OFDM系统中基于预编码的时变信道半盲估计方法

讨论了一种适用于时变信道的OFDM系统半盲信道估计方法。该算法以一种基于线性预编码的信道估计技术为基础,利用接收信号的自相关矩阵所包含的信道信息进行信道估计。引入了一个具有单一参数的遗忘多项式,在时变信道下快速获得接收信号的自相关矩阵,使得该算法具有很强的信道跟踪能力。仿真结果表明,在时变多径瑞利衰落信道中,当信道仅在一个OFDM符号时间内保持不变时,该方法仍能够获得较高的信道估计精度和系统误比特率性能。     

Lp-stability of estimation errors of kalman filter for tracking time-varying parameters

The Kalman filtering algorithm, owing to its optimality in some sense, is widely used in systems and control, signal processing and many other fields. This paper presents a detailed analysis for the L_p-stability of tracking errors when the Kalman filter is used for tracking unknown time-varying parameters. The results of this paper differ from the previous ones in that the regression vector (in a linear regression model) or the output matrix (in state space terminology) is random rather than deterministic. The context is kept general so that, in particular, the time-varying parameter is allowed to be unbounded, and no assumption of stationarity or independence for signals is made.     

Convergence analysis of the RLS identification algorithm with exponential forgetting in stationary ARX‐structures

The recursive least‐squares (RLS) identification algorithm is often extended with exponential forgetting as a tool for parameter estimation in time‐varying stochastic systems. The statistical properties of the parameter estimates obtained from such an extended RLS‐algorithm depend in a non‐linear way on the time‐varying characteristics and on the forgetting factor. In this paper, the RLS‐estimator with exponential forgetting is applied to time‐invariant Gaussian autoregressions with second‐order stationary external inputs, i.e.to Gaussian ARX‐processes. Approximate expressions for the asymptotic bias and covariance of the parameter estimates when the forgetting factor tends to one and time to infinity are given, showing that the bias is non‐zero and that the covariance function decays exponentially with a rate that is given by the forgetting factor. The orders of magnitude of the errors in the asymptotic expressions are also derived. Copyright © 1999 John Wiley & Sons, Ltd.     

基于SRMA-UKF的永磁同步电机无传感器系统设计

为了解决传统UKF在永磁同步电机无传感器系统中存在的鲁棒性差、由于舍入误差导致的协方差矩阵发散的问题,提出了带遗忘因子的平方根UKF算法,在滤波过程中采用平方根矩阵代替协方差矩阵进入迭代运算,有效的克服了系统的发散问题,并且通过引入了遗忘因子的概念,将原有滤波器改造成强跟踪滤波器,从而提高了系统的鲁棒性。从MATLAB/Simulink仿真结果可以发现,与UKF、SR-UKF相比,对于突变状态的跟踪能力,SRMA-UKF有较大的提高,转子速度以及转子位置跟踪更精确,误差更小,鲁棒性得到提高。     

可变遗忘因子递推最小二乘法对时变参数测量

针对传统的递推最小二乘法对于非平稳环境下的突变和时变信号的跟踪能力不够,常常无法检测到信号特征参数的问题,提出了在指数加权递推最小二乘法中引入可变的加权遗忘因子λ,对电力系统时变信号的幅值、相位、频率进行测量的方法。加权λ对算法的收敛速度和跟踪能力有很大影响,如能很好的调节λ,既可确保对时变参数的快速跟踪能力,又能具备小的参数估计误差。仿真结果表明:与传统的递推最小二乘法相比,该方法测量精度和收敛速度更优越,即使在低信噪比环境下,也能较精确的测出时变参数值。     

基于自适应容积卡尔曼滤波算法的电力系统动态谐波状态估计

由于传统的谐波状态估计的参数辨识算法要求噪声的协方差矩阵固定不变,而实际工程中噪声的协方差矩阵是随时间变化的,工程中存在错误的量测数据,导致传统参数辨识算法估计的谐波电流参数的准确度较低。因此,提出自适应容积卡尔曼滤波算法来提高辨识谐波电流参数的准确度。首先,针对时变噪声干扰,采用基于渐消记忆指数加权法的噪声估值器算法生成时变噪声的协方差矩阵;其次,针对错误的量测数据,采用开窗估计算法修正错误的量测数据;然后,将修正的噪声协方差矩阵和量测数据代入容积卡尔曼滤波算法中,对谐波电流参数进行估计;最后,搭建IEEE 13节点系统仿真模型,验证了自适应容积卡尔曼滤波算法在时变噪声干扰及量测数据错误情况下仍可准确地估计谐波电流参数,确保了动态谐波状态估计的准确性。     

基于 SST-SCKF 的运动目标超宽带定位算法研究

使用超宽带(UWB)进行定位过程中,卡尔曼滤波是一种常见的降噪方法,但由于对非线性系统滤波性能差,且定位目标运动轨迹易超出基站布局区域以及受到异常噪声干扰,会影响定位系统的准确性和稳定性。针对这一问题,提出一种对称强跟踪(SST)平方根容积卡尔曼(SCKF)算法,通过引入对称时变渐消因子调节各协方差矩阵,实现改变误差协方差矩阵中多重衰落因子矩阵的工作方式,进而调整滤波增益,计算复杂度虽略有增加,但增强定位模型的适应性与鲁棒性。仿真验证表明,在异常噪声干扰下,改进后的算法(SST-SCKF)相较于SCKF/多重渐消因子的SCKF(ST-ASCKF)算法可有效提高定位准确度,且定位轨迹较于单渐消因子的SCKF算法(STSCKF)更为平滑;利用SST-SCKF算法设计基于UWB技术的定位方案,通过动态模拟实验表明,本文提出的SST-SCKF算法较之SCKF/STSCKF/ST-ASCKF滤波性能更优,为复杂环境噪声下人员UWB定位提供更好的降噪,使定位更为精准。     

噪声情况下的时变间谐波谱估计

间谐波幅值远小于基频或其它整数倍谐波的幅值,使其对噪声非常敏感,噪声往往会将这类微弱信号淹没。另一方面,实际间谐波频谱是随时间变化的,应看作随机信号来处理。该文提出一种基于4阶累积量的可变遗忘因子递推最小二乘法(cumulants recursive least square-variable forgetting factor,CRLS-VFF),将间谐波信号看作一个时变自回归(auto-regressive,AR)模型,利用参数化谱估计方法分辨率高的优点,将间谐波谱估计问题转化为时变AR参数的估计。4阶累积量可抑制任何高斯噪声,保证算法的频率分辨率;可变遗忘因子提高了算法跟踪时变参数的能力。对根据间谐波特点构建的仿真模型及典型的间谐波源——变频装置产生的信号进行仿真,结果证明：该方法能在噪声情况下准确估计出时变间谐波的频谱。     

Stability of Kalman filter for time-varying systems with correlated noise

The stability of a Kalman filter based on an adaptive estimator in the time average sense for a time-varying stochastic system with correlated noise is obtained under a persistent excitation condition. The stabilities of the closed-loop system and estimating error are established by designing an adaptive control law and restricting the growth rates of input and output signals. The stabilities of the extended least squares algorithm with forgetting factor and with covariance modification in the time average sense and sample average sense respectively are obtained. © 1997 by John Wiley & Sons, Ltd.     

A NEW ROBUST ALGORITHM FOR ESTIMATING THE STATE OF A POWER SYSTEM

ABSTRACT

The paper presents a new algorithm to estimate the state of a power system. It is essentially an improved version of the Weighted Least Squares (WLS) whcih is widely used in practice. In order to obtain an optimal estimate, the weighting matrix is to be chosen as the error covariance matrix in the WLS algorithm. In practice the error covariance is largely unknown and the weighting matrix is suitably tuned. There are problems of convergence if the weighting matrix is chosen as the inverse of E [VV^t]. The new algorithm overcomes such problems and is found to have good convergence characteristics.

Although it is essentially an improvement of the WLS algorithm, it differes from it in the method of implementation. It is based upon a technique used to obtain the pseudo-inverse of a non-square matrix and is an extension of it to include weighting factors. The new algorithm does not call for any type of tuning of the weighting matrix and the number of iterations to converge do not depend upon the mix of measurements or size of the system. This is the main contribution of the paper.     

Parallel architectures for RLS with directional forgetting

Efficient parallel architectures for recursive least squares with directional forgetting are presented. Two different arrays are proposed. The first employs O(n) processors and exhibits a 1/(2n + 3) throughput rate, n being the number of parameters to be estimated. The second can achieve a 1/(n + 2) throughput rate at the expense of an O(n²) processor complexity. Both architectures make use of the UD algorithm, here properly modified so as to embody the directional-forgetting variant.     

基于双边滤波的协方差目标跟踪算法

现有的使用协方差建模的目标跟踪方法在目标形变或是光照变化较大的情况下,达不到理想的跟踪效果。在分析目前协方差建模目标跟踪方法缺点的基础上,提出一种融合双边滤波的协方差目标跟踪算法。首先,将待跟踪图像进行双边滤波,提取滤波后的图像特征构建协方差特征矩阵作为跟踪模板。其次,由于协方差矩阵为对称正定流形,服从对数-欧几里德黎曼度量。在此度量下,构建了目标协方差矩阵相似性度量和模板更新策略。各种条件下的实验结果表明,新构建的基于双边滤波的协方差特征矩阵对目标形变和光照变化有更好的适应性。     

Uncertainty evaluation for estimates from recursive projection algorithms

In this paper we consider parameter estimation of linear systems described by y_i = a θ + e_i, where the ith measurement y_i is linearly dependent on the parameter vector θ ε ?^?p through the regressor vector a ε ?^?p and the measurement error e_i is unknown but bounded. Some properties of previously presented algorithms for recursive parameter identification in the unknown but bounded error (UBBE) context are discussed. In particular it is analysed how different levels of information on the error structure can influence the choice of the identification algorithms and the possibility of evaluating the reliability of the estimates. Attention is also focused on the influence that forgetting schemes have on the estimates and on their confidence evaluation.     

基于Sigma点卡尔曼滤波器的电力频率跟踪新算法

通过变换,首先将三相电压信号转换成一复电压信号,再利用一种复数型Sigma点卡尔曼滤波(CSPKF)算法以改进对发生谐波畸变和随机噪声干扰的电力系统电压信号的频率进行动态估计和跟踪的过程。理论证明,CSPKF算法与现有的复数型扩展卡尔曼滤波(ECKF)算法相比具有更佳的跟踪精度和稳定性。此外,CSPKF算法还成功解决了所有卡尔曼滤波算法都必须面对的当算法收敛后,系统参数发生突变的情况下需要重置误差协方差矩阵来重新跟踪这些变化的问题,进一步提高了其跟踪速度。对几种暂态电力信号模型的算法仿真表明,CSPKF算法具有优异的动态跟踪性能,迅速跟踪频率和幅值变化的同时又保持了较低的跟踪误差。     

用于电压闪变包络跟踪的改进RLS算法

电压闪变是严重的电能质量问题之一,本文提出采用基于可变遗忘因子的高阶累积量递推最小二乘(RLS)算法跟踪电压闪变包络。基于高阶累积量的误差准则取代传统的基于二阶统计量的误差准则,使算法不受任何高斯噪声的影响;可变遗忘因子的应用,使得算法能够快速跟踪包络的变化,又能在稳态情况下具有较好的收敛性能。Matlab仿真分析结果表明,在高斯噪声污染存在的情况下,本文算法相比传统RLS算法具有更高的估计精度,能够在噪声中准确地检测出电压闪变包络。     

基于可变遗忘因子广义RLS算法的频率估计

传统的递推最小二乘(RLS)算法有良好的抑制噪声的能力,但在非稳态环境下跟踪能力弱,导致误差大.RLS和Kalman滤波之间存在一一对应的关系,引入Kalman滤波的一步预测估计和新的状态转移矩阵,可以得到广义的RLS算法,该算法改进了跟踪能力.同时,考虑到加权遗忘因子对算法的收敛速度和跟踪能力也有很大影响,故在广义RLS算法中再引入可变的遗忘因子,以确保对时变参数的快速跟踪能力和小的参数估计误差.对基于可变遗忘因子的广义RLS自适应算法和按指数加权的传统RLS算法进行了仿真比较,分析了在稳态下加入谐波、输入幅值变化、输入频率变化等情况下,2种方法所得的频率估计值和均方误差,结果显示所提方法在精度和收敛速度上都更优越.     

An Adaptive Synchronous Machine Stabilizer

An adaptive synchronous machine stabilizer utilizing the least-squares identification with varying forgetting factor and a self-searching control strategy is proposed in this paper. The use of varying forgetting factor in the identification algorithm improves parameter tracking under both transient and dynamic conditions, and the use of a self-searching pole shifting control technique increases the flexibility when applied to varying operating conditions encountered in power systems. These features make this stabilizer very robust. Studies given in the paper show that the proposed self-tuning stabilizer provides excellent damping under varying operating conditions and with different types of disturbances.     

AUTOMATIC TIME STEP CONTROL IN TLM DIFFUSION MODELLING FOR PROBLEMS WITH TIME-VARYING LOAD CONDITIONS

Existing methods for time step control in TLM diffusion modelling that are based on the ‘m’ parameter introduced by Pulko are shown to be inappropriate for problems where the load conditions are arbitrarily time-varying. A new parameter is proposed for monitoring the error in the TLM diffusion model arising from the hyperbolic component of the lossy wave equation. This parameter is used as the controlling variable in a general purpose time step control algorithm and its suitability is demonstrated by applying this algorithm to a number of test cases in which the load conditions are time-varying.     

基于WAMS／SCADA混合量测的电力系统强跟踪滤波动态状态估计

针对当前电力系统动态状态估计主要采用的扩展卡尔曼滤波(EKF)法存在鲁棒性差、建模具有不确定性等缺点,提出一种强跟踪滤波动态状态估计算法.该算法在扩展卡尔曼滤波器中引入时变次优渐消因子,在线调整状态预报误差协方差矩阵和相应的增益矩阵,使状态估计残差方差最小.同时,引入广域测量系统(WAMS)/-数据采集与监视控制(SCADA)系统的混合量测数据,增加了系统的冗余量测,进一步提高了动态状态估计的性能.仿真结果表明,所提方法在正常情况以及负荷突变、存在坏数据、网络拓扑错误各种情况下具有较好的预测和滤波效果.     

重载机车粘着性能参数的极大似然辨识方法

针对重载机车运行中机车的粘着利用率低、易空转、易打滑的问题,提出一种对轨面粘着性能参数的实时在线估计算法。首先从分析机车粘着行为出发,选用Kiencke的粘着-蠕滑模型作为辨识模型,然后算法利用极大似然意义下的模型参数辨识框架,将参数估计转化为二次规划问题求解,进而构造出辨识的迭代算法。同时考虑到轮轨环境突变的不可测,辨识算法引入时变遗忘因子来适应轨面环境的切换。仿真结果表明,该算法能及时跟踪上轮轨环境的变化,有效辨识出粘着性能参数。