BDS/GPS短期ISB建模和预报的Kalman滤波方法

ISB是多系统PPP数据处理中必须要考虑的一项误差,因此有必要对BDS/GPS短期ISB建模和预报进行研究。为了提高ISB预报精度,针对等权LS(least square)估计ISB模型参数时忽略了拟合数据权重不同的问题,提出了采用Kalman滤波对模型参数进行估计,并根据ISB拟合数据距预报时刻的远近调整Kalman滤波拟合数据的方差。本文采用7d的ISB数据进行建模,根据所建模型预报第8天的ISB值,并对预报精度和定位结果进行了验证。进行试验的4个测站Kalman拟合模型的ISB预报精度比LS拟合模型分别提高了29.7%、11.5%、43.5%和32.0%。采用Kalman拟合模型的ISB预报值作为先验约束,PPP平均定位精度在E和U方向上比采用LS拟合模型预报值分别多提高了2.7%和0.9%,比不加ISB先验约束在E、N、U方向分别提高了10.6%、26.3%和3.4%。     

卡尔曼滤波在GPS数据预处理中的应用

为了提高GPS数据预处理过程中基线解算的精度,文章研究利用多项式拟合法得到Kalman滤波的系统状态方程和转移矩阵,提出利用Kalman滤波算法对三差观测值进行粗差及周跳的修复。实验结果证明Kalman滤波可以对含噪信号进行有效的降噪,经降噪后的信号具有更好的分布。     

The CMEKF Method for Sub-Sea Pipeline Monitoring and Leak Detection

1 .IntroductionIdentificationofaleakingflowlinerepresentsaspecialchallengetoboththeoilandgasindustryandregulators .While ,historically ,thenumberofcasesofshallowwaterreleasefromflowlinesisex tremelysmall,deepwaterflowlinesoperateunderconditionsrarelyencounteredinpreviousdevelopmentschemes .Theremotenessofthedeepwaterpipeline ,coupledwithanumberofcomplexinteractionsbe tweenthereleasedfluidsandthedeepwaterenvironment,effectivelyeliminatesmostexternaldetectionmethods (Scott,1 999) .Thecauseoflea…     

State-dependent Riccati equation-based robust dive plane control of AUV with control constraints

The paper treats the question of suboptimal dive plane control of autonomous underwater vehicles (AUVs) using the state-dependent Riccati equation (SDRE) technique. The SDRE method provides an effective mean of designing nonlinear control systems for minimum as well as nonminimum phase AUV models. It is assumed that the hydrodynamic parameters of the nonlinear vehicle model are imprecisely known, and in order to obtain a practical design, a hard constraint on control fin deflection is imposed. The problem of depth control is treated as a robust nonlinear output (depth) regulation problem with constant disturbance and reference exogenous signals. As such an internal model of first-order fed by the tracking error is constructed. A quadratic performance index is chosen for optimization and the algebraic Riccati equation is solved to obtain a suboptimal control law for the model with unconstrained input. For the design of model with fin angle constraints, a slack variable is introduced to transform the constrained control input problem into an unconstrained problem, and a suboptimal control law is designed for the augmented system using a modified performance index. Using the center manifold theorem, it is shown that in the closed-loop system, the system trajectories are regulated to a manifold (called output zeroing manifold) on which the depth tracking error is zero and the equilibrium state is asymptotically stable. Simulation results are presented which show that effective depth control is accomplished in spite of the uncertainties in the system parameters and control fin deflection constraints.     

水下模糊图象维纳滤波和卡尔曼滤波恢复算法研究

该文将海水中图象传输特性的研究与用此传输特性进行水下模糊图象恢复结合起来 ,得到一套测量水体传递函数及进行水下图象处理的方法。采用维纳 (Wiener)滤波和卡尔曼 (Kalman)滤波算法 ,对模糊图象进了恢复。结果表明 ,利用计算机采用一定的算法对散射性质已知的水体造成的图象模糊可以进行有效的复原 ,且良好效果。     

Data assimilation methods for estimating a heterogeneous conductivity field by assimilating transient solute transport data via ensemble Kalman filter

An ensemble Kalman filter (EnKF) is developed to identify a hydraulic conductivity distribution in a heterogeneous medium by assimilating solute concentration measurements of solute transport in the field with a steady‐state flow. A synthetic case with the mixed Neumann/Dirichlet boundary conditions is designed to investigate the capacity of the data assimilation methods to identify a conductivity distribution. The developed method is demonstrated in 2‐D transient solute transport with two different initial instant solute injection areas. The influences of the observation error and model error on the updated results are considered in this study. The study results indicate that the EnKF method will significantly improve the estimation of the hydraulic conductivity field by assimilating solute concentration measurements. The larger area of the initial distribution and the more observed data obtained, the better the calculation results. When the standard deviation of the observation error varies from 1% to 30% of the solute concentration measurements, the simulated results by the data assimilation method do not change much, which indicates that assimilation results are not very sensitive to the standard deviation of the observation error in this study. When the inflation factor is more than 1.0 to enlarge the model error by increasing the forecast error covariance matrix, the updated results of the hydraulic conductivity by the data assimilation method are not good at all. Copyright © 2012 John Wiley & Sons, Ltd.     

气候模式FIO-ESM对2015/16年厄尔尼诺的预测

Recently atmospheric and oceanic observations indicate the tropical Pacific is at the El Ni?o condition. However,it's not clear whether this El Ni?o event of this year is comparable to the very strong one of 1997/98 which brought huge influence on the whole world. In this study, based on the Ensemble Adjusted Kalman Filter(EAKF)assimilation scheme and First Institute of Oceanography-Earth System Model(FIO-ESM), the assimilation system is setup, which can provide reasonable initial conditions for prediction. And the hindcast results suggest the skill of El Ni?o-Southern Oscillation(ENSO) prediction is comparable to other dynamical coupled models. Then the prediction for 2015/16 El Ni?o by using FIO-ESM is started from 1 November 2015. The ensemble results indicate that the 2015/16 El Ni?o will continue to be strong. By the end of 2015, the strongest strength is very like more than 2.0°C and the ensemble mean strength is 2.34°C, which indicates 2015/16 El Ni?o event will be very strong but slightly less than that of 1997/98 El Ni?o event(2.40°C) calculated relative a climatology based on the years1992–2014. The prediction results also suggest 2015/16 El Ni?o event will be a transition to ENSO-neutral level in the early spring(FMA) 2016, and then may transfer to La Ni?a in summer 2016.     

集合均方根滤波同化地面自动站资料的技术研究

模式地形与观测站地形高度差异一直是地面资料同化面临的棘手问题,合理的同化方案能够将地面自动站资料有效的同化到中尺度数值模式中。本文首先采用Guo et al.(2002)的方案实现了在WRF模式中应用集合Kalman滤波方法同化地面自动站资料;然后对方案进行调整,对10 m高度风场、2 m高度位温、2 m高度露点和地表气压进行同化。通过均方根误差分析,模拟结果和同化增量分析来确定集合平方根滤波(EnSRF)同化地面自动站资料的有效性,并进行敏感性试验分析检验模式对各要素物理量的响应状况。结果表明:在EnSRF同化系统中应用Guo et al.(2002)的方案将地面自动站资料进行同化到数值模式中,能够部分改善模拟结果;地面观测资料(温度、湿度、风场、地表气压)中各物理量分别同化到数值模式都能影响18小时降水预报,但各物理量所起作用大小不同,其中对结果影响最大的是露点;使用位温、露点分别代替温度、比湿进行同化模拟效果更好,对自动站资料的同化也更加有效。     

基于GPS的建筑物变形监测数据处理与系统设计

着重介绍了GPS数据的单历元解算方法,以及监测点变形量的分析和预测,探讨了这种方法在变形监测系统数据处理中的应用情况及数据相关性对变形精度的影响。阐述了对变形量进行分析和预测的各种方法,提出用整体方差法对变形量进行分析,并用卡尔曼滤波方法对变形值进行预测,试算结果表明这些方法是可行的。最后,设计了一套GPS自动变形监测系统,包括软件功能设计、数据库实现等,并对系统安全性和可靠性进行了分析。