圆阵相位干涉仪二维测向解模糊新方法

长短基线结合和模糊角度聚类是圆阵相位干涉仪测向中两种常用的解模糊方法。针对角度聚类解模糊方法存在的不足,在对模糊方向的方向矢量投影在阵列所在平面的分布规律进行分析的基础上,提出一种基于模糊方向矢量投影聚类的解模糊新方法。该方法不但有效地减小了聚类所需的计算复杂度,而且易于选择聚类门限。仿真实验表明,所提出的新方法具有很好的解模糊性能。     

复杂曲面零件散乱点云特征点提取

复杂曲面零件的几何特征提取对加工质量的检测及逆向重构具有重要意义。提出了一种复杂曲面零件散乱点云特征点提取方法。首先,提出了基于高斯权重的邻域主成分分析的方法,通过估计每一点邻域的局部变化程度对点云模型进行初始标记;然后,采用基于标记的自动识别法实现特征点的提取;最后,通过特征点聚类的方式去除了特征点集中的异常点,完善了提取的点云特征。该方法直接操作于散乱点云,无需任何的拓扑连接信息。试验结果表明:该方法简单、有效,不需要过多地人为调节参数,特征点提取完整。     

基于CAIV的导弹低成本保障性指标论证技术

利用CAIV技术分别建立反舰导弹的作战效能模型和总拥有费用模型,并以权衡空间为约束条件,以费用最低为目标,建立保障性指标权衡优化模型。该方法可以为导弹武器装备保障性指标论证工作提供必要的理论基础和技术支持。     

高速走丝电火花线切割工作介质性能要求研究

系统分析了高速走丝电火花线切割工作介质的主要性能要求,通过使用典型工作介质的切割对比,分析了线切割加工的工艺规律,指出工作介质洗涤能力是影响线切割工艺的最关键因素,对评价工作介质的优劣提出了参考意见。     

基于SPWD多载波频率估计算法的改进

针对目前提出的多载波估计算法存在的速度与精度的问题,基于模糊控制理论,改进和完善了多载波信号载频估计算法.通过对多载波时隙ALOHA网络真实卫星信号的载频频点提取,验证了该改进算法的时效性.     

Using the MIR bands in vegetation indices for the estimation of grassland biophysical parameters from satellite remote sensing in the Alps region of Trentino (Italy)

Development of new methods for estimating biophysical parameters can be considered one of the most important targets for the improvement of grassland parameters estimation at full canopy cover. In fact, accurate assessment methods of biophysical characteristics of vegetation are needed in order to avoid the uncertainties of carbon terrestrial sinks.

Remote sensing is a valid tool for scaling up ecosystem measurements towards landscape levels serving a wide range of applications, many of them being related to carbon-cycle models. The aim of this study was to test the suitability of satellite platform sensors in estimating grassland biophysical parameters such as LAI, biomass, phytomass, and Green herbage ratio (GR). Also, we wanted to compare some of the most common NIR and red/green-based vegetation indices with ones that also make use of the MIR band, in relation to their ability to predict grassland biophysical parameters.

Ground-truth measurements were taken on July 2003 and 2004 on the Monte Bondone plateau (Italian Alps, Trento district) in grasslands varying in land use and management intensities. From satellite platforms, an IRS-1C-LISS III image (18/07/2003; 25 m resolution in the visible-NIR and 70 m resolution in the MIR) and a SPOT 5 image (27/07/2004, 10 m resolution in the visible-NIR and MIR) were used.

LAI, biomass, and phytomass measurements showed logarithmic relationships with the investigated NIR and red/green-based indices. GreenNDVI showed the highest R² values (0.59, IRS 2003; 0.60, SPOT 2004). Index saturation occurred above approximately 100–150 g m⁻² of biomass (LAI 1.5–2). On the other hand, GR relationships were shown to be linear. MIR-based indices performed better than NIR and red/green-based ones in estimating biophysical variables, with no saturation effect. Biomass showed a linear regression with Canopy Index (MIR/green ratio) and with the Normalised Canopy Index (NCI) calculated as a normalised difference between MIR and green bands (IRS: R² = 0.91 and 0.90, respectively. SPOT: R² = 0.63 and 0.64). Similar correlations could also be found for LAI and phytomass, and GR predictability was shown to be higher than NDVI and GreenNDVI. According to these results obtained in the investigated areas, phytomass, biomass, LAI, and GR are linearly correlated with the investigated MIR band indices and as a result, these parameters could be estimated from the adopted satellite platforms with limited saturation problems.     

Materials selection for electronic enclosures in space environment considering electromagnetic interference effect

Using low power electronic devices for space applications to reduce the mass and energy consumption has lead to electromagnetic interference (EMI) problem. Electronic enclosures are used to shield electronic devices against EMI. In the past, electromagnetic shielding has been mainly the only criteria considered in electronic enclosure design. However, there are several structural and thermal requirements for selection of shielding materials which should also be taken into account. In this research work, three quantitative materials selection methods, i.e. Digital Logic (DL), Modified Digital Logic (MDL), and Z-transformation, are employed to select the best material from among a list of candidate materials. Composite and metallic electronic enclosures are explored and the best material is selected. Z-transformation method is applicable to both of the considered case studies while DL and MDL can only be used for solving one of them. Z-transformation method ranks aluminum as the first choice among various metallic materials. The wide range of Z-transformation application and its practical results confirm the superiority of Z-transformation method over DL and MDL methods.     

地磁Ap指数滞后太阳周循环分析

把1932-2006年地磁Ap指数12个月流动均值分解成为(Ap)R和(Ap)I.其中(Ap)R为太阳黑子数R的线性函数,与太阳黑子数R相位相同,可能对应于日冕物质抛射(CME)等地磁控制因素. (Ap)I分量与太阳黑子数R相位相差约180°,该分量可能对应于极冕洞变化(从太阳峰年开始,由日面极区逐渐向赤道延伸).以地磁Ap指数与太阳黑子数R滞后非常严重的第20太阳周为例,证实了(Ap)I分量与极冕洞向赤道延伸循环变化相对应.因此极冕洞循环变化可能是导致地磁扰动指数与太阳周循环相位不一致,出现滞后现象的一个十分重要原因.     

Geoeffectivity of Coronal Mass Ejections

Coronal mass ejections and post-shock streams driven by them are the most efficient drivers of strong magnetospheric activity, magnetic storms. For this reason there is considerable interest in trying to make reliable forecasts for the effects of CMEs as much in advance as possible. To succeed this requires understanding of all aspects related to CMEs, starting from their emergence on the Sun to their propagation to the vicinity of the Earth and to effects within the magnetosphere. In this article we discuss some recent results on the geoeffectivity of different types of CME/shock structures. A particularly intriguing observation is that smoothly rotating magnetic fields within CMEs are most efficient in driving storm activity seen in the inner magnetosphere due to enhanced ring current, whereas the sheath regions between the shock and the ejecta tend to favour high-latitude activity.     

An unsupervised classification method of flight states for hypersonic targets based on hyperspectral features

In response to the challenges of aerospace defense caused by the rapid development of hypersonic targets in recent years, the research on the unsupervised classification of flight states for hypersonic targets is carried out in this paper, which is based on the Hyperspectral Features (HFs) of hypersonic targets covered with plasma sheath during high-speed flight. First, a new concept of the super node is defined to improve classification accuracy by alleviating the intraclass variability of HFs. Then, the frequency domain information of the curve of HFs is utilized to reduce the feature redundancy according to the prior theoretical knowledge that the fluctuation characteristics of HFs of the same flight states are similar. Finally, an unsupervised classification method based on the Density Peak Clustering (DPC) for HFs is designed to class flight states after eliminating the impact of intraclass variability and feature dimension redundancy. The proposal is compared with the traditional classification algorithms on simulated hyperspectral data sets of typical flight states of the hypersonic vehicle and an actual-observation hyperspectral data set. The results indicate that the performance of our proposal has competitive advantages in terms of Overall Accuracy (OA), Average Accuracy (AA) and Kappa coefficient.