一种软件脆弱性自动分析定位的方法

摘要： 为了更好地满足航空航天领域中使用的多种嵌入式软件的高可靠性要求，设计一种软件脆弱性自动分析定位的方法.该方法基于程序切片技术，改进现有的前向计算动态切片算法，利用动态程序切片收集程序运行时的动态信息，构造程序切片谱，设定相关统计量统计程序语句的脆弱性可疑度，生成脆弱性定位报告.在发现软件中存在的脆弱性后，该方法对导致此脆弱性的程序代码根源实现了自动分析定位.基于此方法设计开发了软件脆弱性自动定位工具并进行了实例验证分析，实验证明了该方法的有效性.     

Geometric reasoning under uncertainty for map-based localization

Map-based navigation in outdoor terrain lacking man-made structures or other highly distinctive landmarks can produce severe localization problems. This paper presents an approach to navigation which implements high level geometric reasoning and matching strategies based on those used by skilled human navigators. This approach, which is demonstrated on a real example involving imagery of mountainous terrain obtained with a video camera and USGS map data, is designed to avoid many of the pitfalls occurring when an attempt is made to navigate by modeling the environment mathematically. It exploits feature attributes which cannot be easily expressed quantitatively but are central to the successful human navigation process.     

Real time localization of gamma ray bursts with INTEGRAL

The INTEGRAL satellite has been successfully launched in October 2002 and has recently started its operational phase. The INTEGRAL burst alert system (IBAS) will distribute in real time the coordinates of the gamma ray bursts (GRBs) detected with INTEGRAL. After a brief introduction on the INTEGRAL instruments, we describe the main IBAS characteristics and report on the initial results. During the initial performance and verification phase of the INTEGRAL mission, which lasted about two months, two GRBs have been localized with accuracy of 2–4 arcmin. These observations have allowed us to validate the IBAS software, which is now expected to provide quick (few seconds delay) and precise (few arcmin) localization for 10–15 GRBs per year.     

孔-轴-孔配合处的孔位移制误差计算公式的论证

文[1]、[2]中通过线性化假定推导出计算孔-轴-孔配合处的孔位移制误差的简化公式,但缺乏充足的理论支持。本文用矢量随机误差分析法导出计算该误差的另一公式,通过典型孔-轴-孔配合的孔位移制误差计算,证明该简化公式不仅使用方便,而且准确、可靠。     

基于改进WKNN的位置指纹室内定位算法

位置指纹算法是目前解决室内定位问题的主要方法,指纹特征和匹配算法为影响算法精度的两大因素.针对室内复杂环境下Wi-Fi信号强度波动较大的现象,提出了基于方差的加权距离以改进WKNN算法.在离线特征提取阶段,选择了均值和方差两个特征值,既反映该采样点的RSS幅值,也反映该点RSS的波动情况;在线阶段,根据方差提出了加权距离进行相似度的计算,查找距离最近的K近邻点,并以实际环境下采集的数据验证了改进WKNN算法在RSS波动大的情况下对定位效果的改善,在综合考虑了AP组合的影响后,实现了误差均值为1.456m的定位效果.     

某型飞机机翼整体油箱工艺研究

介绍了某型机机翼整体油箱的研制过程,针对总体气动、结构和使用维护等对整体油箱提出的性能要求,工程部门相应采用严格的环境措施、确保协调精度的预装配措施以及湿连接、密封装配措施等,解决了研制过程中诸多关键难题,保证了整体油箱的研制进度和产品质量,经各种试验检测和试飞考核,该整体油箱质量稳定可靠,性能良好。     

运动单站干涉仪相位差直接定位方法

针对传统干涉仪测向定位在低信噪比(SNR)下性能降低、多频段系统复杂等缺点,本文直接利用运动单站干涉仪模糊相位差(PD)实现对地面静止辐射源定位.为解决该定位问题,结合干涉仪相位差的几何特性,提出了一种多角度距离参数化高斯和滤波(MB-RP-GMF)定位解算方法,利用相位差误差检测获得多个初始方位角,通过距离参数化得到一组滤波初始值,采用扩展卡尔曼滤波(EKF)进行定位计算.仿真结果表明,该定位方法在低信噪比下优于仅测向(BO)定位,且仅采用一组干涉仪便可对多个频段辐射源进行定位.     

应用ZigBee技术的航天器内部三维空间定位方案

针对航天器内部缺少三维空间定位方案的问题,提出一种应用紫蜂(ZigBee)技术的无线传感器网络定位方案,即通过在航天器内部布置ZigBee无线传感器网络,设置若干数量的参考节点和未知节点,利用参考节点的先验位置信息,以及针对未知节点的测距信息,完成对未知节点的三维空间定位。建立基于参考节点平面的三维空间坐标系,根据到达角度(AOA)计算出未知节点的法向坐标(Z坐标),将未知节点投影至参考节点所在平面(XOY平面),利用三边定位法计算出未知节点在XOY平面的坐标。该方案理论上最少只需要6个参考节点,就可以实现对航天器内部未知节点的定位,并且不需要时间同步,适合于无线传感器网络的低复杂度设计需求;利用到达时间差(TDOA)算法进行AOA计算,通过对参考节点进行分层布局,避免使用复杂的天线阵列技术。仿真验证结果表明:本文方案具有较高的定位精度,同时具有较低的硬件和组网要求,以及较低的计算和通信开销,适合于航天器内部的三维空间定位。     

A closed-form solution for moving source localization using LBI changing rate of phase difference only

Due to the deficiencies in the conventional multiple-receiver localization systems based on direction of arrival(DOA) such as system complexity of interferometer or array and amplitude/phase unbalance between multiple receiving channels and constraint on antenna configuration,a new radiated source localization method using the changing rate of phase difference(CRPD)measured by a long baseline interferometer(LBI) only is studied. To solve the strictly nonlinear problem, a two-stage closed-form solution is proposed. In the first stage, the DOA and its changing rate are estimated from the CRPD of each observer by the pseudolinear least square(PLS) method,and then in the second stage, the source position and velocity are found by another PLS minimization. The bias of the algorithm caused by the correlation between the measurement matrix and the noise in the second stage is analyzed. To reduce this bias, an instrumental variable(IV) method is derived. A weighted IV estimator is given in order to reduce the estimation variance. The proposed method does not need any initial guess and the computation is small. The Cramer–Rao lower bound(CRLB) and mean square error(MSE) are also analyzed. Simulation results show that the proposed method can be close to the CRLB with moderate Gaussian measurement noise.