适用于快衰落环境的OFDM/TDM信道估计算法

针对正交频分复用/时分复用(OFDM/TDM)系统现有导频结构和信道估计算法缺乏对信道快衰落实时跟踪能力的问题,提出一种改进信道估计算法.该算法利用相邻2个时域复用导频序列对信道特性进行估计得到信道传递函数,通过内插处理获得数据符号的信道状态信息,并利用最小均方误差准则的频域均衡算法补偿信道衰落.仿真结果表明,与现有信道估计算法相比,在相同数据传输效率情况下,提出算法不仅能够适用于慢衰落信道,同时对快衰落信道的实时变化有良好的跟踪能力,改善系统性能.     

多径衰落信道下 OFDM系统定时同步优化

在多径环境下,正交频分复用(OFDM)系统对第1径的寻找是解决定时同步的关键,多数算法将最强径当作第1径,使得定时同步向后延迟,造成严重的ISI.本文在PN训练定时同步的基础上,通过寻找滑动求和值的第1个下降沿来确定第1径的位置.仿真结果证明了本算法的优越性,同时在第1径不为最强径的情况下算法也能得到较好效果.     

采用新型峰均比降低技术的OFDM功放线性化方案

针对OFDM信号的高峰均比问题,提出了一种将新型峰均比降低技术与数字预失真技术相结合的功率放大器线性化方案.其中,峰均比降低技术由传统的削峰技术、新型的削谷技术和闲置载波技术组成,信号的峰值和谷值同时被削去,峰均比得到最大程度的压缩.传统削峰技术用于降低信号的峰值;新型的削谷技术采用基于切线原理的矢量孔调整法,同时改变信号的幅度和相位,使得信号谷值变大;闲置载波技术用于减少削峰削谷处理所产生的带内和带外失真.实测结果表明,基于记忆多项式模型的数字预失真技术可以将功放输出信号的ACPR值优化10.5 dB,而在结合了新型峰均比降低技术之后,本方案可以将ACPR值进一步优化18.5 dB.该方案适用于未来4G通信系统.     

OFDM系统中基于最优阈值ACE的PAPR抑制

动态星座图扩展(Active constellation extension, ACE)是一种能够有效降低正交频分复用(Orthogonal frequency division multiplexing, OFDM)系统峰均功率比(Peak-to-average power ratio, PAPR)的方法,为解决现有ACE算法因选择固定限幅阈值而限制降低PAPR效果的问题,提出一种最优阈值ACE(Optimal threshold ACE, OTACE)算法,该算法能在每次迭代时根据信号功率求得最合适的限幅阈值,从而增强抑制PAPR的效果.通过数据拟合得到合适的迭代次数,在此基础上对OTACE算法抑制PAPR的效果进行了仿真分析,仿真结果表明,与凸集映射(Projection onto convex sets, POCS)和智能梯度投影(Smart gradient projection, SGP)算法相比,OTACE分别能提高5 dB和3 dB左右的PAPR增益.在广电1、广电6和巴西A三种信道下,分别在多普勒频移为20 Hz和60 Hz时测试OTACE算法对系统误码率(Bit e...     

探讨回归系数的stein型主成分估计问题

设计阵X呈病态的线性回归模型,给出一种新的关于回归系数的有偏估计,即stein型主成分估计,且对偏参数的存在性。最优性进行了一些探讨。     

LMS和RLS自适应滤波算法对比研究

介绍了自适应滤波器的基本原理,对最小均方(LMS,Least Mean Squares)和递归最小二乘(RLS,Recursive Least Squares)自适应算法进行仿真分析及对比研究.仿真结果及实例均表明,两种算法都能有效抑制和抵消各种干扰,但相比之下,RLS算法具有更好的收敛性能及稳定性.     

一种利用CORDIC算法降低WiMAX系统峰均值功率比方法的研究及实现

基于802．16标准的微波存取全球互通（WiMAX）技术能够提供较高数据速率传输和较高的频谱利用率，并且实现简单，是宽带无线接入系统中最富有竞争力的技术之一。针对WiMAX系统中峰均值功率比（PAPR）过高的问题，从硬件实现的角度出发，利用协调旋转数字计算（CORDIC）算法来求出信号的幅度，采用限幅加滤波的方法来达到降低队PR的目的。实验结果表明这种方法在降低PAPR．值方面具有结构简单，效果良好的特点。     

一种适用于OFD M系统的块状导频自适应方案

针对传统OFT)M系统导频插入密度固定所导致的信道利用率较低的弊端,研究了块状导频的插入方法、自适应策略与传输信令.导频间信道状态一致时,OFDM信道估计精度主要取决于导频密度,因此,通过控制导频密度,提出了一种改进的块状导频自适应方案,在满足系统误码率要求的前提下可有效提高OFDM系统的信道利用率.仿真结果表明该方案可以实现通信需求与导频的自动匹配,在保证系统性能的同时有效降低导频密度.     

基于脉冲成型的降低FBMC系统峰均比方法

针对滤波器组多载波(FBMC:Filter Bank MultiCarrier)系统峰均比(PAPR:Peak to Average Power Ration)较高的问题,基于FBMC系统中原型滤波器及其多项结构,借鉴正交频分复用(OFDM:Orthogonal Frequency Division Multiplexing)降低峰均比方法,提出一种基于脉冲成型(PS:Pulse Shaping)技术的降低FBMC系统峰均比的方法.该方法在综合滤波器S/P(Serial to Parallel)后添加频率加权,实现了时域的循环位移,从而达到降低峰均比的目的.仿真结果表明,在FBMC系统中引入PS技术后,峰均比有明显下降.     

Efficient Algorithm for Gradually Varied Flows in Channel Networks

This paper presents an efficient solution technique for one-dimensional unsteady flow routing through a general channel network system—dendritic, looped, divergent, or any combination of such networks. The finite difference method is used to solve the de St. Venant equations in all the branches of the network simultaneously. The number of equations to be solved at a time during any iteration is reduced to only four times the number of branches of the network. This results in a significant reduction in storage requirements and solution time. Importantly, the algorithm does not require any special node numbering schemes and the nodes can be numbered independently for each branch. The algorithm is also suitable for programming on a parallel-processing computer.     

Estimation of Operation Time for Soil Vapor Extraction Systems

Soil vapor extraction (SVE) has become an acceptable method of removing volatile organic compounds from soil. However, determining the length of time these systems should operate has been historically difficult. This paper presents a procedure for determining this length of operation. The procedure incorporates principles of uncertainty analysis, contaminant transport, and decision theory. An example is provided to illustrate the use of the procedure. Additional analysis of the results shows that a simple calculation can be made that will determine if a SVE system that has been operating for a period of time and is in the later stages of contaminant removal should continue to operate. This calculation consists of dividing the cost of treating the remaining contaminated soil with an alternative method (e.g., ex situ biological treatment) by the annual operation and maintenance cost and comparing this ratio to the inverse of the interest rate. If this ratio is less than the inverse of the interest rate the system should be shut off.     

汽车防抱制动系统自适应模糊控制算法

提出了一种基于自适应模糊算法的防抱制动系统控制方式,针对汽车纵向双轮模型,设计了模糊控制器和滑移率校正器,校正器通过车辆的输入输出参数辨识最佳滑移率,并调整系统控制参数,以提高系统的控制性能,仿真实验验证了控制算法的有效性。     

一种改进的MIMO系统串行干扰消除检测算法

使用串行干扰消除(SIC)方法进行MIMO系统信号检测时,先检测出的信号对后续层信号的检测有很大影响,存在误差传播现象.针对这一问题,提出一种改进的SIC检测算法.所提算法在进行SIC检测时,对前层采用穷举搜索,并对所得到的P维信号的累积度量值进行计算和排序,从中取出L组具有最小度量值的信号,再对其后续层利用SIC进行检测.由于对前P层进行穷举搜索,降低了检测的误码率,从而减少了误差传播,提高了系统的检测性能.通过调节参数P和L,可以在计算复杂度和检测性能间取得适当的折中.     

Real-Time Demand Estimation and Confidence Limit Analysis for Water Distribution Systems

A real-time estimation of water distribution system state variables such as nodal pressures and chlorine concentrations can lead to savings in time and money and provide better customer service. While a good knowledge of nodal demands is prerequisite for pressure and water quality prediction, little effort has been placed in real-time demand estimation. This study presents a real-time demand estimation method using field measurement provided by supervisory control and data acquisition systems. For real-time demand estimation, a recursive state estimator based on weighted least-squares scheme and Kalman filter are applied. Furthermore, based on estimated demands, real-time nodal pressures and chlorine concentrations are predicted. The uncertainties in demand estimates and predicted state variables are quantified in terms of confidence limits. The approximate methods such as first-order second-moment analysis and Latin hypercube sampling are used for uncertainty quantification and verified by Monte Carlo simulation. Application to a real network with synthetically generated data gives good demand estimations and reliable predictions of nodal pressure and chlorine concentration. Alternative measurement data sets are compared to assess the value of measurement types for demand estimation. With the defined measurement error magnitudes, pipe flow data are significantly more important than pressure head measurements in estimating demands with a high degree of confidence.     

General Realization Algorithm for Modal Identification of Linear Dynamic Systems

The general realization algorithm (GRA) is developed to identify modal parameters of linear multi-degree-of-freedom dynamic systems subjected to measured (known) arbitrary dynamic loading from known initial conditions. The GRA extends the well known eigensystem realization algorithm (ERA) based on Hankel matrix decomposition by allowing an arbitrary input signal in the realization algorithm. This generalization is obtained by performing a weighted Hankel matrix decomposition, where the weighting is determined by the loading. The state-space matrices are identified in a two-step procedure that includes a state reconstruction followed by a least-squares optimization to get the minimum prediction error for the response. The statistical properties (i.e., bias, variance, and robustness to added output noise introduced to model measurement noise and modeling errors) of the modal parameter estimators provided by the GRA are investigated through numerical simulation based on a benchmark problem with nonclassical damping.     

复合泊松分布参数估计的EM算法

复合泊松分布是非寿险精算中的重要理赔模型,利用正规的统计方法(如极大似然估计)估计模型的参数往往比较困难,而矩估计的精度在大样本下才能有令人满意的结果.本文应用EM算法研究了复合泊松分布的参数估计问题,给出了参数满足的方程,并给出了参数的矩估结果,对两种参数估计结果,通过计算机模拟,表明EM算法对参数的估计更为有效,且EM算法在小样本下就能得到较好的估计效果.     

State Estimation in Structural Systems with Model Uncertainties

This paper presents an observer designed under the assumption that differences between predicted and measured outputs arise from discrepancies between the real structural system and the nominal model used to represent it. The observer gain is independent of the assumed model error parametrization and proves to be the transpose of the state to output matrix of a state space formulation. The estimated state with the proposed observer is shown to be identical to that obtained by exciting the nominal model with the known input while adjusting the measured portion of the state to match the measurements at the start of every step. Numerical experiments suggest that the proposed observer can provide state estimates that are substantially more accurate than results predicted by projecting the measurements in a truncated modal space.     

Bayesian State Estimation Method for Nonlinear Systems and Its Application to Recorded Seismic Response

The focus of this paper is to demonstrate the application of a recently developed Bayesian state estimation method to the recorded seismic response of a building and to discuss the issue of model selection. The method, known as the particle filter, is based on stochastic simulation. Unlike the well-known extended Kalman filter, it is applicable to highly nonlinear systems with non-Gaussian uncertainties. The particle filter is applied to strong motion data recorded in the 1994 Northridge earthquake in a seven-story hotel whose structural system consists of nonductile reinforced-concrete moment frames, two of which were severely damaged during the earthquake. We address the issue of model selection. Two identification models are proposed: a time-varying linear model and a simplified time-varying nonlinear degradation model. The latter is derived from a nonlinear finite-element model of the building previously developed at Caltech. For the former model, the resulting performance is poor since the parameters need to vary significantly with time in order to capture the structural degradation of the building during the earthquake. The latter model performs better because it is able to characterize this degradation to a certain extent even with its parameters fixed. For this case study, the particle filter provides consistent state and parameter estimates, in contrast to the extended Kalman filter, which provides inconsistent estimates. It is concluded that for a state estimation procedure to be successful, at least two factors are essential: an appropriate estimation algorithm and a suitable identification model.