直升机目标的雷达特性分析

为了研究直升机目标的雷达特性,采用了理论分析、统计分析和性能指标体系分析的方法,对直升机与固定翼飞机、不同直升机之间目标的雷达特性进行了对比分析;建立了直升机的雷达特性统计模型,对统计特性结果进行了分析;采用性能指标体系分析的方法对旋翼调制特性进行了分析,得到了直升机目标的雷达特性影响因素和分布规律。其方法和结果可以作为直升机系统、防空武器系统和直升机靶标的设计参考。     

基于惯性系的双轴旋转式惯导系统转位方法

针对双轴旋转式惯导系统中不能自动补偿标度因数误差与地球自转耦合误差的问题,提出了一种改进的转位方法。该方法不增加转轴数目,通过补偿地球自转在转轴平面的投影抑制耦合误差项,计算结果表明该方法适用于激光陀螺惯导系统和光纤陀螺惯导系统。利用改进的转位方法对双轴旋转式惯导系统进行仿真,仿真结果验证了该方法的可行性。研究结果为双轴旋转式惯导系统的工程设计和改进提供了一定的理论支持。     

基于主分量分析的多维时间序列特征选择方法研究

研究了多维时间序列的特征选择问题。首先,讨论了主分量分析的原理;然后,将提出的斜率趋势算法和核函数分类算法应用于多维时间序列的特征选择过程中,并探讨了两类方法的优缺点;最后,采用两种算法对目标距离像多维时间序列进行了仿真实验。实验结果表明了所应用方法的可行性和有效性.     

基于图论分割的多光谱图像非监督分类方法

针对传统基于像素的多光谱遥感图像分类方法存在的"麻点"现象、采样成本高等问题,提出了一种基于图论分割的非监督分类方法,首先采用基于图论的分割算法,按局部邻近相似像素点分割成若干子区域,再以分割后子区域为基本单元,整体进行模糊 C均值聚类,最终实现对多光谱图像的非监督分类.实验证明,该方法结合了局部邻近像素点的相互关系以及相似区域的整体特征,有效解决了麻点问题,具有较高的分类精度和算法效率,降低了采样成本.     

可重构自适应无线电引信干扰技术研究

根据无线电引信的工作特点,提出了可重构自适应无线电引信干扰技术。设计了自适应无线电引信干扰技术方案,分析了无线电引信信号调制识别技术。对该技术的应用前景进行了展望。     

基于支持向量机的无参考遥感图像质量评价方法

针对遥感图像主观评价方法的低效率以及常用客观评价方法无法充分考虑人眼对图像的感知特性的问题,文章提出了一种基于支持向量机的无参考遥感图像质量（quality）评价方法。首先建立遥感图像主观评价库,然后在不需要图像失真信息的基础上,利用支持向量机（SVM）将图像的失真类型分为三类,并对每类进行单项评价,再通过加权得到遥感图像的总评分,最后将本文方法、信噪比与信息熵的评价结果回归到主观评价空间并进行对比。实验证明,文章所提方法能客观地评价遥感图像的质量,且优于信噪比和信息熵两种质量评价方法,其结果与人眼视觉感受相符。     

Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system

Identification of the appropriate combination of classifier and dimensionality reduction method has been a recurring task for various hyperspectral image classification scenarios. Image classification by multiple classifier system has been evolving as a promising method for enhancing accuracy and reliability of image classification. Because of the diversity in generalization capabilities of various dimensionality reduction methods, the classifier optimal to the problem and hence the accuracy of image classification varies considerably. The impact of including multiple dimensionality reduction methods in the MCS architecture for the supervised classification of a hyperspectral image for land cover classification has been assessed in this study. Multi-source airborne hyperspectral images acquired over five different sites covering a range of land cover categories have been classified by a multiple classifier system and compared against the classification results obtained from support vector machines (SVM). The MCS offers acceptable classification results across the images or sites when there are multiple dimensionality reduction methods in addition to different classifiers. Apart from offering acceptable classification results, the MCS indicates about 5% increase in the overall accuracy when compared to the SVM classifier across the hyperspectral images and sites. Results indicate the presence of dimensionality reduction method specific empirical preferences by land cover categories for certain classifiers thereby demanding the design of MCS to support adaptive selection of classifiers and dimensionality reduction methods for hyperspectral image classification.     

Moderate geomagnetic storms of January 22–25, 2012 and their influences on the wave components in ionosphere and upper stratosphere-mesosphere regions

Moderate geomagnetic storms occurred during January 22–25, 2012 period. The geomagnetic storms are characterized by different indices and parameters. The SYM-H value on January 22 increased abruptly to 67 nT at sudden storm commencement (SSC), followed by a sharp decrease to −87 nT. A second SSC on January 24 followed by a shock on January 25 was also observed. These SSCs before the main storms and the short recovery periods imply the geomagnetic storms are CME  -driven. The sudden jump of solar wind dynamic pressure and IMF _Bz$B_z$ are also consistent with occurrence of CMEs. This is also reflected in the change in total electron content (TEC) during the storm relative to quiet days globally. The response of the ionospheric to geomagnetic storms can also be detected from wave components that account for the majority of TEC variance during the period. The dominant coherent modes of TEC variability are diurnal and semidiurnal signals which account upto 83% and 30% of the total TEC variance over fairly exclusive ionospheric regions respectively. Comparison of TEC anomalies attributed to diurnal (DW1) and semidiurnal (SW2) tides, as well as stationary planetary waves (SPW1) at 12 UTC shows enhancement in the positive anomalies following the storm. Moreover, the impact of the geomagnetic storms are distinctly marked in the daily time series of amplitudes of DW1, SW2 and SPW1. The abrupt changes in amplitudes of DW1 (5 TECU) and SW2 (2 TECU) are observed within 20°S–20°N latitude band and along 20°N respectively while that of SPW1 is about 3 TECU. Coherent oscillation with a period of 2.4 days between interplanetary magnetic field and TEC was detected during the storm. This oscillation is also detected in the amplitudes of DW1 over EIA regions in both hemispheres. Eventhough upward coupling of quasi two day wave (QTDWs) of the same periodicity, known to have caused such oscillation, are detected in both ionosphere and upper stratosphere, this one can likely be attributed to the geomagnetic storm as it happens after the storm commencement. Moreover, further analysis has indicated that QTDWs in the ionosphere are strengthened as a result of coherent oscillation of interplanetary magnetic field with the same frequency as QTDWs. On the otherhand, occurrences of minor SSW and geomagnetic storms in quick succession complicated clear demarcation of attribution of the respective events to variability of QTDWs amplitudes over upper stratosphere.     

宽带卫星通信信号的多维星座图解调方法

高速卫星数据传输需要利用较高调制维度和较多星座点数来实现,一方面导致接收端具有较大实现复杂度,另一方面系统可靠性极易受信道非线性影响。针对以上问题,通过对多维星座图信号的解调性能进行理论分析,得到极大后验概率（MAP）解调误符号率（SER）的理论公式,在保证系统SER性能的基础上,提出了基于星座图相关的检测方法。该方法利用脉冲正交性得到接收星座点,对其进行向量相关运算得到解调数据,有效降低了解调判决过程的复杂度。针对信号经过高功率放大器（HPA）后的非线性失真问题,建立了HPA信道估计模型,利用最小二乘（LS）信道估计方法对原解调星座图进行修正,提出了基于修正星座图的解调方法。仿真结果表明,该方法在降低实现复杂度的同时,有效提高了系统抗HPA信道非线性失真的能力,可实现对宽带高速卫星通信信号的高效准确解调。     

基于双重分类深度学习的低空目标自动检测方法

由于图像数量多,因此准确、高效的目标检测是提升靶场光测图像处理自动化程度的关键步骤.针对低空目标图像及目标类型多、目标特性变化等情况导致传统目标检测算法适应性差的问题,提出了一种基于双重分类深度学习的低空目标自动检测方法.该方法基于深度学习目标检测框架YOLO V3,根据低空目标的亮度和形状的双重属性特征,将网络输出层...