一种基于CCSDS AOS的星上数据系统仿真分析

高级在轨系统 (AOS,Advanced Orbiting System)是空间数据系统咨询委员会(CCSDS,Consultative Committee for Space Data Systems)制定的关于空 -空和空 -地的数据管理系统。文中在详细分析 CCSDS AOS协议的基础上 ,应用这一协议设计了一种星上数据处理系统 ,它的功能是把不同类型、不同速率的数据进行封装 ,形成统一的虚拟信道数据单元 (VCDU ,VirtualCode Data Unit)格式 ,然后通过信息处理模块 ,把各条虚拟信道上 VCDU复接成连续的数据流。在信息处理模块中 ,设计了一种 VCDU转换算法 ,根据各信道的数据流的速率来分配相应的时间。仿真表明 ,这种算法能保证各虚拟信道能高效、公平地共用同一条物理信道。     

一种新的共轭增强MUSIC测向算法

提出一种新的测向算法。利用阵列输出的非零时延互相关函数及其共轭形成伪阵列输出,从而得到伪协方差矩阵,对其进行特征分解,利用信号子空间与噪声子空间的正交性,搜索得到的空间谱函数的极大值点对应的角度就是波达方向。仿真实验表明,新算法可对多于阵元数的信号进行测向,其测角精度和分辨力优于MUISC和MUSIC-like算法。     

利用CAD工程图自动生成数控加工程序

本文介绍了利用工程图自动生成孔加工程序的方法。既可以自动选择图中不同尺寸的孔,又可以避免同一坐标位置多个同尺寸的圆的问题。同时又克服了手工编程容易出现错误的问题。也避免了其他自动编程软件需要在图上选择要加工的孔困难。     

MTD雷达系统仿真及其抗干扰性能分析

动目标检测(MTD)雷达作为一种被广泛用于强杂波背景下检测运动目标的雷达,其各项性能越来越受关注.在分析MTD雷达信号处理系统的基础上,对某MTD雷达系统进行了仿真,得到了该系统中各个处理点上的具体信号形式.该仿真模型能较好地满足分析MTD雷达各项性能的需要.最后给出了发现概率随信杂比变化的曲线来说明该雷达的抗干扰性能.     

星载SAR成像处理系统中多线程矩阵转置的设计和实现

矩阵转置被广泛地应用在星载合成孔径雷达的地面成像处理中,由于对原始回波数据快速成像的需求,根据现有共享内存系统的特点,提出利用多线程矩阵转置的并行处理方法。在Linux环境下,利用基于Pthread的线程调度方法和封装类,在研究和分析矩阵转置的几种处理方法的基础上,设计和实现多线程矩阵转置。通过在HP Proliant DL580平台上的实验证明,多线程矩阵转置与传统的矩阵转置相比,能充分利用处理器的资源,具有良好的并行扩展性,有利于进行快速成像处理。     

一种计算二维图象矩的神经网络方法

提出一种计算二维图象矩的神经网络方法,首先,导出一维信号矩与一维Ｈａｄａｍａｒｄ变换之间的关系。通过２Ｎ－一维Ｈａｄａｍａｒｄ变换和极少量加法,移位及乘法运行可计算二维图象矩。然后给出用Ｔａｎｋ－Ｈｏｐｆｉｅｌｄ神经网络实现Ｈａｄａｍａｒｄ变换的方法,并对该网络作了改进。     

扩压叶栅剪敏液晶试验与图像处理

基于剪敏液晶涂层(SSLCC)材料的光学特性,发展了适合于内流场狭小空间环境下的SSLCC边界层流动显示技术:设计并加工了微型摄像头-发光二极管(LED)组合式图像采集设备解决拍摄光路问题;基于二维SSLCC图像与三维模型的空间映射关系,建立了真实模型的三维重构方法;通过SSLCC图像光谱Hue色相值转化,实现了液晶图像信息的定量分析。以西北工业大学高亚声速平面叶栅风洞为平台,开展了某扩压叶栅吸力面边界层流态的剪敏液晶流动显示试验。结果表明:所发展的剪敏液晶显示技术可进行叶栅内流场边界层的流态测量;所建立的图像处理方法可为边界层流动特征的辨识及其特征位置确定提供技术支撑;在来流马赫数为0.12、攻角为0°的条件下,叶片吸力面边界层沿流向依次经历了层流边界层分离、再附着及转捩为湍流状态的过程,且边界层的发展受叶栅角区分离流动影响,造成其前缘分离区减小,再附着点和边界层转捩位置向前缘移动。     

弧齿锥齿轮齿面拓扑修形及加工参数计算

为了能够实现对齿面啮合性能的灵活控制,针对弧齿锥齿轮小轮提出一种齿面拓扑修形方法,即借助二阶曲面对齿面偏差拓扑的近似表达,将齿面拓扑修形分解为5个方向:螺旋角修正、压力角修正、齿长曲率修正、齿廓曲率修正及齿面挠率修正,通过改变5个方向的修形系数对小轮齿面拓扑结构进行自由控制。在此基础上,建立齿面偏差与机床加工参数之间的修正数学模型,通过构建敏感性矩阵并采用最小二乘法求解,反求出获得修形齿面的小轮加工参数,以便指导加工。以一对弧齿锥齿轮副为例进行修形啮合分析,仿真结果表明:选取齿长曲率修形系数为0.0001,齿廓曲率修形系数为0.0005,齿面挠率修形系数为0.0003,对齿面进行拓扑修形后传动误差幅值为-25.60″,接触迹线倾斜角度变为54.7°,相比原始结果啮合性能得到改善。滚检接触区与理论仿真结果一致,验证了修形方法的有效性。      

ABORT-like detector to combat active deceptive jamming in a network of LFM radars

This paper studies an electronic counter-counter measures (ECCM) scheme combating against deceptive electronic counter measure (ECM) techniques.An adaptive detector exploiting generalized likelihood ratio test (GRLT) criterion is applied to detect the presence of deceptive jamming in fractional Fourier transform (FrFT) domain.First,the generating mechanism of spurious frequencies is analyzed based on the Volterra serial.The proposed nonlinear distortion model based on power amplifier behavior is robust in distortion analysis when the memory effect is considered.Second,a modified adaptive beamformer orthogonal rejection test (ABORT) like detector in closed form is built.The proposed detector can discriminate the echo and deceptive jamming adaptively by exploiting primary data and secondary data.This ECCM scheme is capable of guaranteeing the performance without the restriction of orthogonality,which is essential for the ABORT detectors.The expansion to radar network is discussed as a special case at the final part of this paper.Numerical simulations demonstrate the effectiveness of the proposed method.     

Mining Spatial Associations with Limited Sensory Information

Abstract

Human navigation in an unknown environment requires an understanding of the spatial relationships of the terrain. For example, a soldier who is on a reconnaissance mission in a new city needs to “know” the spatial layout of the surroundings with high confidence. Oftentimes, this understanding must be acquired within a very short amount of time and with limited sensory inputs. The soldier would benefit from a digital avatar that draws inferences about the spatial layout of the city based on an initial set of observations and guides the soldier either in further exploring the environment or in making decisions based on these inferences. In this paper, we present and evaluate an inductive approach to learning spatial associations using sensory data that is available from the simulation environment of a computer game, Unreal Tournament. We study two kinds of spatial relationships between nodes on a level of a game map: nodes that are placed near each other to satisfy some spatial requirement and nodes that are placed near each other to satisfy the design preferences of a level architect. We show that we can infer both kinds of relationships using an association rule mining algorithm. Furthermore, we show how to use an ontology to distinguish between these relationships in order to discover different types of spatial arrangements on a specific map. We discuss how the inferred associations can be used to control an avatar that makes recommendations for navigating unexplored areas on a map. We conclude with some thoughts on the applicability of our methods to scenarios in the real world, beyond the simulation environment of a game, and on how the learned associations can be represented and queried by a simple question-answer type system.