惯性+星光折射定位组合导航技术研究

由于现有惯性器件精度水平有限,纯惯性导航误差较大,因此需要采用组合导航的方式来提高导航精度。目前,全自主组合导航方式中传统的惯性+星光定姿组合导航方法只能实现定姿,不能实现定位,无法修正加速度表测量误差引起的惯性导航误差,故在精度上可提升空间有限。为此,提出了一种惯性+星光折射定位组合导航方法,重点从星光折射定位原理、大气折射模型、非线性滤波和选星策略几个方面进行论证及分析。通过理论分析与数学仿真相结合的手段,验证了星光折射定位原理的正确性及工程可行性,可将自主导航精度提升至100m,从而为进一步提高自主导航精度提供了一种技术途径。     

STABC-IR: An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism

The battlefield environment is changing rapidly, and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage. The current Intention Recognition (IR) method for air targets has shortcomings in temporality, interpretability and back-and-forth dependency of intentions. To address these problems, this paper designs a novel air target intention recognition method named STABC-IR, which is based on Bidirectional Gated Recurrent Unit (BiGRU) and Conditional Random Field (CRF) with Space-Time Attention mechanism (STA). First, the problem of intention recognition of air targets is described and analyzed in detail. Then, a temporal network based on BiGRU is constructed to achieve the temporal requirement. Subsequently, STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements. Finally, an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment. The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%, which is higher than other latest intention recognition methods. STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability, which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.     

基于深度卷积网络的SAR图像目标检测识别

在SAR图像解译应用领域,目标的自动检测与识别一直是该领域的研究重点和热点,也是该领域的研究难点。针对SAR图像的目标检测与识别方法一般由滤波、分割、特征提取和目标识别等多个相互独立的步骤组成。复杂的流程不仅限制了SAR图像目标检测识别的效率,多步骤处理也使模型的整体优化难以进行,进而制约了目标检测识别的精度。采用近几年在计算机视觉领域表现突出的深度学习方法来处理SAR图像的目标检测识别问题,通过使用CNN、Fast RCNN以及Faster RCNN等模型对MSTAR SAR公开数据集进行目标识别及目标检测实验,验证了卷积神经网络在SAR图像目标识别领域的有效性及高效性,为后续该领域的进一步研究应用奠定了基础。     

智能博弈趋势下未来空天防御体系展望

当前，军事智能化正成为继机械化、信息化之后推动新一轮军事变革的强大动力，深刻影响着未来战争的制胜机理、作战规则及战争形态。本文全面分析了智能化在未来空天防御体系的应用优势，结合国外智能化防御体系发展特点，提出了未来智能空天防御体系发展设想，梳理了空天防御体系智能化关键技术，阐述了空天防御体系智能化变革的重要意义。     

A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning, flight scheduling, airport operation, and passenger service. Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity, which brings critical challenges to accurate flight delay prediction. In recent years, Graph Convolutional Networks (GCNs) have become popular in flight delay prediction due to the advantage in extracting complicated relationships. However, most of the existing GCN-based methods have failed to effectively capture the spatial–temporal information in flight delay prediction. In this paper, a Geographical and Operational Graph Convolutional Network (GOGCN) is proposed for multi-airport flight delay prediction. The GOGCN is a GCN-based spatial–temporal model that improves node feature representation ability with geographical and operational spatial–temporal interactions in a graph. Specifically, an operational aggregator is designed to extract global operational information based on the graph structure, while a geographical aggregator is developed to capture the similar nature among spatially close airports. Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.     

一种针对多光谱遥感图像的桥梁快速识别算法

提出了一种针对多光谱图像中桥梁的识别算法。首先,根据水体和背景地物在不同光谱波段的亮度差异,计算多光谱图像的水体指数得到水体增强图,搜索其具有明显双峰的直方图得到最优阈值,实现河流的完整提取；其次,利用桥梁的存在会导致局部水体的光谱异常,沿河流中间线进行潜在桥梁区域的快速提取；再进一步利用桥梁长度以及与河流的空间关系进行鉴别,有效剔除虚警。利用 SPOT4遥感影像进行实验,结果表明本文算法运算量小,对于多个桥梁的识别具有很好的实用性。     

一种基于State模式的惯导系统软件设计方法

State模式是一种面向对象的设计方法。针对惯导系统软件设计中的问题,提出了一种基于State模式的惯导系统软件设计方法,并将其应用于惯导系统软件分支流程的设计过程中。与传统设计方法相比,该方法将逻辑判断和处理封装在状态对象中,为不同系统状态子类声明了一个公共接口,用子类实现特定状态下的行为操作,避免了多种状态转换时逻辑判断的复杂度,降低了惯导系统软件的耦合程度,增强了代码的可靠性、健壮性和可移植性,从而提高了软件质量。     

基于LOFAR谱图的水下目标识别方法

基于LOFAR谱图的主成分分析法是目前用于对水下目标识别的重要方法之一,文中详细分析了该方法的基本原理和具体应用过程,并对潜艇辐射噪声的实测数据进行了仿真和分类试验,给出了仿真图和试验结果.结果表明,运用该方法对水下目标进行识别的识别率较高.     

液气式针栓喷注单元喷雾场结构试验研究

径向孔型针栓喷注器相对于径向缝型针栓喷注器具有更复杂的喷雾场。为了研究径向孔型针栓喷注器的喷雾场结构，将径向孔型针栓喷注器简化为单个气体射流与液膜碰撞的针栓喷注单元，采用了背景光成像系统结合激光相位多普勒技术（PDA），以水和空气为模拟介质，对液气式针栓喷注单元的喷雾场进行了试验研究。试验结果表明，液气式针栓喷注单元喷雾的三维结构呈现“喇叭”状。根据喷雾的形成过程及液滴的分布，液气式针栓喷注单元喷雾可以划分为4个区域：碰撞区、液滴区、液雾区及液丝区。液气式针栓喷注单元喷雾的分布范围可由内边界角、外边界角、中线角及散布角表示，均随局部动量比的增大而增大。液滴区的粒子主要由碰撞过程产生，SMD较大；液雾区的粒子经碰撞过程产生后，在气动力作用下进一步雾化，SMD小。由于液雾区的速度和粒径同时受到气动力作用的影响，粒径分布与速度分布在空间上呈现负相关趋势。     

基于N-dot的涡扇发动机加速控制器设计

针对双转子民用涡扇发动机,进行基于转子加速度的加速控制器设计研究。采用常规的PI控制器结构,针对结构中所需的转子加速度指令,给出了1种满足要求的设计方法,并将PI控制参数的求解过程转化为带约束的非线性优化问题,利用模式搜索算法求解。构建包含稳态控制器和加速控制器的涡扇发动机闭环控制回路,进行加速过程仿真测试。结果表明:所设计的加速控制器满足涡扇发动机加速过程的性能要求,对于工程设计与应用具有一定的参考价值。