循环中值平均滤除遥测地形粗差

对于地形遥测数据中引入的脉冲噪声,传统的滤波算法往往需要选择滤波门限,自适应能力不强,或者不能在滤除噪声的同时有效地保护信号数据,或者运算复杂。针对以上问题,文中提出了一种快速的脉冲噪声自适应滤除算法,该算法基于数理统计思想,对局部数据(滤波窗口)进行均值和方差估计,并根据估计结果自动选择检噪门限,进而实现噪声检测和平滑。实验结果显示,在脉冲噪声密度小于5%时,该算法的滤波信噪比增益远高于常用滤波算法和其他同类算法;对平稳变化信号的处理效果较好。     

遥测地形数据中粗差的自动滤除

对于地形遥测数据中引入的脉冲噪声（粗差）,传统的滤波算法往往需要选择滤波门限、自适应能力不强,或者不能在滤除噪声的同时有效地保护信号数据。针对以上问题,提出了一种新颖的脉冲噪声自适应滤除算法,该算法基于数理统计和模糊数学思想,对局部数据（滤波窗口）进行均值和方差估计,并根据估计结果自动选择检噪门限,进而实现噪声检测和平滑。实验结果显示,在脉冲噪声密度小于5%时,该算法的滤波信噪比增益远高于常用滤波算法和其它同类算法;算法对平稳信号的处理效果较好。     

基于平方根UKF双向滤波的单站无源定位算法

单站无源定位可观测性弱、参数测量精度不高,因此初始值测量误差往往较大.而无迹卡尔曼滤波(Unscented Kalman filtering,UKF)算法对初始值较为敏感,并且由于数值计算的舍入误差会产生滤波发散,为了解决以上问题,提出了一种基于平方根UKF(Square-root UKF,SRUKF)的双向滤波算法.该算法通过使用误差协方差的平方根替代协方差阵参与滤波,保证了算法的稳定性,同时运用平方根无迹卡尔曼滤波后向平滑(Un-scented Rauch-Tung-Striebel smoother,URTSS)后向平滑方法,用平滑值取代初始值,为前向滤波提供较高精度的起始值,提高算法的滤波精度,从而提高了算法对初始值的鲁棒性.仿真结果表明,与UKF算法和SRUKF算法相比,该算法提高了滤波的稳定性、收敛速度、定位精度及对初始值的鲁棒性.     

基于自适应波束形成的现场EMC测量方法

在复杂的电磁环境中对电子设备进行现场测量时最大的问题是背景噪声的滤除。提出基于阵列信号处理的电磁兼容现场测量方法。利用空间平滑算法进行辐射信号的精确DOA(波达方向)估计,再依据最小方差无失真响应MVDR(Minimum Variance Distortionless Response)准则进行波束形成,从而对接收到的信号实现空域滤波。仿真结果证明,该算法能有效滤除背景噪声中的同频相干信号,提高现场电磁辐射测量的精度。     

一种柔性提升装置中力测量滤波算法研究

钢丝绳等引起的纵向振动信号,对装配线上的智能提升装置测量和控制产生了极大影响,从而降低了装配工人的工作效率.采用常规的FIR、IIR等滤波算法进行处理,在信号的突变点处滤波效果不理想.为了有效地解决上述问题,提出了一种基于突变点检测的混合滤波方法.该方法通过顶点检测算法和筛选条件设置,能有效地检测出振动信号的突变点;同时经过混合滤波算法,有效地滤除了振荡信号对测量系统的影响.实验结果表明,该方法能有效地滤除振动信号,提高了测量系统的测量精度.     

基于M估计的鲁棒后向平滑CKF单站跟踪算法

提出一种基于M估计的鲁棒后向平滑容积卡尔曼滤波（M-estimated based Robust Backward-Smoothing Cubature Kalman Filter,MR-BSCKF）算法。该算法将改进的M估计思想引入后向平滑容积卡尔曼滤波（BSCKF）算法中,引入Mahalanobis距离构建P-Huber等价权函数,通过降低野值误判概率进一步提高滤波算法的鲁棒性;在传统CKF算法的基础上增加后向平滑函数,通过后向平滑和前向滤波相结合的二次滤波进一步提高滤波的精度,实现了算法精度和抗野值能力的统一。仿真结果表明,与传统算法相比,MR-BSCKF在有野值和无野值的情况下都能够得到更加准确的目标跟踪结果,且鲁棒性更强。     

一种针对彩色图像随机值脉冲噪声的矢量滤波方法

为了去除彩色图像随机值脉冲噪声,提出了一种新的矢量滤波方法。该方法对图像的平滑区域和边缘区域的滤波工作分开进行,平滑区域滤波方法将窗口分成多个区域,然后基于矢量中值和平滑区域像素的特征检测出平滑区域的信号,边缘区域的滤波是在已知信号的基础上对非信号进行矢量中值滤波。仿真实验结果表明,该方法能够有效地去除彩色图像的随机值脉冲噪声,尤其当噪声密度较高时,去噪效果明显优于传统的矢量中值滤波。     

热红外与可见光视频融合的运动目标检测

在室外环境中,可见光相机可以获取场景中丰富的纹理细节和光谱信息,但受光照变化的影响很大;而热红外相机对光照变化不敏感,但热红外成像对比度低、颜色信息缺失。为了充分利用两者的互补信息,实现运动目标的精确检测,同时提高检测的鲁棒性,提出了一种应用RGBT混合高斯模型的目标检测方法。该方法将热红外图像作为第4个分量加入到传统的混合高斯模型中,提高了算法的正检率;还引入了阴影去除算法,增强了算法的鲁棒性。实验表明,该方法比传统的混合高斯模型检测精度更高,目标更完整,同时也能较好地满足实时性的要求。     

基于曲波变换和Retinex人脸光照处理算法

针对光照对人脸识别影响问题,提出了一种基于曲波变换和Retinex人脸光照处理的算法。该算法对光照变化人脸对数变化后的图片进行曲波变换(Curvelet);利用Kimmel变分模型作为平滑滤波算子对低频图像进行平滑滤波,对高频系数进行阈值去噪。通过曲波逆变换得到光照亮度成分图像;利用Retinex模型提取光照不变成分。通过Yale B与CMU PIE人脸库的实验结果表明:该算法能有效地消除光照变化对人脸识别的影响并提高人脸识别率。     

多模型平滑算法及其在机动目标跟踪中的应用

在机动目标跟踪中的多模型平滑问题的研究中,关于单模型的线性或者非线性系统的平滑问题,已经得到深入的研究并且提出了很多高效的算法。针对多模型的滤波问题也在目标跟踪领域得到了深入的研究。然而,将基于单模型的平滑算法扩展到多模型系统中并非易事。为此提出一种基于正向滤波结合反向平滑的多模型平滑算法。正向滤波采用交互式多模型滤波,而反向平滑算法则采用类似于二阶广义伪贝叶斯的多模型算法。上述算法采用递归结构,使算法简单并且计算效率高。通过机动目标跟踪的仿真与多模型平滑算法比较,可以取得更高的估计精度。     

基于视频图像的飞行姿态检测方法研究

利用流场实验平台对仿生实验的风洞环境进行模拟,以航模飞机为检测对象,给出了一种基于双摄像机的视频图像飞行姿态检测方法。利用颜色特征分别提取两路视频图像中的飞行航模目标,采用提取直线的方法获得航模目标中的直线参数,通过直线参数的变化分别解算飞行的横滚角和俯仰角姿态。与AHRS航姿参考系统MIN—IVA900的数据进行对比表明：该方法检测结果的最大偏差小于1°,取得了飞行姿态检测结果的一致性。     

基于事件的视觉传感器及其应用综述

基于事件的相机是一种生物启发的新型视觉传感器,可实时高效地捕捉场景的变化.与基于帧的传统相机不同,事件相机仅报告触发的像素级亮度变化(称为事件),并以微秒级分辨率输出异步事件流.该类视觉传感器已经逐渐成为图像处理、计算机视觉、机器人感知与状态估计、神经形态学等领域的研究热点.首先,本文阐述了事件相机的基本原理、发展历程、优势与挑战;然后,介绍了3种典型事件相机(包括DVS(dynamic vision sensor)、ATIS(asynchronous time based image sensor)和DAVIS(dynamic and active pixel vision sensor))以及多种新型事件相机;接下来,重点回顾了事件相机在特征提取、深度估计、光流估计、强度图像估计与三维重建、目标识别与跟踪、自主定位与位姿估计、视觉里程计与SLAM、多传感器融合等方面的应用研究;最后,归纳了事件相机的研究进展,并探讨了未来的发展趋势.     

Conflict resolution for air traffic management: a study inmultiagent hybrid systems

Air traffic management (ATM) of the future allows for the possibility of free flight, in which aircraft choose their own optimal routes, altitudes, and velocities. The safe resolution of trajectory conflicts between aircraft is necessary to the success of such a distributed control system. In this paper, we present a method to synthesize provably safe conflict resolution manoeuvres. The method models the aircraft and the manoeuvre as a hybrid control system and calculates the maximal set of safe initial conditions for each aircraft so that separation is assured in the presence of uncertainties in the actions of the other aircraft. Examples of manoeuvres using both speed and heading changes are worked out in detail     

Increasing the accuracy of Time-of-Flight cameras for machine vision applications

Range imaging based on the Time-of-Flight (ToF) principle evolved largely in recent years. Especially, the lateral resolution, the ability to operate outdoors with sunlight and the sensitivity have been improved. Nevertheless, the acceptance of depth cameras for machine vision in the industry environment is still rather limited. The major shortcoming of ToF depth cameras compared to laser range scanners is their measuring accuracy, which is not sufficient for several applications. In this paper, we firstly introduce several state of the art depth cameras briefly and demonstrate their capabilities. Afterwards, we explore possibilities to increase the radial resolution and the accuracy of ToF depth cameras based on the Photonic Mixer Device (PMD). In general, the usage of higher modulation frequencies promises higher depth resolution but yields on the other hand higher noise levels. Moreover, the accuracy is limited by systematic errors and the measurement are affected by random noise and we show how to minimize and compensate them in industry environments.     

DIY基于摄像头的激光测距仪

基于微型计算机摄像头和激光发射器的一种智能距离测试系统设计。利用计算机的高速运算,采用图像识别技术,只用一个摄像和激光笔就可以测出距离。极大地节约了成本和重量,在机器人及飞行器中得到了很好的作用。     

飞行试验机翼变形测量的一种方法

飞机飞行过程中的机翼变形会引起武器在俯仰、滚转和偏航3个方向的姿态变化,三维姿态变化的大小将直接影响武器对目标的命中性能,需要对不同飞行状态下武器的三雏运动姿态进行测量,修正真实飞行状态下机翼变形引起的对准误差.在吊舱中安装两台高速摄像机同时拍摄机载武器,对采集到的序列图像进行处理,就能获得机载武器空中挂飞过程中的三维运动姿态参数.     

Spatial resolution estimation of CBERS-1 and CBERS-2 CCD cameras

Modulation Transfer Function (MTF) is a means of characterizing sensor spatial resolution and is used to measure image quality during in-flight commissioning phase. Another important parameter is the Effective Instantaneous Field of View (EIFOV), which is also used to measure system performance. This work describes three approaches to estimate the effective spatial resolution of Charge-Coupled Device (CCD) cameras aboard China–Brazil Earth Resources Satellite (CBERS)-1 and CBERS-2. These approaches use artificial and natural targets to estimate the EIFOV in across-track and along-track directions. CBERS-1 and CBERS-2 have the same specifications and carry three sensors: the Wide Field Imager (WFI), High-Resolution CCD Camera and Infrared Multispectral Scanner (IRMSS). The results obtained from these methods are compared with those of preflight measurements. The results have shown that the effective spatial resolutions in the across-track direction, for both CBERS-1 and CBERS-2 cameras, are worse than those in the along-track direction. Furthermore, it has been observed that their spatial resolutions are not consistent with the camera specifications in both directions. Although the methodologies presented here have been used to estimate the spatial resolution of CCD cameras aboard CBERS satellite, they can also be used to estimate the spatial resolution of similar sensors.     

Visual Odometry for a Hopping Rover on an Asteroid Surface using Multiple Monocular Cameras

Visual odometry refers to the use of images to estimate the motion of a mobile robot. Real-time systems have already been demonstrated for terrestrial robotic vehicles, while a near real-time system has been successfully used on the Mars Exploration Rovers for planetary exploration. In this paper, we adapt this method to estimate the motion of a hopping rover on an asteroid surface. Due to the limited stereo depth resolution and the continuous rotational motion on a hopping rover, we propose to use a system of multiple monocular cameras. We describe how the scale of the scene observed by different cameras without overlapping views can be transferred between the cameras, allowing us to reconstruct a single continuous trajectory from multiple image sequences. We describe the implementation of our algorithm and its performance under simulation using rendered images.     

Analysis of Error in Depth Perception with Vergence and Spatially Varying Sensing

In stereo vision the depth of a 3-D point is estimated based on the position of its projections on the left and right images. The image plane of cameras that produces the images consists of discrete pixels. This discretization of images generates uncertainty in estimation of the depth at each 3-D point. In this paper, we investigate the effect of vergence and spatially varying resolution on the depth estimation error. First, vergence is studied when pairs of stereo images with uniform resolution are used. Then the problem is studied for a stereo system similar to that of humans, in which cameras have high resolution in the center and nonlinearly decreasing resolution toward the periphery. In this paper we are only concerned with error in depth perception, assuming that stereo matching is already done.     

Hybrid light field imaging for improved spatial resolution and depth range

Light field imaging involves capturing both angular and spatial distribution of light; it enables new capabilities, such as post-capture digital refocusing, camera aperture adjustment, perspective shift, and depth estimation. Micro-lens array (MLA)-based light field cameras provide a cost-effective approach to light field imaging. There are two main limitations of MLA-based light field cameras: low spatial resolution and narrow baseline. While low spatial resolution limits the general purpose use and applicability of light field cameras, narrow baseline limits the depth estimation range and accuracy. In this paper, we present a hybrid stereo imaging system that includes a light field camera and a regular camera. The hybrid system addresses both spatial resolution and narrow baseline issues of the MLA-based light field cameras while preserving light field imaging capabilities.