基于OPENGL 的雷达/ARPA 模拟器

针对雷达/ARPA模拟器的特点,利用使用OPENGL图形库开发了雷达/ARPA模拟器,详细介绍了地形数据的表示和抽稀方法,雷达图像的显示方法和静态目标回波的产生方法,实验表明本文介绍的技术能实现较好的仿真。     

机载PD雷达模拟器目标检测仿真研究

在针对机载航电设备系统的飞行训练模拟器中，雷达模拟器与惯导、气象与地理、电子对抗模拟器构成一个分布式交互仿真系统。目标的检测模拟是雷达模拟器设计中的一个重要问题。该文讨论在一定的气象条件、地理环境等情况下，模拟机载PD雷达设备完成目标自动检测的方法。根据PD雷达回波频谱特点，将其划分为主杂波、旁瓣杂波区和热噪声区三个区间，分别计算目标的信噪(杂)比，采用加权形式的随机数与检测概率进行比较的方法进行判断。进行了仿真实验，结果表明该方法判断过程明确，具有实用性。     

基于PCI接口的合成孔径雷达目标模拟器的设计与实现

介绍了一种基于PCI接口的合成孔径雷达目标模拟器的设计与实现.该模拟器将已存于PC硬盘中的雷达原始回波数据或按照某种算法生成的雷达仿真数据,转换成I/Q两路视频信号.着重介绍了系统的硬件结构设计和软件实现功能,给出了选用的主要的芯片型号.本设计已应用于某合成孔径雷达信号模拟器系统中,使用证明该系统工作稳定,实现了设计中要求的功能.     

探地雷达目标回波信号双曲线提取算法研究

探地雷达对于电力接地网检测来说,是一种新型的检测方式,根据探地雷达在检测接地网过程中返回的图像提取目标信息实现对电力接地网的检测,对于检测地下导体的完整性方面有着较强的适用性和非凡意义。传统双曲线提取算法通常采用LOG算子完成边缘检测,借助雷达有效口径及扫描点间距完成对雷达双曲线顶点的寻找。在前人工作的基础上提出了采用Canny算子进行边缘检测,再进行双曲线提取的雷达直达波滤除以及目标识别算法,并与相关算法进行对比实验,得到比较完整的雷达目标反射图像,验证了算法的可行性。与传统雷达目标双曲线提取方式相比,该算法避免了雷达有效口径及扫描点间距的引入,提升了目标双曲线的完整性,精确性。     

基于PCI总线的雷达杂波模拟器

该文介绍了一种基于PCI总线和FPGA技术的雷达杂波模拟器.该模拟器主要用于在实验室环境下对雷达进行工作效能测试.在该模拟器的设计中主要采用了通用的雷达杂波数学模型,通过该模型计算出雷达在特定环境下的杂波数据.同时该模拟器具备实际记录杂波的播放功能.杂波数据通过PCI总线,传输给杂波模拟器.通过时域和频域的数字信号处理的方法,用FPGA器件构成卷积器或FFT运算器,还原出与雷达信号形式相关的杂波信号.该输出的杂波信号连同模拟的目标回波信号将被注入到雷达的接收机前端,用于检测雷达在各种杂波及无源干扰条件下对目标的处理能力.在该文中还介绍了该模拟器的典型应用情况.     

航海模拟器与分布交互仿真技术

该文介绍采用DIS技术开发航海模拟器系统的设计思想及主要技术，包括船舶操纵数学模型、以电子海图为背景的船舶动态显示、雷达/ARPA图象、用OpenGL开发三维视景、仪表以及网络技术。     

机载火控雷达训练模拟器的设计

航空电子综合仿真系统中的机载火控雷达模拟器与视景、火控、敌我识别和电子对抗等仿真系统构成一个具有多种训练功能的模拟系统。讨论了雷达模拟器的软、硬件结构,说明了机动目标多精度模型的建立方法,分析了地、海与体杂波的雷达反射特性模型。采用以检测概率逼近的方法来模拟机动目标的杂波环境下的检测,并进行目标航迹的处理。分别进行目标、真实回波和辅助图形的绘制合成完成的雷达图像。上述方案应用于某型飞行训练模拟器的研制,取得预期效果,具有良好的工程应用前景。     

雷达模拟器中可视化仿真技术的实现

实验室要调试和验证雷达系统设计与雷达数据处理算法,雷达模拟器是必不可少的实物。雷达模拟器可仿真雷达的各种功能。为了实现这些功能进行可视化。针对模拟器可视化的要求,将MapObjects组件技术应用到雷达模拟器中,在VC环境下结合Windows编程技术具体实现了战场地理环境信息的建立,控制地理信息对象的设置与显示。建立想定目标运动仿真和雷达波位扫描等功能,能直观、准确、动态地表现雷达战场环境。仿真实验表明,雷达模拟器实现了目标运动和雷达数据处理算法的工作效果,为设计新型雷达提供可靠参考。     

基于LabVIEW的ARPA算法设计与实现

ARPA是一种结合雷达与计算技术应用的船舶避碰设备,基于LabVIEW软件设计开发了ARPA跟踪算法仿真系统,系统可模拟实现船用ARPA常用的跟踪滤波算法及自适应算法,对比分析了两种算法的应用效果与应用条件,对于航海教学中关于ARPA跟踪算法内容的学习具有较强的应用价值.     

基于红外相机和毫米波雷达融合的夜间行人检测

为了提升夜间环境行人检测的能力,使用红外相机和毫米波雷达进行信息融合.对两传感器数据进行时间配准并分别进行处理,利用改进YOLO算法处理红外图像得到目标类别特征,处理毫米波雷达数据获得目标的距离和速度特征,再建立空间对准模型对两传感器进行目标匹配,最后利用基于特征的融合算法完成夜间行人多模态信息输出.两传感器检测目标首次匹配之后,可以将类别信息反馈给雷达数据处理单元进行记录,当红外图像检测算法漏检时,利用雷达记录的信息补充输出类别特征.通过实验证明该融合算法提升了单一传感器行人检测成功率,在夜间场景具有良好的应用效果.     

一种基于结构特征边缘的多传感器图像配准方法

图像配准是多传感器图像融合等处理的前提. 本文以包含人造目标的合成孔径雷达(Synthetic aperture radar, SAR)图像和可见光图像为处理对象, 提出了一种基于结构特征边缘的多传感器图像配准方法. 该方法提取人造目标在两类图像中表现的共性特征---结构特征边缘, 并基于边缘匹配构造虚拟角点, 采用基于特征一致的粗配准方法和基于虚拟角点的精配准方法, 对待配准图像实现由粗到精的自动配准. 实验结果表明, 本文方法能够取得较高的配准精度.     

Automatic feature point extraction and tracking in image sequences for arbitrary camera motion

An automatic egomotion compensation based point correspondence algorithm is presented. A basic problem in autonomous navigation and motion estimation is automatically detecting and tracking features in consecutive frames, a challenging problem when camera motion is significant. In general, feature displacements between consecutive frames can be approximately decomposed into two components: (i) displacements due to camera motion which can be approximately compensated by image rotation, scaling, and translation; (ii) displacements due to object motion and/or perspective projection. In this paper, we introduce a two-step approach: First, the motion of the camera is compensated using a computational vision based image registration algorithm. Then consecutive frames are transformed to the same coordinate system and the feature correspondence problem is solved as though tracking moving objects for a stationary camera. Methods of subpixel accuracy feature matching, tracking and error analysis are introduced. The approach results in a robust and efficient algorithm. Results on several real image sequences are presented.The support of the Advanced Research Projects Agency (ARPA Order No. 8459) and the U.S. Army Engineer Topographic Laboratories under Contract DACA 76-92-C-0009 is gratefully acknowledged.     

基于圆形约束C-V水平集的肺部CT图像病灶分割

针对肺部CT图像中圆形病灶区域的分割问题,对Chan-Vese水平集图像分割方法进行了分析和改进,提出了基于圆形约束的C-V水平集模型,进而提出了基于圆形约束水平集的肺部图像病灶分割算法,解决了图像中大小不同的多圆检测问题。对合成图像和实际临床肺部CT图像进行了分割实验,结果表明,该方法可以较好地分割出图像中的多个圆形区域,算法具有较好的抗噪性,实现速度较快,有利于实现肺部CT图像肺结节自动检测。     

基于注意力的毫米波-激光雷达融合目标检测

针对自动驾驶中使用激光雷达进行目标检测时漏检被遮挡目标、远距离目标和复杂天气场景下目标的问题,提出一种基于注意力机制的毫米波-激光雷达特征融合的目标检测方法.首先,将毫米波和激光雷达各自的扫描帧数据分别聚合到它们的标注帧上,并将毫米波和激光雷达的数据点进行空间对齐;其次,对两者进行聚合和空间对齐后的数据分别进行Poin...     

Classified road detection from satellite images based on perceptual organization

Extracting roads from satellite images is an important task in both research and practice. This work presents an improved model for road detection based on the principles of perceptual organization and classification fusion in human vision system (HVS). The model consists of four levels: pixels, primitives, structures and objects, and two additional sub‐processes: automatic classification of road scenes and global integration of multiform roads. Based on the model, a novel algorithm for detecting roads from satellite images is also proposed, in which two types of road primitives, namely blob‐like primitive and line‐like primitive are defined, measured, extracted and linked using different methods for dissimilar road scenes. A hierarchical search strategy driven by saliency measurement is adopted in both linking processes. The blob primitives are linked using heuristic grouping and the line primitives are connected through genetic algorithm (GA) evolution. Finally, all of the linked road segments are normalized with centre‐main lines and integrated into global smooth road curves through tensor voting. Experimental results show that the algorithm is capable of detecting multiform roads from real satellite images with high adaptability and reliability.     

Stochastic models for recognition of occluded targets

Recognition of occluded objects in synthetic aperture radar (SAR) images is a significant problem for automatic target recognition. Stochastic models provide some attractive features for pattern matching and recognition under partial occlusion and noise. In this paper, we present a hidden Markov modeling based approach for recognizing objects in SAR images. We identify the peculiar characteristics of SAR sensors and using these characteristics we develop feature based multiple models for a given SAR image of an object. The models exploiting the relative geometry of feature locations or the amplitude of SAR radar return are based on sequentialization of scattering centers extracted from SAR images. In order to improve performance we integrate these models synergistically using their probabilistic estimates for recognition of a particular target at a specific azimuth. Experimental results are presented using both synthetic and real SAR images.     

Development of an automated flexible fixture for planar objects

A three fingered automated flexible fixturing system has been developed to fixture planar objects in a machining process. The fixturing system consists of two computer numerically controlled (CNC) modules, an auxiliary mechanism and a fixturing algorithm. This fixture is designed on the idea of the minimum number of fingers needed to immobilise an object. The fixturing algorithm is developed on the concept of the maximal inscribed circle in a polygon. It offers the flexibility to reconfigure the fingers of the system to accommodate workpieces of different shapes and sizes. A prototype of the system has been successfully built and tested, showing satisfactory performance in mechanical design and automatic control over the configurations of the fingers. Examples are given of the application of the system with objects of different shapes. The system is shown to be flexible, reconfigurable and automatic, capable of fixturing planar objects of different shapes and sizes in the machining process.     

双基地雷达探测范围的三维可视化研究

利用插值方法计算双基地雷达散射截面积,由此建立压制干扰条件下的双基地雷达探测范围方程。将导数取零作为曲线取极值的必要条件,推导出双基地雷达警戒区域边界解析表达式。在估算警戒区域面积的基础上,提出双基地雷达盲区的构造算法,并给出网格点上双基地雷达探测高度最大值与最小值的计算方法。利用OpenGL绘制双基地雷达三维探测范围。实验结果表明,该方法能在微机上实时地显示压制干扰条件下双基地雷达三维探测范围的动态变化。     

Multi-channel higher-order local autocorrelation for object detection on satellite images

ABSTRACT

An automatic object-detection method is necessary to facilitate the efficient analysis of satellite images consisting of multispectral images. Considering that the relationship between spectrums is important for discriminating objects in multispectral images. This paper proposes a feature extraction method that can capture both spatial and spectral relationships of the multispectral images. Moreover, image preprocessing and dimensionality reduction procedures are introduced for stable feature extraction. In this study, we conducted experiments for detecting two types of objects by using Landsat 8 images. The proposed method improved the detection performance relative to other image features.