BIT技术在导航系统中的应用优势

介绍了BIT技术的定义、分类和关键技术,阐述了导航系统故障检测的特点,探讨了BIT技术在导航系统应用,能够增强导航系统内部各子系统故障诊断能力,信息融合能力,并为冗余设计和系统重构提供基础等方面的优势.     

基于滑窗检测的机载设备故障诊断仿真系统

根据机载设备故障诊断的需求,结合飞机性能监控系统的结构框架和机载设备的特点,提出基于快速存取记录器（QAR）记录的飞行数据和滑窗检测原理的机载设备故障诊断算法,并实现相应的机栽设备故障诊断仿真系统。在简述QAR数据译码与数据选择方法的基础上,论述了滑窗检测的原理并对其性能进行分析：设计实现了基于QAR的机载设备故障诊断仿真系统并给出仿真结果。结果表明,该系统可以有效诊断存在的故障,并能大大降低虚警概率,对保障飞行安全具有重要意义。     

基于CDM-CZM的复合材料补片补强参数分析

 复合材料开口补强设计参数的确定对于结构设计具有重要的意义。针对复合材料层合板开口区补片补强结构,采用各向异性材料连续介质损伤力学模型(CDM)对复合材料层合板的损伤演化进行描述,采用粘聚区模型(CZM)对补片与母板间界面材料的分层损伤进行模拟,建立了复合材料开口区补片补强结构三维非线性渐进损伤模型,模型可预测补强结构强度和损伤演化过程。应用本文模型分析了补片铺层方式、补片厚度和补片半径3个主要设计参数对补强效果的影响,明确了补片与母板间界面材料分层损伤破坏是导致补强结构最终失效的主要原因。     

2D C/SiC 复合材料的静压痕力的损伤表征

为考察2D C/SiC 复合材料在外力作用下产生的不可见损伤对其力学性能的影响。通过静压痕实验引入损伤,采用红外热波成像和残余性能测试的方法,表征2D C/SiC 复合材料的损伤。结果表明:施加能量为0.75 J时,复合材料内部开始产生损伤, 施加能量为1.8 J(接触力1 700 N、压痕深1 mm)时,损伤模式发生转变;随着损伤程度的加重,残余压缩强度下降率约为残余拉伸强度下降率的2倍。     

基于小波包分解和滑动功率谱的舰船轴频电场信号检测

轴频电场是可在水下探测到的一种运动舰船特征物理场,由于海水的衰减作用,其信号十分微弱。文章提出了一种基于小波包分解和滑动功率谱的微弱舰船轴频电场信号检测方法。首先,利用小波包变换对观测信号进行多子带分解;然后,分别提取各子带信号功率作为检测特征量,使用浮动门限对其进行滑动检测。实测数据和仿真数据处理表明,该方法能够在较低的信噪比条件下有效工作,性能优于传统的功率谱检测方法。     

反导毁伤元头部形状对目标穿甲毁伤效应的影响

利用有限元方法动态分析了不同形状的毁伤元对反舰导弹的穿甲毁伤效应。计算结果表明:相同初速相同质量的毁伤元穿透导弹引战系统靶板后,尖头毁伤元的剩余动能大于平头毁伤元的剩余动能。     

基于数字包络检波的RNSS通道时延标定方法

提出一种采用数字处理的时延测试方法,用于对导航卫星导航信号发射通道分数码片时延的精确测量。该方法是通过高速A/D（模/数）转换器,对导航卫星下行的BPSK（二进制相移键控）信号和卫星导航秒脉冲进行双通道采样,读取采样数据并进行数据处理。根据秒脉冲信号触发门限上升沿确定时延测量起点,对BPSK采样数据进行平方律检波,获取码片换相点,计算换相点和秒脉冲之间的分数码片时延,并进行滤波器时延校准,从而得到导航卫星发射链路的分数码片时延,该方法不需要进行伪随机信号的捕获和跟踪,测量精度主要取决于采样器采样率。通过在测试中使用一根校准电缆对该方法进行验证,验证结果表明,采用本文提出测试方法的测量误差优于0.3ns。     

基于厚壁工件X射线实时成像的焊缝缺陷自动检测

基于X射线成像的焊缝缺陷自动检测技术对提高工业射线检测的自动化水平具有重要意义。焊缝缺陷在线连续检测的实时性要求较高,随着工件厚度的增加,其焊缝X射线实时图像的信噪比变得很低,使得已有的处理算法难以满足实时性以及有效处理缺陷误检与漏检之间的矛盾。针对这些问题,在分析了传统方法在厚壁工件X射线图像焊缝缺陷自动检测中存在的问题基础上,对传统方法进行了改进,提出了双阈值背景消除法和平行焊接方向波形分析法,然后利用所提出算法之间的冗余性和互补性,融合多种分割结果以解决缺陷误检与漏检之间的矛盾。试验结果表明,所提出的缺陷自动检测方法能够在满足实时性要求的同时,实现缺陷检出,有效避免误检。     

Study on Titanium Alloy TC4 Ballistic Penetration Resistance Part I:Ballistic Impact Tests

Ballistic impact test of different-scale casings is an efficient way to demonstrate the casing containment capability at the preliminary design stage of the engine. For the sake of studying the titanium alloy TC4 casing performance, the ballistic tests of flat and curved simulation casing are implemented by using two flat blades of different sizes as the projectile. The impact mechanism and failure of the target are discussed. Impact of the projectile is a highly nonlinear transient process with the large deformation of the target. On the impact, failures of the flat casing and the subscale casing are similar, concluding two parts, the global dishing and localized ductile tearing. The main localized failure mode combines plugging (shear) and petaling (shear) if the projectile perforates or penetrates, while crater (shear) if the projectile rebounds. The ballistic limit equation is verified by the test data and the results show that this empirical equation could be a practical way to estimate the critical velocity.     

Asymptotic Performance of Sparse Signal Detection Using Convex Programming Method

The detection of sparse signals against background noise is considered. Detecting signals of such kind is difficult since only a small portion of the signal carries information. Prior knowledge is usually assumed to ease detection. In this paper, we consider the general unknown and arbitrary sparse signal detection problem when no prior knowledge is available. Under a Neyman-Pearson hypothesis-testing framework, a new detection scheme is proposed by combining a generalized likelihood ratio test (GLRT)-like test statistic and convex programming methods which directly exploit sparsity in an underdetermined system of linear equations. We characterize large sample behavior of the proposed method by analyzing its asymptotic performance. Specifically, we give the condition for the Chernoff-consistent detection which shows that the proposed method is very sensitive to the 2 norm energy of the sparse signals. Both the false alarm rate and the miss rate tend to zero at vanishing signal-to-noise ratio (SNR), as long as the signal energy grows at least logarithmically with the problem dimension. Next we give a large deviation analysis to characterize the error exponent for the Neyman-Pearson detection. We derive the oracle error exponent assuming signal knowledge. Then we explicitly derive the error exponent of the proposed scheme and compare it with the oracle exponent. We complement our study with numerical experiments, showing that the proposed method performs in the vicinity of the likelihood ratio test (LRT) method in the finite sample scenario and the error probability degrades exponentially with the number of observations.