Data assimilation using the particle filter for identifying the elasto‐plastic material properties of geomaterials

A computational method, incorporating the finite element model (FEM) into data assimilation using the particle filter, is presented for identifying elasto‐plastic material properties based on sequential measurements under the known changing traction boundary conditions to overcome some difficulties in identifying the parameters for elasto‐plastic problems from which the existing inverse analysis strategies have suffered. A soil–water coupled problem, which uses the elasto‐plastic constitutive model, is dealt with as the geotechnical application. Measured data on the settlement and the pore pressure are obtained from a synthetic FEM computation as the forward problem under the known parameters to be identified for both the element tests and the ground behavior during the embankment construction sequence. Parameter identification for elasto‐plastic problems, such as soil behavior, should be made by considering the measurements of deformation and/or pore pressure step by step from the initial stage of construction and throughout the deformation history under the changing traction boundary conditions because of the embankment or the excavation because the ground behavior is highly dependent on the loading history. Thus, it appears that sequential data assimilation techniques, such as the particle filter, are the preferable tools that can provide estimates of the state variables, that is, deformation, pore pressure, and unknown parameters, for the constitutive model in geotechnical practice. The present paper discusses the priority of the particle filter in its application to initial/boundary value problems for elasto‐plastic materials and demonstrates a couple of numerical examples. Copyright © 2012 John Wiley & Sons, Ltd.     

Coupling between weather radar rainfall data and a distributed hydrological model for real-time flood forecasting / Couplage de données pluviométriques issues de radars météorologiques et d’un modèle hydrologique distribué pour la prévision de crue en temps réel

Abstract

Abstract After the destructive flood in 1998, the Chinese government planned to build national weather radar networks and to use radar data for real-time flood forecasting. Hence, coupling of weather radar rainfall data and a hydrological (Xinanjiang) model became an important issue. The present study reports on experience in such coupling at the Shiguanhe watershed. After having corrected the radar reflectivity and the attenuation data, the weather radar rainfall was estimated and then corrected in real time using a Kalman filter. In general, the precipitation estimated from weather radar is reasonably accurate in most of the catchment investigated, after corrections as above. Compared to the results simulated by raingauge data, the simulations based on the weather radar data are of similar accuracy. Present research results show that rainfall estimated from the weather radar, the radar data correction method, the method of coupling, and the Xinanjiang model lend themselves well to application in operational real-time flood forecasting.     

顾及有色噪声的光纤陀螺随机噪声自适应滤波方法

作为光纤陀螺误差的重要组成部分,随机噪声严重影响着光纤陀螺的精度,对光纤陀螺随机噪声进行准确建模和补偿是提升陀螺精度的有效方式。本文针对光纤陀螺随机噪声的复杂性,难以对其进行精确分析,ARIMA （auto-regressive moving average）模型Kalman滤波中有色噪声不能使用状态扩充法建模的问题,扩展了Harvey方程,实现有色噪声白化。同时,考虑先验噪声的不确定性以及模型参数在线更新导致的参数与状态噪声相互耦合,分析了动态Allan方差估计量测噪声的不足,使用VBAKF （variational Bayesian adaptive Kalman filter）实时修正滤波状态噪声与量测噪声。试验表明,Harvey法较传统滤波建模方式,随机噪声序列方差降低40%,Harvey法结合VBAKF使序列方差降低了54%;VBAKF较动态Allan方差,可以更好地估计量测噪声。结果表明,此方法可有效抑制随机噪声Kalman滤波中有色噪声和随机模型不准确的影响,提高随机误差补偿精度。     

移动机器人SLAM位姿估计的改进四元数无迹卡尔曼滤波EI北大核心CSCD

在全自主运动控制的移动机器人系统中,自身位姿的估计和校正对于移动机器人的运动至关重要。卡尔曼滤波是解决移动机器人同步定位与地图构建(SLAM)常用方法。相较于卡尔曼滤波,无迹卡尔曼滤波(UKF)无须对复杂的非线性函数进行雅可比矩阵运算。本文基于无迹卡尔曼滤波,根据先验协方差的平方根选择sigma点,计算协方差以及加权均值。用四元数表示姿态,将四元数矢量转换为旋转空间进行矩阵运算,在此基础上设计了一种位姿估计算法——基于四元数平方根的无迹卡尔曼滤波(QSR-UKF)算法。试验将EKF、QSR-UKF、SR-UKFEKF 3种算法的位姿估计结果进行仿真分析,并通过相关定量指标进行了描述,验证了本文算法的有效性。     

磁法数据处理中的扩边和优化中值滤波方法的研究

依据地质体在地面上引起磁异常的特点,提出了三方向扩边方法,这种方法能够减小边界效应,使处理结果更可靠。在实际的测量中,磁法数据受外界的干扰比较大,干扰因素给数据处理和资料解释工作带来了影响。首次将中值滤波技术引入到磁法数据处理中,提出了一种新的滤波方法,即优化中值滤波方法,来解决这个问题,并给出了优化中值滤波的模型以及计算公式。最后利用优化中值滤波方法对实际磁测数据进行处理,取得了较好的效果。     

基于经验模式分解的滤波去噪法及其在GPS多路径效应中的应用

经验模式分解(Empirical Mode Decomposition,EMD)是一种新的信号处理技术,它是基于数据本身的,且能在空间域中将信号进行分解,从而可以区分噪声和有用信号。根据EMD分解白噪声而得到的本征模式函数(IMF)分量的能量密度与其平均周期的乘积为一常量这一特性,建立一种新的基于EMD滤波去噪方法,并将该方法应用于GPS多路径效应的研究中。通过对模拟数据与GPS实测数据的处理分析,得出以下主要结论:①EMD滤波去噪法与小波方法都能最大限度地削弱测量的随机误差,但EMD滤波去噪法比小波方法更直接,且不受测不准原理及小波函数选择的影响;②相比小波方法,EMD能够更有效地剔除瞬时强噪声,从而能够提取更精确的多路径效应重复性误差改正模型。     

IMU/DGPS辅助车载CCD及激光扫描仪三维数据采集与建模

三维信息快速采集是真实场景建模与三维虚拟现实技术的关键。本文提出了一种基于激光扫描仪、线/面阵CCD相机及GPS与IMU等多种传感器融合的车载移动式数据快速采集系统。各传感器安置在车内稳定平台上并随车保持一致的运动姿态。通过对GPS和IMU数据进行卡尔曼(Kalman)滤波,可推测出整个系统及各传感器的位置和最佳姿态估计;从扫描仪点云数据可提取出街道场景中事物的三维几何信息;线阵CCD相机用于获取路面带状地物等线性特征;面阵CCD采集街道两侧面状纹理信息,从而快速获得城市目标的地理坐标和三维建模信息,由此可重建城市路面街道的三维真实场景。     

地热回灌防腐过滤器及配套技术试验

利用专题立项开凿的地热生产井和地热回灌井,对第三纪孔隙型热储层进行了加压条件下的对井同层回灌试验。通过研发地热回灌防腐过滤器及配套技术,解决了先前在回灌过程中由各种物理、化学和生物因素引起的堵塞,导致回灌量减小、回灌井早期丧失回灌能力这一技术难题。     

中国地面气温自适应递减平均降尺度方法的改进研究

针对基于卡尔曼滤波类型的自适应递减平均降尺度方案对中国地面气温精细化预报中,原方案在强降温天气预报结果不理想的问题,将其中只随空间变化的递减平均权重系数w(i)改进为含有空间、天气过程信息的自适应函数w(i,p)(i为站点信息,p为天气过程信息);在此基础上将上一次的强降温日信息当做预报前一天信息,应用改进w(i,p)递减平均降尺度方案称为w(i,p)相似法;而直接从强降温历史信息中统计插值预报与观测值的系统性偏差均值,并用以修正预报结果的方法称为w(i,p)统计法。结果表明:改进为自适应函数w(i,p)后的降尺度方案对中国地面气温的预报效果有不同程度的改善,对于24 h强降温预报,采用w(i,p)降尺度方案的预报误差均方差较原方案减小了0.19℃;而进一步改进的w(i,p)相似法与w(i,p)统计法的预报误差均方差分别减小了1.12℃、1.45℃,改进效果显著。