无人机地面站图像压缩传输系统设计与应用

针对无人机地面指挥控制系统要实现互连、互通、互操作的网络化要求,设计了视频信号实时网络传输和显示系统。系统由基于DSP的服务器端和通用客户端播放器组成,服务器端通过设置并行任务完成视频信号的实时采集、编码和发送,客户端完成视频信号的实时接收、解码和播放,从而使系统实现了图像信息在地面站所有显示终端上的实时显示。实验结果表明,系统完全满足无人机地面站系统功能及指标要求,并且通过网络化技术节省了地面站宝贵空间资源,系统设计方法对同类型系统具有一定借鉴意义。     

基于几何矩阵的增强虚拟现实

增强虚拟现实是组合真实场景和虚拟场景,以达到丰富真实场景的目的.针对摄像机相对景深做较小运动时所获得的视频,提出了一种基于几何矩阵的增强虚拟现实方法.摄像机在这种运动情况下,可以用一个几何矩阵来描述两帧之间的关系,这样就大大简化真实场景和虚拟场景的组合.同时,由于虚拟场景来自二维图像,因此,非常容易对准虚拟物体在视频中的位置.理论和真实视频实验表明,本文提出的增强虚拟现实方法具有计算量小和真实性强等优点.     

Use of measurements from an imaging sensor for precision targettracking

The extraction of measurements for precision tracking of the centroid of a target from a forward-looking infrared imaging sensor is presented. The size of the image of the target is assumed to be small, i.e. around 10 pixels. The statistical characterization of the centroid of the target is obtained. Similarly, the statistical properties of the image correlation of two frames, which measures the target offset, are derived. Explicit expressions that map the video noise statistics into measurement noise statistics are obtained. The offset measurement noise is shown to be autocorrelated. State variable models for tracking the target centroid with these measurements are then presented. Simulation results and quantitative conclusions about achievable subpixel tracking accuracy are given. It is shown that the filter that models the autocorrelated measurement noise provides the best performance     

基于运动形态学的掠地飞行器视频检测方法

 结合数学形态学、视频图像运动分析以及数学统计分析方法，提出了一种用于空中预警控制系统(AWACS)的小型掠地飞行器运动形态学视频检测方法。该方法首先应用形态学tophat滤波提取出视频图像中的可疑目标(SO)，并求取SO的运动参数，利用真实目标和类目标干扰的不同运动特性，应用数学统计的方法对各运动参数进行分析以剔除部分类目标干扰，从而获得潜在目标(PT)的灰度分布和运动参数。在应用形态学tophat滤波器进行可疑目标提取中，一种新的金字塔型结构元的引入有效减少了虚警数量。外场试验表明，该检测方法能够有效抑制航拍掠地飞行器视频图像中的地物干扰，降低虚警目标的数量。     

Surveillance system based on data fusion from image and acousticarray sensors

The present publication shows a model of a monitoring system of high benefit, based on two complementary sensorial systems (image+acoustics), which allows the information received to fuse, and to prioritize the computational resources toward the zones with considerable risk. The application uses components such as: active acoustic radar based on a phase array of microphones, with techniques of beamforming, and multibeam detection schemes; efficient compression algorithms and video transmission in real time. The system is composed of an arbitrary number of sensors (monitoring probes), distributed by the monitoring space, a data network and a central server (monitoring manager). The sensors consist of a microprocessor, with its memory and three main modules: (1) acoustic radar; (2) video capture card; and (3) network card. The flexibility of the system allows new modules to be added such as: personal detector card; sound card; cards for telecontrol; etc. The sensor microprocessor preprocesses the information received by the modules, packs and transmits it to the central server through a data network. The system uses IP protocol, and SNMPv2 for management     

SDRAM在机载显示系统中的研究应用

飞机座舱显示系统是航电电子系统的重要组成部分，在机载显示器实时视频图像处理中，通常都要对视频图像进行缓存，因此需要大容量的存储器。传统的SRAM由于成本高、速率慢，已经无法满足视频处理对存储器的要求，SDRAM速率快、容量大、体积小，是比较理想的器件。提出一种基于SDRAM的设计，在DVI视频处理电路中可以实现对1600×1200的视频做图像缓存处理，可以在航电显示系统中用于视频缩放、视频压缩、视频增强等。     

景象匹配中的几何透视校正和归一化互相关模板匹配

几何失真是使景象匹配性能下降的一个很重要的因素,基于参考图与失真实时图中四对对应点,本文采用透视变换方法,对失真实时图进行校正。对校正后的实时图,本文采用归一化互相关方法,进行模板匹配。仿真实验表明,这两种方法简单易行,校正与匹配结果精确。     

复杂动态系统实时仿真模型自动生成

提出了一种表示复杂动态系统数学模型的分层结构,探讨了模型变量与参数在实时仿真程序中的引用－赋值关系,提出了一个既包含了模型的数学特征,又包含了模型实时运行所需的信息的实时仿真模型分层结构。在此基础上设计了一个实时仿真模型库及实时仿真模型自动生成系统。     

SVSS: Intelligent video surveillance system for aircraft

Safety and security are the most discussed topics in the aviation field. The latest security initiatives in the field of aviation propose [1I] the aircraft carriers to implement video surveillance within the aircraft at strategic locations. The current proposals allow the video surveillance data to be stored within the aircraft and monitored by one of the flight crew. The monitoring crew will be responsible for identifying the anomaly within the aircraft and take necessary preventive actions. With the introduction of additional technology within the aircraft, mere human perception may not be sufficient to make a decision. In this research work, the authors explore the possibility of implementing a smart video surveillance system (SVSS) within the aircraft that is tuned toward detecting the behavioral anomaly within the aircraft. The SVSS will generate security triggers when it detects an anomaly within the aircraft. These triggers could be combined with other triggers generated by different aircraft components (possible alarms from the flight crew, data traffic anomaly, or alarm generated by one of the avionics components) to provide a better understanding of the situation to the monitoring crew.     

激光告警多目标定位与跟踪技术研究

研究了激光告警多目标定位和跟踪的建模方法。利用聚类概念,对激光告警器测量数据进行集类,并对各个类对应的目标状态进行空间-时间融合。通过求解航迹信息融合的状态估计值,实时形成同步脉冲控制逻辑,从而实现对激光多目标的实时跟踪和激光信号的实时提取。给出了激光告警多目标定位与跟踪的设计过程,提出了一种实用简便的融合算法,并设计了一套该模型的原理样机。试验结果验证了这种方法的有效性。     

Detection of obstacles in the flight path of an aircraft

The National Aeronautics and Space Administration (NASA), along with members of the aircraft industry, recently developed technologies for a new supersonic aircraft. One of the technological areas considered for this aircraft is the use of video cameras and image-processing equipment to aid the pilot in detecting other aircraft in the sky. The detection techniques should provide high detection probability for obstacles that can vary from subpixel to a few pixels in size, while maintaining a low false alarm probability in the presence of noise and severe background clutter. Furthermore, the detection algorithms must be able to report such obstacles in a timely fashion, imposing severe constraints on their execution time. Approaches are described here to detect airborne obstacles on collision course and crossing trajectories in video images captured from an airborne aircraft. In both cases the approaches consist of an image-processing stage to identify possible obstacles followed by a tracking stage to distinguish between true obstacles and image clutter, based on their behavior. For collision course object detection, the image-processing stage uses morphological filter to remove large-sized clutter. To remove the remaining small-sized clutter, differences in the behavior of image translation and expansion of the corresponding features is used in the tracking stage. For crossing object detection, the image-processing stage uses low-stop filter and image differencing to separate stationary background clutter. The remaining clutter is removed in the tracking stage by assuming that the genuine object has a large signal strength, as well as a significant and consistent motion over a number of frames. The crossing object detection algorithm was implemented on a pipelined architecture from DataCube and runs in real time. Both algorithms have been successfully tested on flight tests conducted by NASA.     

Algorithms for Fast Image Registration

The automatic determination of local similarity between two images (image registration) is one of the most fundamental problems of image processing and pattern recognition. A class of registration algorithms that are reasonably efficient and robust for translational displacement has been considered to determine relative shift between reference and search images. Stochastic image models defined on a rectangular region of support are used to determine feature vectors associated with reference and search images. A new measure, namely, coefficient of variation, is defined to take into account effects of contrast and sharpness of the images. Based upon this measure, a computationally efficient two-stage algorithm is obtained by combining the image-model based algorithm with a template matching technique. Simulation results with several synthetic and real images are presented to evaluate the performance of the algorithms.     

Confidence Limits for the Pointing Error of Gimbaled Sensors

The confidence limits for the pointing error of a gimbaled sensor relative to an off-gimbal reference are derived in this paper. The various misalignment angles and errors that lead to discrepancies between the indicated and the actual pointing direction are discussed in detail. A mathematical model is developed from which the statistics of the pointing error are derived. The confidence limits are obtained for a variety of cases.     

含湍流可变向微下击暴流的建模

本文建立一个适用于飞行力学研究和飞行员训练的微暴流风切变模型。该模型可模拟以任意方向倾泻的微暴流，其暴核离地高度、暴核直径、暴流强度、暴流倾泻方向、距机场位置等皆为可调参数，使用灵活方便，文中还给出了一种模拟非平衡微暴流湍流的方法，使微暴流模型更趋完善。     

5mm直径翘边孔横向耦合射流轨迹实验

采用粒子图像测速仪（PIV）对5mm直径翘边孔横向耦合射流流场进行了研究,实验结果表明:射流入射角与翘边孔角度有较大的偏差,但是随着翘边孔角度的增加,射流入射角增大;引入翘边孔射流有效直径,由试验数据得到了有效直径的数值。对射流轨迹曲线进行了拟合,考虑了两种主通道气流速度、四种不同角度的翘边孔和三种不同深度的主通道,方程式能较好地反映小尺寸通道翘边孔射流的流动结构,为今后的流场设计提供依据。      

Data fusion and tracking using HMMs in a distributed sensor network

This work deals with the problem of multiple target tracking, from the measurements made on a field of passive sonars activated by an active sonar (multistatic network). The difficulties encountered then are of two kinds: each sensor alone does not provide full observability of a target, and multiple, possibly maneuvering targets moving in a cluttered environment must be dealt with. The algorithm presented here is based on a discrete Markovian modelization of the targets evolution in time. It starts with a fusion of the detections obtained at each measurement time. Tracking and target motion analysis (TMA) are next achieved thanks to dynamic programming (DP). This approach leads to multiple and maneuvering target tracking, with few assumptions; for instance, the use of deterministic target state models are avoided. Simulation results are presented and discussed.     

电子散斑干涉法在振动测量中的物光位相调制法

介绍的物光正弦调制法是给振动物体的基座以低频振动,相当于物光位相受到正弦调制,仪器在不“冻结”参考状态(即CCD采样的信号不在帧存器中锁定)时,连续进行相邻两时刻散斑图的“相减”,并在监视器上得到与振型有关的实时图象。当物体达到自振频率时,将出现清楚的振型。因此,本方法可进行频率扫描,通过观察监视器的条纹场确定各阶自振频率及相应的振型,其条纹分辨率高于时间平均法,与相减法比较则可在很宽频率范围内进行频率扫描。      

Preventing Damage by Hidden Objects in Vegetation

The paper presents the experimental radar-based sensor to operate in environment with vegetation cover for detection and discrimination there the small-sized invisible dielectric and metallic objects. This sensor constitutes a vehicle-housed emergency system for surveillance of area with vegetation ahead on the path of moving vehicle to prevent its contact with hidden objects. The emergency system functioning in basically implemented by real-time electromagnetic imaging of the scene of interest and its following image processing to enhance the target responses. The developed and tested experimental radar techniques are under consideration. The results of experimental examinations in field are presented and discussed.     

基于惯性导航/无线传感器网络组合的采煤机定位方法研究

采煤机是井下综采工作面的重要设备，采煤机精确定位技术是煤矿生产装备自动化的关键技术之一。为了实现高精度定位满足井下综采自动化作业需求，提出了基于惯性导航/无线传感器网络组合的采煤机定位方法。采用锚节点安置于液压支架上，移动节点与惯导固定安装于采煤机上的配置方案，利用位置已知的锚节点测距信息估计和修正惯导误差，同时实施对安置于推进过程中的液压支架上锚节点（未知节点）位置信息的实时校准，从而达到采煤机高精度定位、无线传感器网络节点动态自动调节的目的。通过试验对所提方法的有效性进行了验证，结果表明，所釆用方法对釆煤机轨迹具有良好跟踪性能，水平定位误差不超过1.57 cm/h。     

Star-configuration searching for satellite attitude computation

A star pattern recognition algorithm is described which recovers attitude information by processing data obtained from a modular star sensor (MOSS) mounted on a spacecraft information is obtained by matching the stars detected by the MOSS with those inside a stellar catalogue. The system performance has been evaluated both from the point of view of computation time and error probability. The effect of errors in the MOSS measurements and multiple matches cases are also discussed