Validation of performance of real-time kinematic PPP. A possible tool for deformation monitoring

Structural failures (bridge or building collapses) and geohazards (landslides, ground subsidence or earthquakes) are worldwide problems that often lead to significant economic and loss of life. Monitoring the deformation of both natural phenomena and man-made structures is a major key to assessing structural dynamic responses. Actually, this monitoring process is under real-time demand for developing warning and alert systems.One of the most used techniques for real-time deformation monitoring is the Global Navigation Satellite System (GNSS) real-time procedure, where the relative positioning approach, using a well-known reference station, has been applied.This study was conducted to evaluate the actual quality of the real-time kinematic Precise Point Positioning (PPP) GNSS solution for deformation monitoring, where it can be concluded that a promise tool is under development and should be taken into account on actual and near future real-time deformation monitoring studies and applications.     

抗差自适应Kalman滤波及其在GNSS导航中的应用

针对Kalman滤波易受粗差影响而导致结果失真的问题,提出一种抗差自适应Kalman滤波方法,该方法结合自适应滤波与抗差Kalman滤波的优点,同时设计自适应因子和抗差因子,采用改进的两段Huber函数与2~3倍的观测噪声中误差来充当抗差因子与粗差判别标准。并对Kalman滤波和抗差自适应滤波(Adaptive Robust Kalman Filtering,ARKF)结果进行比较。车载实验结果表明,ARKF可以有效抵制观测异常对状态估值的影响,同时在系统先验信息不能精确给出的情况下,显著改善了滤波估值的稳定性和可用性。     

A scalable delay based analytical framework for CSMA/CA wireless mesh networks

We present an analytical framework for the performance analysis of CSMA/CA based wireless mesh networks. This framework can provide an accurate throughput-delay evaluation for both saturated and unsaturated cases. An efficient algorithm that determines the collision domain for each node based on both the interference range and routing in the network is presented. As another important application of this framework, we develop an analytic model that enables us to obtain closed form expressions for delay in terms of multipath routing variables. A flow-deviation algorithm is used to derive the optimal flow over a given set of routes for any number of classes. The model takes into account the effects of neighbor interference and hidden terminals, and tools are provided to make it feasible for the performance analysis and optimization of large-scale networks. Numerical results are presented for different network topologies and compared with simulation studies.     

Identification of relationship between sunspots and natural runoff in the Yellow River based on discrete wavelet analysis

Annual natural runoff is an important index of a river, which may be affected by solar activities. In this study, 304 years of annual natural runoff at the Sanmenxia station located in the Yellow River and the sunspot relative number are decomposed with the application of a Complex Morlet. According to the results of real part, modulus and second power of modulus, the annual runoff series at the Sanmenxia station has an obvious periodic oscillation on 90–100, 50–80, 35–50, 15–35, about 10, and less than 10-year scales. Also, there are obvious periodic variability with 60–90 years, 30–50 years and about 10 years. There are two centers of energy: one is about 1840–1850 on 7–11-year scale and the other is about 1825–1925 on 60–70-year scale. From the wavelet variance, 3, 26, 46, 68 year periods are detected within a 100-year scale, and the 68-year period is the most significant. Similar analyses are conducted for the sunspot relative number within the same period 1700–2003. The sunspot series shows 11- and 60-year period variation, as well as eight energy centers. Then, the correlation analyses for 11- and 60-year serial scales are computed. From a long-term period (1700–2003) view, there is no notable correlation between the natural runoff and the sunspot relative number; however, it is evident that the correlations exist within a short-term period. The results also indicate that the relationships between solar activities and the natural runoff in the Yellow River are complicated.     

基于环境星红树林特征指数的LAI估算

基于环境星影像红树林反射率特征建立红树林特征指数,通过实测红树林叶面积指数与特征指数、常用植被指数NDVI和SR之间线性相关性的建立,研究提出的 特征指数在LAI估算中的能力。提出的红树林特征指数BSTDEV与LAI的R2最高,达0.7616,且误差也最小。同时BSTDEV较常用植被指数更全面地反映了不同浓密程度的 红树林LAI的分布情况,未出现NDVI或SR在LAI估算中对稀疏或浓密红树林的低估。研究结果表明,基于红树林在环境星中建立的特征指数不仅能够有效地指示红树林 ,辅助实现区域红树林的提取,同时在红树林LAI估算中也有着一定优越性。     

基于IoT的库区地质灾害监测系统及应用

分析了库区地质灾害的特性和现有监测手段存在的问题,提出基于IoT的库区地质灾害监测系统架构。设计了三系统合一的表面位移监测系统,并结合WSNs系统和北斗报文通讯终端将数据采集、传输、处理、远程监控融为一体。通过武钢程潮尾矿库项目和哈尔乌素滑坡监测项目验证了该系统架构和相关技术的可行性。     

遥感图像中机场目标的识别方法

机场识别作为模式识别领域的问题之一,在军事上有着重要的应用前景。它包括判断机场是否存在和机场定位两个方面。结合已有的方法,提出了一种新的机场识别的解决方案。该方案先使用阈值分割方法进行图像分割,然后利用像素标记法提取出最大连通区域,作为疑是机场区域(ROI);用Canny算子进行图像的边缘提取,提出了一种改进的Hough变换,能够从边缘图像中准确地提取出直线段,并最终实现机场跑道的定位。     

联合两个界面的数据求解超定边值问题的途径

给定地面和卫星界面上的比较密集的重力数据,可采用虚拟压缩恢复法或球谐展开法求解地球外部重力场;给定地面部分区域和卫星界面上部分区域的重力数据,可采用球谐展开法求解.阐述上述两种方法的理论基础,给出基于上述两种方法求解重力场的模型.