Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: a Normalized Difference Snow and Ice Index

The VEGETATION (VGT) sensor in SPOT 4 has four spectral bands that are equivalent to Landsat Thematic Mapper (TM) bands (blue, red, near-infrared and mid-infrared spectral bands) and provides daily images of the global land surface at a 1-km spatial resolution. We propose a new index for identifying and mapping of snow ice cover, namely the Normalized Difference Snow/Ice Index (NDSII), which uses reflectance values of red and mid-infrared spectral bands of Landsat TM and VGT. For Landsat TM data, NDSII is calculated as NDSIITM =(TM3 -TM5)/(TM3 +TM5); for VGT data, NDSII is calculated as NDSIIVGT =(B2- MIR)/(B2 + MIR). As a case study we used a Landsat TM image that covers the eastern part of the Qilian mountain range in the Qinghai-Xizang (Tibetan) plateau of China. NDSIITM gave similar estimates of the area and spatial distribution of snow/ice cover to the Normalized Difference Snow Index (NDSI=(TM2-TM5)/(TM2+TM5)) which has been proposed by Hall et al. The results indicated that the VGT sensor might have the potential for operational monitoring and mapping of snow/ice cover from regional to global scales, when using NDSIIVGT.

An Obstacle Cluster Based Data Fusion Method for Mobile Robot

首先给出了一种通过融合多个超声波传感器和一台激光全局定位系统的数据建立机器人环境地图的方法 ,并在此基础上 ,首次提出了机器人在非结构环境下识别障碍物的一种新方法 ,即基于障碍物群的方法 .该方法的最大特点在于它可以更加简洁、有效地提取和描述机器人的环境特征 ,这对于较好地实现机器人的导航、避障 ,提高系统的自主性和实时性是至关重要的 .大量的实验结果表明了该方法的有效性 .

基于多传感器融合的组合导航方法研究

本文提出一种基于多传感器融合的组合导航方法,能够在小型旋翼无人机上实现低成本、高精度导航定位.该方法通过建立导航系统的机械编排模型,设计了一个17状态的扩展卡尔曼滤波器(EKF).对加速计的零偏和陀螺仪的漂移进行在线估计,实时的补偿传感器的测量误差.从而对旋翼无人机的速度、位置、角速度和姿态等参数进行精确的估计.通过对实际飞行数据仿真实验,并对比参考的导航系统,证明该方法在飞机的全包线飞行下均能够解算出可靠的导航信息。

基于黑板结构的多传感器数据溶合系统FS-1

本文借助于数据溶合方法中的引导法构成的系统在移动式机器人定位中取得了令人满意的结果.本系统的特点是:用分布式黑板作为计算机构,使系统具有并行处理能力;把时间(时序)推理引入系统.在数据溶合中考虑了时间的重要作用.本文提出的溶合方法和结构原则上可用于其它相关的问题领域.

深水机器人低成本导航系统的位置估计方法研究

依据6000米自治水下机器人及其长基线声学定位系统现有的导航设备,将测距声信标和机器人载体携带的低成本导航传感器:涡轮式计程仪,压力传感器以及TCM2电子罗盘测量的导航数据相融合,分别提出两种基于EKF的导航数据融合算法,对机器人的位置以及水流参数进行估计,解决复杂环境下的深水机器人位置估计问题.蒙特卡洛仿真实验和湖上试验数据后处理表明,设计的位置估计算法收敛快,精度高,计算时间小,能够满足深水机器人的导航需要.

Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing

The aim of this paper is to show that Dempster-Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. Dempster-Shafer formulation allows for consideration of unions of classes, and to represent both imprecision and uncertainty, through the definition of belief and plausibility functions. These two functions, derived from mass function, are generally chosen in a supervised way. In this paper, the authors describe an unsupervised method, based on the comparison of monosource classification results, to select the classes necessary for Dempster-Shafer evidence combination and to define their mass functions. Data fusion is then performed, discarding invalid clusters (e.g. corresponding to conflicting information) thank to an iterative process. Unsupervised multisource classification algorithm is applied to MAC-Europe'91 multisensor airborne campaign data collected over the Orgeval French site. Classification results using different combinations of sensors (TMS and AirSAR) or wavelengths (L- and C-bands) are compared. Performance of data fusion is evaluated in terms of identification of land cover types. The best results are obtained when all three data sets are used. Furthermore, some other combinations of data are tried, and their ability to discriminate between the different land cover types is quantified

三维环境中基于ESMF的多机器人协作观测方法

提出了一种基于扩展集员估计（ESMF）的多机器人协作观测方法，该方法将多机器人之间的观测数据融合过程嵌入到估计过程当中，从而减少了数据处理的过程，增强了算法的快速性。同时，这种方法在实现协作观测时只需要协作机器人传送观测信息而不是整个的估计信息，因此可以减轻多机器人系统的通信负担。除此之外，该方法在融合多机器人的观测数据过程中避免了多余的近似过程，增加了观测的准确性。最后，给出了三维环境下的仿真结果，验证了方法的可行性。

全地形移动机器人轮-地几何接触角估计

研究全地形移动机器人在不平坦地形中轮-地几何接触角的实时估计问题.本文以带有被动柔顺机构的六轮全地形移动机器人为对象,抛弃轮-地接触点位于车轮支撑臂延长线上这一假设,通过定义轮-地几何接触角δ来反映轮-地接触点在轮缘上位置的变化和地形不平坦给机器人运动带来的影响,将机器人看成是一个串-并联多刚体系统,基于速度闭链理论建立考虑地形不平坦和车轮滑移的机器人运动学模型,并针对轮-地几何接触角δ难以直接测量的问题,提出一种基于模型的卡尔曼滤波实时估计方法.利用卡尔曼滤波对机器人内部传感器的测量值进行噪声处理,基于机器人整体运动学模型对各个轮-地几何接触角进行实时估计,物理实验数据的处理结果验证了本文方法的有效性,从而为机器人运动学的精确计算和高质量的导航控制奠定了基础.

基于多传感器的可变形机器人自主控制方法研究

将GPS、电子罗盘、倾角仪、码盘传感器等应用到可变形机器人自主运动控制中.针对可变形机器人自身结构特点,提出了一种基于多传感器信息融合的可变形机器人在野外环境中自主控制的方法.该方法主要实现了在非结构环境中机器人的自主变形、自主避障和自主导航定位等功能.实验验证了该方法的有效性.

移动机器人多传感器信息融合技术述评

多传感器信息融合技术是目前移动机器人领域的研究热点。详细阐述了多传感器信息融合技术在移动机器人领域中的应用与研究进展,尤其对多传感器信息融合实现方法进行了深入的探讨。指明了移动机器人领域中多传感器信息融合技术未来的发展方向。

智能机器人系统中局部环境特征的提取

本文基于栅格地图和滚动视窗的控制方法 ,提出了一种提取机器人局部障碍物群环境特征的数据融合新方法 .该方法在多个级别对原始数据进行不同程度的抽象和压缩 ,减少机器人内部模块之间或机器人之间、机器人与控制中心进行通讯的数据量 ,提高系统的动态性能 .同时 ,该方法对复杂环境具有良好的自适应性和实时性 .本文分别列举了仿真实验和物理实验结果 ,表明了机器人采用障碍物群的环境特征提取方法可以成功地完成躲避障碍物和路径规划的任务

移动机器人的障碍物群检测方法

利用激光和超声波传感器在用栅格表示法形成地图的基础上 ,提出了进行数据融合以提取环境特征的新方法 :识别障碍物群。该方法能够在密集障碍物环境中为机器人的路径规划和避障提供准确的环境特征信息 ,提高机器人系统的自主性和实时性。实验结果表明了该方法的有效性。

Ground target detection, classification and sensor fusion in distributed fiber seismic sensor network - art. no. 683015

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.