熵权灰色关联分析法的坦克夜间射击目标选择

针对夜间战场上坦克目标威胁评估问题,结合多目标决策问题,将熵权和灰色关联分析法相结合,提出一种基于熵权灰色关联分析的坦克夜间射击目标选择模型,并详细给出了该算法的思想、算法.该方法克服了传统评估算法中主观赋值的缺陷,最后利用实例证明了该方法的有效性.     

海军舰艇编队防空导弹协同作战技术研究

主要以信息化条件下如何提升海军舰艇编队防空导弹作战能力为背景,重点描述协同指挥和协同制导作战的概念内涵、国内外研究现状、作战模式、主要实现的功能,给出不同指挥层次下各指挥节点的体系结构和功能定位,进而从信息场和功能界面等方面说明协同指挥和协同制导2个功能层次的区别.     

基于几何关系的视线指令制导律研究

针对近程车载防空系统拦截空地导弹的制导问题,设计了一种视线指令制导律.首先建立了地面站、拦截弹和空地导弹三者之间相对运动模型,然后以控制拦截弹位于地面站和空地导弹连线上为目标,根据三者在空间的几何关系设计了一种视线指令制导律,并对拦截的可行性进行了分析.仿真结果验证了所设计的制导律能够有效保证地面站的安全,与比例导引律相比,提高了制导精度,缩短了拦截时间.     

空舰导弹火控系统精度分析研究

介绍了一种空舰导弹火控系统动态精度试验的试验方法、误差分析理论和方法.用导弹火控系统的数据测量设备获取火控系统输出射击诸元参数实际值,再测量载机和靶船的位置和运动参数,经真值解算模型解算出射击诸元参数标准值.将实际值与标准值进行比较与分析,即可得知空舰导弹火控系统的动态精度是否满足要求.此方法已成功地应用于导弹火控系统精度飞行试验中.     

Missile systems: “Flexible modularity” and incremental technological change in military production∗

It is often stated that technological change in the military field is characterised by the introduction of radical innovations rather than by incremental processes of technological change. This article illustrates the diversity of military innovation by arguing that technological change in missiles systems is dominated by incremental change. While some large weapons platforms display the design rigidities normally associated with complex systems, missiles are characterised by “flexible modularity”. Flexible modularity facilitates the continuous introduction of upgrades, and makes missile systems amenable to gradual technological change. Besides, it has significant implications for the future role of missile systems, and poses special problems for missile disarmament and technology control initiatives.     

UNDERSTANDING CHINA'S ANTISATELLITE TEST

In discussing China's January 2007 hit-to-kill intercept of the Chinese weather satellite FengYun-1C, most American analysts sought the “message” for the United States—either by asserting that China's test was a deliberate step toward a comprehensive counterspace capability to offset U.S. conventional military superiority or an attempt to force the United States to the negotiating table on “the prevention of an arms race in outer space.” Chinese officials, after a long silence, eventually claimed the test was an “experiment” that was “not targeted at any country.” We traveled to China several times in 2007 and had a series of conversations with Chinese individuals knowledgeable about the history of this particular antisatellite program and with access to information about the decision-making process prior to and after the final test. These discussions were off the record, not for attribution, given the sensitivity of the subject. They reflected the views of some of the key institutions involved in the test from the State, the Communist Party, the People's Liberation Army, and aerospace experts involved in debris calculations. The information conveyed to us suggests that American commentators tend to place much greater importance on the United States as a driver in China's decision to develop the technology and conduct the test than do the Chinese.     

舰空导弹武器系统抗击能力评估

舰空导弹武器系统抗击能力通常会随着舰空导弹单次射击而发生变化,为增强舰空导弹武器系统抗击能力评估方法的实用性,提出了一种新的舰空导弹武器系统抗击能力评估方法,进而构建了舰空导弹单次射击能力评估方法。算例证明,本文提供的方法可以有效评定单艘水面舰艇上单个舰空导弹武器系统在作战使用环境下的抗击能力。     

基于PSO-RBF神经网络的雷达目标识别

为提高雷达系统目标识别能力,对粒子群算法及RBF神经网络进行了分析。针对离子群算法(PSO)易陷入局部极小的缺陷,提出了基于自适应时变权重和局部搜索算子的改进PSO算法,并将该算法应用到RBF神经网络核函数参数的优化学习中,进行了雷达目标识别仿真实验。仿真结果表明,相对于标准PSO-RBF神经网络,改进算法不仅收敛速度快,且误差精度高,特别在干扰较强时,目标的识别率有较大提高。     

无人机小样本条件下遮挡和混淆目标识别方法

针对无人机对地目标识别过程中的小样本问题以及目标存在的遮挡和混淆情况,提出了一种融合自注意力机制的小样本目标识别模型。在利用元学习思想获取小样本学习能力的基础上,将自注意力机制学习目标内部各部分之间的上下文依赖关系引入模型,从而增强目标表征能力,以解决遮挡和混淆情况下有效特征不足的难题。为验证模型效果,通过对基准数据集和无人机航拍数据进一步加工,构建了遮挡和混淆目标数据集,设置了不同的遮挡程度和背景混淆率。通过在不同数据集上的验证,并与深度学习模型对比,证明提出的模型具有更高的学习效率和识别正确率。     

A pixel-level local contrast measure for infrared small target detection

Infrared (IR) small target detection is one of the key technologies of infrared search and track (IRST) systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure (PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously. With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker (RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed.