Economic Filters Applied to Quantitative Mineral Resource Simulations

The U.S. Geological Survey has developed a technique that allows mineral resource experts to apply economic filters to estimates of undiscovered mineral resources. This technique builds on previous work that developed quantitative methods for mineral resource assessments. A Monte-Carlo calculation uses mineral deposit models to estimate commodity grades and tonnages of undiscovered deposits. The results then are analyzed using simple estimates of capital expenditures and daily operating costs for a mine and associated mill. The daily operating costs and the value of the ore are used to calculate the net present value of the deposit, which is compared to the capital expenditures to determine whether the deposit is economic. Repetition of these calculations for many deposits produces a table that can be interpreted in terms of the probability of there being deposits that have anet present value exceeding some specified amount. Sample calculations indicate that applying economic filters to simulated mineral resources might change the perception of the results compared to presenting the calculations in terms of the expected mean gross-in-place value of the minerals.     

低通滤波器在高光谱影像分类中的应用

研究了低通滤波器对类别可分性的改善原理,指出将其应用于高光谱影像分类有利于获取更为准确的类别分布信息,进而提高影像分类精度。最后通过实验对低通滤波器在高光谱影像分类中的表现进行了验证。     

地表形变监测的改进相干目标法

如何从雷达干涉时间序列影像中获取更全面的相干目标集合,进行变形时间序列分析是当前研究的难点和热点。本文提出了改进的相干目标法,可获取更为全面且可信度高的相干目标集合,进而提高地表形变监测的时空分辨率和精度。根据雷达影像中同类型地物散射分布相近的特点,采用非参数同分布检验算法提取后向散射特性相近的同质点开展空间非局部滤波,提高干涉图的质量。与此同时,利用多尺度的极大似然条纹频率估计算法分离差分干涉图中的系统性相位,并基于同质点进行自适应相干性估计,获取相干性的平稳估计量,从而获取更多的相干点目标。利用20景TerraSAR-X条带模式时间序列影像,分别利用传统的及改进后的相干目标法对香港填海区域地表形变信息进行时序分析。对试验数据的分析结果表明,本文提出的改进方法在具有稀疏植被的填海区可有效增加相干目标点的提取,得到更为可信的沉降结果。     

Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)

In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.     

EnSRF雷达资料同化在一次飑线过程中的应用研究

本文利用包含复杂冰相微物理过程的WRF（Weather Research and Forecasting）模式,针对2007年4月23日发生在我国华南地区的一次典型飑线天气过程,分别进行了确定性预报和集合预报试验,发现确定性预报能大致捕捉到飑线系统的发生发展过程,但对飑线后部的层云区模拟效果较差。集合预报能够有效地减少模式的不确定性,大部分集合成员对飑线的模拟效果优于确定性预报。进一步将集合预报得到的40个成员作为背景场,采用EnSRF（Ensemble Square Root Filter）同化多普勒天气雷达资料,并将分析得到的集合作为初始场进行集合预报,通过与未同化雷达资料的集合对比,考察了EnSRF同化多部雷达资料对飑线系统的影响。结果表明:EnSRF雷达资料同化增加了模式初始场的中小尺度信息,大部分集合成员的分析场能够较准确地再现飑线的热力场、动力场和微物理场的细致特征,并且模拟出飑线后部的层云结构。通过对EnSRF分析的集合进行模拟发现,大部分集合成员较未同化雷达资料时模拟效果有明显改善。同化后的集合预报ETS（Equitable Threat Score）评分最高,其次是未同化的集合预报,确定性预报的最低。     

基于NDVI时间序列数据的江西省水稻种植制度变化研究

利用Savitzky-Golay滤波对覆盖江西省范围的SPOT VGT NDVI时间序列数据进行平滑处理的基础上,结合坡度数据,通过非监督分类的方法提取了江西省2000、2005和2010年水稻种植范围,并根据NDVI的年内动态变化,从水稻种植范围、水稻生长季起始时间、水稻复种指数和NDVI最大振幅等分析了江西省水稻种植和生长情况,探讨2000~2010年江西省水稻生产的变化。     

基于方差递推法的Kalman滤波在钟差预报中的应用

卫星导航系统中星载原子钟的钟差预报对于导航、定位及授时具有重要的作用。为了提高卫星钟差预报的精度,设计了一种两步确定卫星钟噪声协方差矩阵的Kalman滤波钟差预报模型。该方法首先基于Hadamard总方差确定卫星钟噪声协方差矩阵的初值,然后,使用方差递推法得到滤波过程中卫星钟的噪声协方差矩阵。使用GPS系统的星载铷钟数据进行短期预报,并与常用的二次多项式模型、灰色模型进行对比,结果表明:本文中提出的方法可以实现高精度的卫星钟差预报且预报效果优于两种常用模型,同时,该方法能够在一定程度上弥补预报误差随预报时间增加而不断变大的不足。     

Underwater Navigation Based on Real-Time Simultaneous Sound Speed Profile Correction

Precise sound speed profile (SSP) information is critical when using sonar for underwater terrain navigation. Nevertheless, acquiring SSP information in real-time is difficult, especially in underwater navigation environment. To account for this problem, this paper presents an underwater navigation method that applies real-time SSP correction to the terrain measurements. The method uses a probe to measure the surface sound speed simultaneously. Meanwhile, redundancy of topographic measurement data is exploited to derive the equivalent SSP information. The acquired equivalent SSP is updated continuously with particle filter algorithm. The terrain measurements can be corrected in real-time with the equivalent SSP to improve the performance of underwater terrain navigation. By removing the impact of inaccurate SSP from the terrain measurements, the proposed method can achieve precise and robust underwater navigation result without using an underway-profiling instrument. Simulated results confirm the good performance of the proposed method.     

Robust adaptive unscented Kalman filter for bearings-only tracking in three dimensional case

This paper proposes an improved version of Unscented Kalman Filter (UKF), namely Robust Adaptive UKF (RAUKF), with a special focus on Bearings-Only Target Tracking for three-dimensional case (3DBOT). The automatic tuning of the noise covariance matrices and the robust estimation of the target states form a critical point for the performance of the Kalman-type filtering algorithms, especially in the variable environmental conditions exposed in underwater. The key idea of the proposed filter is to combine robust aspects of UKF and adaption of the process and measurement noise covariance matrices with low computational complexity. The main contribution of this paper is to adjust these matrices by means of the steepest descent algorithm, and the H_∞ technique is embedded to achieve superior performance in terms of accuracy and robustness against initial conditions and model uncertainties. Different experiments are performed to evaluate the performance of the proposed algorithm in the 3DBOT problem with a single moving observer. Simulations demonstrate that the proposed filter produce more accurate results with satisfactory computational burden in comparison with other methods.     

顾及有色噪声的自适应粒子滤波UWB定位算法

传统卡尔曼滤波算法要求噪声模型符合高斯分布,在UWB室内定位中,由于载体本身的机制等干扰,观测噪声不仅仅是白噪声,也存在有色噪声的情况,而粒子滤波可以处理有色噪声的问题。本文通过增加似然分布自适应调整来改进粒子滤波用于目标跟踪的精度,同时研究在白噪声、有色噪声下似然分布自适应调整粒子滤波和拓展卡尔曼滤波在UWB中的优势与不同。试验结果表明：观测噪声为白噪声时,拓展卡尔曼滤波和粒子滤波均可以较好地实现对行人的定位跟踪;观测噪声为有色噪声时,自适应粒子滤波定位效果优于粒子滤波、拓展卡尔曼滤波。