基于流动显示的翼尖涡不稳定频率测量

翼尖涡涡核振荡频率的准确测量是翼尖涡控制技术得以有效实施的重要前提。采用流动显示方法,研究了椭圆机翼翼尖涡在低雷诺数条件下的不稳定特性。分别采用单点谱分析和动力学模态分解技术,从流动显示图像序列中提取了涡核振荡的短波不稳定模态的频率,2种方法得到的频率相对误差最大不超过5%。研究结果表明:涡对的空间运动通常展现出长波与短波模态的耦合,涡核的高频短波振荡耦合在低频长波摆动中,以前者为主要含能模态;短波不稳定性的无量纲振荡频率随雷诺数的增大而增大、随机翼攻角的增大而减小。      

基于CEEMDAN的雷达信号脉内细微特征提取法

有效的信号特征提取是高精度雷达辐射源识别的基础,以脉冲描述字为代表的传统特征已无法满足复杂电磁环境的需要。本文提出一种基于自适应噪声完备集合经验模态分解（CEEMDAN）的有效雷达辐射源脉内细微特征提取算法。雷达信号由对非平稳、非线性信号尤为有效的CEEMDAN分解产生的个别分量重构,抑噪效果通过1 000次蒙特卡罗实验得到验证,同时设计基于该重构的一种脉内特征空间。本文方法与主流特征提取方法的识别精度在6部雷达辐射源产生的3 000个不同脉内调制的加噪信号样本上进行了实验对比,结果表明不同种类信号样本在本文特征空间中清晰可分,本文方法较之主流方法更加精确,尤其在0 dB信噪比（SNR）下仍保持90%以上的高精度。      

军用运输机运输效能指标

给出了运输任务类型和运输效能定义,从效果与耗费两方面着手建立了运输效能指标体系,将运输效能归纳为安全性、质量或体积、距离、准确性、时间、场地、人员、设备及费用等9种因素,在确立各个因素运输效能单项指标的基础上提出了一种运输效能综合指标,以四种典型运输任务为例,建立了常用的运输效能指标。     

Rainfall retrieval over Indian land and oceanic regions from SSM/I microwave data

Present study focuses on the estimation of rainfall over Indian land and oceanic regions from the Special Sensor Microwave/Imager (SSM/I) on the Defense Meteorological Satellite Program (DMSP) F-13. Based on the measurements at 19.35, 22.235 and 85.5 GHz channels of SSM/I Satellite, scattering index (SI) has been developed for the Indian land and oceanic regions separately. These scattering indices were co-located against rainfall from Precipitation Radar (PR) onboard Tropical Rainfall Measuring Mission (TRMM) to develop a new regional relationship between the SI and the rain rate for the Indian land and oceanic regions. A non-linear fit between the rain rate and the SI is established for rain measurement. In order to have confidence in our method, we have also estimated rainfall using the global rainfall and scattering index relationship developed by Ferraro and Marks [Ferraro, R.R., Marks, G.F. The development of SSM/I rain rate retrieval algorithms using ground based radar measurements. J. Atmos. Ocean. Technol. 12, 755–770, 1995]. The validation with the rain-gauge shows that the present scheme is able to retrieve rainfall with better accuracy than that of Ferraro and Marks (1995). Further intercomparison with TRMM-2A12 and validation with rain-gauges rainfall showed that the present algorithm is able to retrieve the rainfall with reasonably good accuracy.     

民用飞机航材销售费用分解结构模型研究

当前我国民用飞机缺乏合理、灵活的航材价格计算模型,而合适的航材销售费用分解结构模型可以降低运营成本、增加利润。本文综合多种因素的影响,从不同的维度对航材定价进行研究,阐述民用飞机航材成本费用的构成;从全寿命周期的角度对航材价格影响因素进行分析,构建国产民用飞机全寿命周期不同航材在多种因素影响下的销售费用分解结构模型。结果表明：构建的分解结构模型合理、有效,能够指导国产民用飞机型号航材定价分析,航材销售价格影响因素分析能够结合不同部门维度或工作维度对全寿命周期内的航材成本进行结构分解,实现利润最大化。     

一种混合的扩展目标跟踪方法

与传统的目标跟踪不同,扩展目标跟踪（EOT）不忽略目标的轮廓特征,同时对目标的质心运动学状态和轮廓特征进行估计。基于随机矩阵的扩展目标跟踪方法用随机正定矩阵来描述目标的轮廓特征,并且建立了适合扩展目标跟踪的量测模型。为了改善目标机动时的跟踪性能,根据椭圆（体）与正定矩阵的关系,提出基于椭圆（体）拟合的扩展目标跟踪方法。进一步地,为了综合上述两类方法的优点,提出一种混合的扩展目标跟踪方法,能够根据目标机动与否在两类方法中进行选择。仿真结果表明,该混合方法的轮廓特征估计误差低于前述两类方法,质心运动学状态的估计性能也更好。     

Storm-time atmospheric density modeling using neural networks and its application in orbit propagation

Upper atmospheric densities during geomagnetic storms are usually poorly estimated due to a lack of clear understanding of coupling mechanisms between the thermosphere and magnetosphere. Consequently, the orbit determination and propagation for low-Earth-orbit objects during geomagnetic storms have large uncertainties. Artificial neural networks are often used to identify nonlinear systems in the absence of rigorous theory. In the present study, an attempt has been made to model the storm-time atmospheric density using neural networks. Considering the debate over the representative of geomagnetic storm effect, i.e. the geomagnetic indices ap and Dst, three neural network models (NNM) are developed with ap, Dst and a combination of ap and Dst respectively. The density data used for training the NNMs are derived from the measurements of the satellites CHAMP and GRACE. The NNMs are evaluated by looking at: (a) the mean residuals and the standard deviations with respect to the density data that are not used in training process, and (b) the accuracy of reconstructing the orbits of selected objects during storms employing each model. This empirical modeling technique and the comparisons with the models NRLMSIS-00 and Jacchia-Bowman 2008 reveal (1) the capability of neural networks to model the relationship between solar and geomagnetic activities, and density variations; and (2) the merits and demerits of ap and Dst when it comes to characterizing density variations during storms.     

S型进气道沿程结冰参数变化的数值模拟

采用欧拉-欧拉法,对低来流马赫数和大气结冰条件下S型进气道沿程的空气-过冷水滴两相流场进行了数值计算,获得了进气道沿程和出口面上结冰参数的分布和变化特征.计算结果表明:当来流马赫数从0.1增加到0.5时,进气道入口处附近相对于来流的气动降温从6K增大到17K左右,进气道出口面上的温度畸变指数逐渐增大,液态水含量畸变指数先急剧增大后缓慢减小,在研究范围内来流马赫数为0.2时液态水含量畸变值达到最大.      

基于核部分非负矩阵分解的亚像元级地物光谱分析

为了进一步提高亚像元级地物的光谱分析精度,提出了一种基于核部分非负矩阵分解(Kernel Protection Non-negative Matrix Factorization,KPNMF)的非线性解混算法.首先通过基于凸面几何理论的端元提取方法提取纯像元端元候选像素集合,然后根据候选像素的空间纯度指数判断纯像元端元.在纯像元端元信息已知的条件下,利用核方法对部分非负矩阵分解(Protection Non-negative Matrix Factorization,PNMF)进行推广,构造相应的目标函数,推导迭代求解过程,分解求得亚像元端元光谱和所有端元的丰度.试验结果表明,提出的解混算法具有良好的非线性分解能力,解混结果优于线性解混算法.