基于边缘与颜色信息的车牌精确定位算法

针对现有车牌定位算法定位率不高和速度慢等问题,结合数学形态学,提出了一种基于车牌边缘检测与颜色信息的精确定位算法.首先对图像进行灰度化、平滑去噪及灰度拉伸等预处理,然后对图像进行垂直边缘检测和二值化处理;再采用线过滤算法滤去干扰边缘线信息,并利用车牌区域灰度变化次数多的特性定位出车牌上下边缘;对经过上下边缘定位后的车牌采用点密度过滤算法滤去零散点,接着采用数学形态学方法寻找连通域粗定位出车牌区域;最后对粗定位车牌在HSV空间进行颜色分割和倾斜矫正,从而精确定位出车牌.实验结果表明,该算法能够实现车牌的快速精确定位.      

基于HLA的防空导弹武器系统仿真平台研究

介绍了一个以防空导弹武器系统为背景,基于高级体系结构(HLA, High Level Architecture)技术实现的包含虚拟现实技术的综合仿真平台.着重论述了该仿真平台的构成、开发过程及其关键技术,包括HLA、基于FEDEP(Federation Development and Execution Process)模式的开发过程,层次化软件结构、计算机生成兵力(CGF, Computer Generated Forces)技术、面向对象建模以及基于包围盒的碰撞检测等.     

基于小波包分解的早期碰摩故障诊断研究

研究了旋转机械转静件早期碰摩故障信号的检测问题.根据小波包分解能在所有频率范围聚焦,对信号奇异性非常敏感的特性,对比分析了不碰摩和早期碰摩信号的小波包分解细节分量,得出应用小波包分解可实现早期碰摩故障诊断.同时,还研究并实现了碰摩故障的定位问题.      

假目标诱骗对抗反辐射导弹作战效能分析

研究多个假目标保护一个真目标(雷达)对抗反辐射导弹(ARM)的作战效能.在确定ARM瞄准方向的基础上,给出导引头在分辨目标时刻对真假目标截获概率的计算方法;推导出计算真目标受到ARM多次攻击时被杀伤概率的通用公式.研究结果表明:在评估假目标诱骗对抗ARM的效能时,必须考虑目标损失积累的影响;为提高诱骗效能,在增加假目标数量的同时,应降低其易损性,提高发射功率,并采用合理的布局方案.     

多管理域合作检测DDoS攻击的一个方法

为有效地防御分布式拒绝服务 DDoS(Distributed Denial of Service) 的 攻击,提出了基于多域间入侵检测系统 IDSs(Intrusion Detection Systems) 合作的攻击检测模型.围绕重要网络资源,构建了以〈路由器,IDS〉对为基础的闭合的IDS 合作环,通过环上节点间信息共享和合作组内的警报关联分析,能够在DDoS攻击数据包汇聚成致命攻击流之前捕获攻击特征并采取相关措施.提出了合作环组织方式、共享信息交换方式、警报关联算法以及各节点系统逻辑结构.利用原型多域合作入侵检测 MDCI(Multiple Domains Cooperative Intrusion-detection)系统实施了DDoS攻击实验,针对实验数据分析可以看出,合作环模型有效地提高了IDS系统对DDoS攻击的预警速度.      

慢旋非合作目标接近轨迹规划

非合作目标自主在轨服务是在轨服务领域的研究方向,具备避碰能力的接近轨迹规划将在未来的在轨服务任务中扮演重要的角色.给出了一种基于滚动时域的慢旋非合作目标接近轨迹规划方法,利用逻辑变量和连续变量的混合形式来描述避碰约束,从而将轨迹规划问题转化为混合整数规划问题.仿真结果表明,基于滚动时域设计的慢旋非合作目标接近轨迹规划能够使得在轨服务航天器安全地接近终端状态.     

基于实时观测数据的大气密度模式修正

针对国际大气密度模式NRLMSISE-00, 以中国神舟飞船探测数据为基础, 提出一种基于实时大气密度观测数据的模式修正方法. 通过计算分析模式计算结果与探测数据的误差分布特征, 针对地磁相对平静期(Ap≤ 30)模式计算的误差特点, 建立了一种平均误差修正方法, 即认为在相对平静期, 在相同纬度和地方时, 模式误差基本相同, 某一时刻模式预测误差可以近似用与其相同纬度和地方时的平均误差来替代, 从而对模式预测结果进行修正. 以神舟4号探测数据为基础, 通过对模式预测结果采用两种方式进行修正, 可以看到模式误差得到了一定的改善. 采用误差库累积准实时修正, 修正后的误差由原来的20 %降至6 %; 采用误差库5天滑动预报修正后, 模式提前1, 2, 3天的预测误差由原来的20 %分别降至7.8 %, 9.4 %和10.5%.      

基于动态RCS的无人机航迹实时规划

传统的无人机航迹规划主要采用仅考虑无人机与雷达距离的简化雷达威胁模型,未充分考虑无人机雷达散射截面RCS(Radar Cross-Section)随自身姿态角改变而产生的动态变化.据此,提出了无人机周向动态RCS模型,并建立了综合考虑无人机动态RCS与雷达距离的探测概率模型,利用遗传算法进行了基于动态RCS的航迹实时规划,计算结果与传统航迹规划结果进行了对比.仿真结果表明该模型的可行性和有效性,能充分利用无人机自身的优势规避威胁,满足无人机的航迹实时规划的要求.      

Digital signal processing and numerical analysis for radar in geophysical applications

Numerical solutions for signal processing are described in this work as a contribution to study of echo detection methods for ionospheric sounder design. The ionospheric sounder is a high frequency radar for geophysical applications. The main detection approach has been done by implementing the spread-spectrum techniques using coding methods to improve the radar’s range resolution by transmitting low power. Digital signal processing has been performed and the numerical methods were checked. An algorithm was proposed and its computational complexity was calculated.     

Systematic examination of the geomagnetic storm sudden commencement using multi resolution analysis

Geomagnetic storm sudden commencements (SSC) contain a wealth of information which is useful in many applications. It is important to point out that the SSCs used in this study are sampled at the rate of one sample per second in order get use of such high resolution data. In this paper, two studies are made on the geomagnetic SSC and an SSC onset automatic detection algorithm is introduced. The first study is about finding the relationship between the SSC rise time and its amplitude. Where it is found that there is a positive correlation between the amplitude and the amplitude gradient which is the amplitude divided by rise time. The second study is checking the spectrum of the SSC, starting from its onset until the end of the SSC rise time. This check had proved that the SSC contains both low and high frequency regions. This led us to introduce a new term, namely the SSC variation rate (VR). This VR is defined as the maximum rate of change of the field in the higher-frequency region of the SSC. These two studies were the guide to build an SSC automatic detector of one sample per second data using multi resolution analysis (MRA) of the discrete wavelet transform (DWT). The data set contains 134 SSCs with different VRs that were collected from the Circum-pan Pacific Magnetometer Network (CPMN). It is found that the standard deviation of the detection error is 41 s and that the average error is 9 s. From the calculated error distribution function, it is found that the detection error is within the range of −1.5 to 3 min. The detection process, as will be shown in the article, takes 70 s for one station and 3 min if the decision is related to the detection(s) of other stations. These results demonstrate the superiority of the proposed algorithm over other algorithms in which the detection error ranges between −8 and 5 min and the detection process takes 2–10 min. In addition to being faster and more accurate than the other algorithms, the proposed algorithm is the first algorithm that automatically detects the SSC onset times from high-resolution data unlike previous studies that focused on determining the SSC times automatically using one-minute resolution data.