,建立了导弹、目标的仿真模型,对常见的目标图像运动特征进行了仿真分析。给出了在空战条件下目标图像运动特征的部分仿真结果。     

水下导弹导引法与弹道仿真

假设目标作直线运动时,以未来方位导引法和自动变提前角导引法两种不同的导引方法为例,建立了水下导弹的线导和自导相对运动方程、导引弹道数学模型并进行了仿真,获得了水下导弹、目标和制导站的相对运动轨迹及水下导弹与目标相对距离随时间变化的曲线和攻击效果。对水下导弹攻击目标距离的预测具有较高的参考价值。     

Modeling and analyzing point cloud generation in missile-borne LiDAR

The missile-borne LiDAR has an essential prospect in precise guidance. However, the instability of the missile has a significant impact on the precision of LIDAR point cloud, thus modeling and analyzing the influencing factors of point cloud generation is necessary. In this investigation, the authors modeled the point cloud calculation process of missile-borne linear array scanning LiDAR under ideal conditions and analyzed the effect of position change on the field of view (FOV) in the course of platform motion. Subsequently, the authors summarized that the stability concerning the field of view and the target tracking are dependent on three limiting conditions. Finally, the authors derived an error formula and analyzed several typical errors in missile platform flight, including scanner error, POS error, system integration error, and platform vibration error. Based on theoretical analysis and simulation experiments, the model proposed in this paper can provide a theoretical basis for the design of the missile-borne LiDAR system and the selection of related instruments in practice.     

基于正态分布曲线的分段式变步长LMS算法

针对传统最小均方误差(Least Mean Square, LMS)自适应滤波算法由于步长固定,在解决稳态误差与收敛性之间的关系时,始终处于矛盾状态的问题,在对传统的固定步长LMS自适应滤波算法分析的基础上,根据变步长LMS自适应滤波算法的步长调整原则,通过构造步长因子与误差信号的非线性函数,提出了一种基于正态分布曲线的分段式变步长LMS自适应滤波算法,并分析了参数取值对算法性能的影响。针对实际信号处理过程中参考信号难以选取的问题,提出了一种基于分裂阵的参考信号选取方法。理论和海试数据分析结果表明:该算法的收敛速度和稳态误差明显优于固定步长的LMS自适应滤波算法和基于Sigmoid函数的变步长LMS自适应滤波算法。     

基于置信规则库的防空目标意图识别方法

为解决防空反导体系对于目标意图识别中多源信息处理的问题,提出了一种新的防空目标识别方法——置信规则库(Belief Rules Base)识别模型,该方法是基于BRB专家知识系统的自学习方法,可以有效处理多源不确定性信息。同时,为了提高其识别效率,建立了一种与之相匹配的参数优化模型,并选择差分进化算法作为BRB识别模型的优化引擎。此外,在BRB识别模型中,专家意见也被引用到模型的初始化和推理过程中。最后采用一个实例对BRB识别模型的准确度进行验证。研究表明:该方法具有较强的实用性,可为防空反导体系的目标意图识别提供有力支撑。     

小型旋翼无人机在未来城市军事行动应用中潜力评估

随着全球范围的城市化进程不断推进,未来军事斗争向城市环境中发展是必然趋势。在城市作战需求分析的基础上,剖析了传统装备的"城市病",阐述了小型旋翼无人机在城市军事行动中的应用潜能。研究表明,针对现有装备在城市作战中显示出的若干短板,发展基于小型旋翼无人机打造侦察平台与精确打击平台将是一个较好的解决方案。而运用军民融合的发展模式挖掘小型旋翼无人机军用潜力,在提升军队作战能力,降低成本均具有积极意义。     

高超声速飞行器滑翔制导方法综述

阐明高超声速飞行器滑翔制导的基本问题,分析滑翔制导过程面临的复杂多约束、机动任务要求、参数扰动等研究难点;分别就国内外标准轨迹制导方法和预测-校正制导方法相关研究现状展开综述,指出了这两类方法中存在的问题。在此基础上,提出高超声速飞行器滑翔制导研究中亟待解决的关键问题,并指出未来滑翔制导方法的研究热点。     

进入角约束下的载机变结构远距引导

针对一般导引律在载机远距引导中存在的问题,结合远距引导的需求,设计了远距引导的三维变结构导引律。该导引律具有较强自适应性,可以使载机以期望的进入角接近目标。仿真结果表明,载机在该导引律作用下,能以较小过载,平滑轨迹快速接近机动目标,并保证载机引导结束时占有有利的空中战术态势。     

团体辅导在护理人员人际适应教学中的应用

护理人员良好的人际适应教学能力不仅是临床带教的需要,而且是现代护理服务团队发展的必须。团体辅导对提高护理人员人际适应教学能力方面有很好的作用,主要有完善人际理念,增强护理团队凝聚力,预防团队信息流通障碍,提高临床护理教学质量等。     

Military Expenditure,Endogeneity and Economic Growth

Abstract

While not always a concern for the general economic growth literature, the debate over the effects of military spending on growth continues to develop, with no consensus, but a deepening understanding of the limitations of previous work. One important issue that has not been adequately dealt with is the endogeneity of military spending in the growth equation, mainly because of the difficulty of finding any variables that would make adequate instruments. This paper considers this issue, using an endogenous growth model estimated on a large sample of 109 non-high-income countries for the period 1998–2012. The empirical analysis is framed within an instrumental variable setting that exploits the increase in military spending that occurs when unrest in a country escalates to turmoil. The estimation results show that endogeneity arising from reverse causality is a crucial issue, with the instrumental variable estimates providing a larger significant negative effect of military spending on growth than OLS would. This result is found to be robust to different sources of heterogeneity and different time periods.