Data assimilation into a Princeton Ocean Model of the Mediterranean Sea using advanced Kalman filters

This study investigates the effectiveness of the Singular Evolutive Extended Kalman filter (SEEK) and its variants (SEIK and SFEK filters) for data assimilation into a Princeton Ocean Model (POM) of the Mediterranean Sea. The SEEK filters are sub-optimal Kalman filters based on the approximation of the filter's error covariance matrices by singular low-rank matrices, reducing in this way extensive computational burden. At the initialization, the filters error covariance matrix is parameterized by a set of multivariate empirical orthogonal functions (EOFs) which describe the dominant modes of the system's variability. The Mediterranean model is implemented on a 1/4° × 1/4° horizontal grid with 25 sigma levels and is forced with 6-hour ECMWF re-analysis atmospheric data. Several twin experiments, in which pseudo-observations of altimetric data and/or data profiles were assimilated, were first performed to evaluate the filters performances and to study their sensitivities to different parameters and setups. The results of these experiments were very encouraging and helped in setting up an effective configuration for the assimilation of real data in near-real time situation. In the hindcast experiments, Topex/Poseidon and ERS weekly sea level anomaly data were first assimilated during 1993 and the filters solution was evaluated against independent Reynolds sea surface temperature (SST) analysis. The assimilation system was able to significantly enhance the consistency between the model and the assimilated data, although the improvement with respect to independent SST data was significantly less pronounced. The model SST was only improved after including SST data in the assimilation system.     

Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo

This study considers advanced statistical approaches for sequential data assimilation. These are explored in the context of nowcasting and forecasting using nonlinear differential equation based marine ecosystem models assimilating sparse and noisy non-Gaussian multivariate observations. The statistical framework uses a state space model with the goal of estimating the time evolving probability distribution of the ecosystem state. Assimilation of observations relies on stochastic dynamic prediction and Bayesian principles. In this study, a new sequential data assimilation approach is introduced based on Markov Chain Monte Carlo (MCMC). The ecosystem state is represented by an ensemble, or sample, from which distributional properties, or summary statistical measures, can be derived. The Metropolis-Hastings based MCMC approach is compared and contrasted with two other sequential data assimilation approaches: sequential importance resampling, and the (approximate) ensemble Kalman filter (including computational comparisons). A simple illustrative application is provided based on a 0-D nonlinear plankton ecosystem model with multivariate non-Gaussian observations of the ecosystem state from a coastal ocean observatory. The MCMC approach is shown to be straightforward to implement and to effectively characterize the non-Gaussian ecosystem state in both nowcast and forecast experiments. Results are reported which illustrate how non-Gaussian information originates, and how it can be used to characterize ecosystem properties.     

基于图像处理的单轨交通PC轨道梁边缘检测

本文结合跨座式单轨交通系统PC轨道梁自身的特性,提出一种基于图像处理技术的PC轨道梁边缘检测方法.首先对轨道梁实景图进行同态滤波,增强图像对比度,突出边缘信息;其次利用canny算子进行边缘粗提取;最后,利用边缘跟踪自适应去除背景干扰信息,提取单一PC轨道梁边缘,同时结合Hough变换进行断线连接优化轨道边缘.仿真结果表明该算法能够准确提取PC轨道梁边缘信息,为后续的单轨线路诊断工作奠定了基础.     

基于LCL滤波器的双馈风电网侧变换器不同控制结构性能研究

为分析基于LCL滤波器的双馈风电网侧变换器在不同电流反馈控制结构情况下的工作性能,采取PI控制器对网侧变换器网侧电流反馈控制结构和变换器侧电流反馈控制结构的电流闭环根轨迹进行分析,对其在理想电网无阻尼电阻和有阻尼电阻、非理想电网无阻尼电阻3种情况下的特性进行了比较。分析及仿真结果表明变换器侧电流反馈控制结构控制算法相对较复杂,但是系统稳定性好,电网电流的谐波畸变率较低;而电网侧电流反馈控制结构较易实现网侧单位功率因数控制,但稳定性较差。     

基于有源滤波器的单台接线变压器仿真研究

电气化铁道牵引供电系统三相严重不平衡,存在大量的谐波和无功,各供电区段需要用分相绝缘器分断.制约了高速铁路和重载铁路的发展.基于有源滤波器的同相供电系统的关键技术是检测补偿电流的实时性,检测的精度高低与动态响应性能好坏直接影响补偿效果.针对单台接线变压器,分析适合它的谐波和无功实时检测技术,平衡变换装置和有源滤波器的控制方法,基于MATLAB/Simulation建立了仿真模型,对单台接线变压器构成的同相AT牵引系统进行了仿真.     

基于单周期控制的统一电能质量调节器的设计

基于统一电能质量调节器（UPQC）对电能质量进行补偿时的同步性问题和控制的复杂性两个方面的考虑,在对UPQC拓扑结构与工作原理分析的基础上,提出了基于单周期控制的统一电能质量调节器控制策略,完成对UPQC中的DVR和APF两部分的同步控制,拓扑结构简单、易于实现。在理论分析的基础上,应用Simulink对单周期控制的统一电能质量系统进行了建模和仿真,仿真结果表明,该方法能在一个开关周期内消除稳态误差,证明了该拓扑的实用性和控制方法的有效性。     

基于LCL滤波器的电压型PWM整流器新型电流控制策略研究

在对基于LCL滤波器的三相电压型PWM整流器研究基础上提出了一种新型的电流控制策略。该策略采取了电网侧电流反馈控制,能够方便对功率因数进行控制。为了保证系统的稳定性,在电流环内引入了电网电流二次微分环节,根据二相静止坐标系下实轴和虚轴正弦函数的特性,在算法中避免了微分环节可能造成的高频噪声问题。对所提出的控制策略进行了分析和仿真,在此基础上搭建了以TMS320LF2407为控制核心的基于LCL滤波器的三相电压型PWM整流器系统。仿真和实验结果验证了该算法的可行性。     

基于预测控制的水下自航器抗海浪变深控制分析

水下自主航行器在近水面航行中存在着深度跟踪和姿态控制较为困难的问题。为此，首先建立了自主航行器的近水面三自由度运动数学模型，然后设计了无迹卡尔曼滤波器实现对系统状态的估计；接着，利用斯特林内插法在变动的工作点处对自主航行器模型进行近似线性化，并根据线性化后的模型设计预测控制器，实现自主航行器的变深运动控制。经过仿真实验，验证了滤波器对自主航行器近水面运动状态估计的准确性以及预测控制器在抗海浪扰动上的控制效果。仿真结果表明，带有无迹卡尔曼滤波器的预测控制器可以快速、准确的实现自主航行器的深度跟踪控制与姿态控制，且具有响应速度快，对外部扰动鲁棒性强的特点。     

多波束宽带恒定束宽波束形成器的DSP实现

波束形成是声纳、雷达信号处理中的重要环节。由于高性能的宽带波束形成算法运算量大交难以实时实现,因此开发研制高速度、高精度、高性能的波束形成器、就很必要。文章提出采用ＴＭＳ３２０Ｆ２０６及ＩＭＳＡ１００为主要芯片,构成多波束宽恒定束宽波束形成器,可实现实时、高速、高性能的多波束宽带恒定束宽波束形成,并给出了具体设计方案。水池实验结果,表明本文的系统设计方案正确、可靠。     

基于果蝇算法优化的移动机器人RBPF-SLAM研究

针对基于传统Rao-Blackwellized粒子滤波(Rao-Blackwellised Particle Filter,RBPF)算法的移动机器人在进行同时定位与地图构建(Simultaneous Location and Mapping,SLAM)时易发生粒子退化导致移动机器人位姿估计不准确以及地图一致性较差的问题,提出一种基于果蝇优化算法的RBPF-SLAM算法。该算法将果蝇种群觅食过程中果蝇具有的趋味特性引入RBPF算法,将粒子视为果蝇个体,粒子的适应度值作为空气中食物味道的浓度,利用果蝇优化算法的高寻优能力使粒子向高似然区域移动并不断迭代寻优,以优化粒子种群的整体分布。同时,在果蝇寻优后的新种群中引入自适应交叉变异操作,以增加种群多样性。根据粒子的适应度值确定交叉概率,对配对好的粒子进行自适应交叉操作,再根据变异概率对当前种群的最优粒子进行变异操作,选取适应度值更高的粒子作为当前最优解。采用指数函数步长公式更新粒子状态,增加寻优过程中的搜索距离,有效提高算法的收敛效率。基于ACES building和MIT Killian Court数据集的仿真实验以及移动机器人实机测试结...