Optimal and self‐tuning fusion Kalman filters for discrete‐time stochastic singular systems

Based on the optimal fusion estimation algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion Kalman filter weighted by scalars is presented for discrete‐time stochastic singular systems with multiple sensors and correlated noises. A cross‐covariance matrix of filtering errors between any two sensors is derived. When the noise statistical information is unknown, a distributed identification approach is presented based on correlation functions and the weighted average method. Further, a distributed self‐tuning fusion filter is given, which includes two stage fusions where the first‐stage fusion is used to identify the noise covariance and the second‐stage fusion is used to obtain the fusion state filter. A simulation verifies the effectiveness of the proposed algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

Distributed stubborn-set-membership filtering with a dynamic event-based scheme: The Takagi-Sugeno fuzzy framework

The article develops a novel stubborn set-membership filtering with the dynamic event-triggered scheme for discrete-time nonlinear systems with the state-delays, unknown-but-bounded noises as well as abnormal measurements. First, in comparison with traditional event schemes, a dynamic event-triggered scheme with a time-varying auxiliary offset variable in the threshold is employed to schedule the access token with the purpose of mitigating the communication burden. A novel fuzzy stubborn filter in a distributed way is then constructed to obtain the ellipsoid set including the real state while constraining the direct influence of abnormal measurements, which could come from outliers or a malicious modification by attackers. An algorithm with the form of recursive linear matrix inequalities is presented to determine the desired ellipsoids as well as the distributed filter gains, where the operation of the algorithm do not depend on the global information of network topology. Finally, the validity of the proposed scheme is illustrated via an inverted pendulum system.     

State estimation of discrete‐time systems with arbitrarily correlated noises

In this paper, the state estimation problem for discrete‐time systems is considered where the noises affecting such systems do not require any constraint condition for the correlation and distribution, that is, the noises can be arbitrarily correlated and arbitrarily distributed random vector. For this, two filtering algorithms based on the criterion of linear minimum mean‐square error are proposed. The first algorithm is an optimal algorithm that can exactly compute the linear minimum mean‐square error estimate of system states. The second algorithm is a suboptimal algorithm that is proposed to reduce the computation and storage load of the proposed optimal algorithm. Computer simulations are carried out to evaluate the performance of the proposed algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive finite‐dimensional optimal linear filtering of nD smooth Gaussian random fields

This paper deals with the optimal filtering problem of nD sampled, Gaussian random fields. The filtering algorithm is based on a state‐space signal model analytically derived from the assumption that the continuous Gaussian random field can be well approximated, almost everywhere, by a continuously differentiable nD surface. An appealing feature of the proposed optimal filter is that it is not based on nD strip processing schemes. The filtering algorithm has a structure which is recursive both with respect to the point‐to‐point scanning procedure of the sampled field and to the dimensionality of the estimate computed at each point. This greatly reduces the numerical complexity of the filtering scheme. The filtering algorithm requires the knowledge of some statistical parameters of the random field. For a greater generality, a procedure for the adaptive estimation of these parameters is also provided. Numerical results are reported to illustrate the applicability and performance of the proposed filter. Copyright © 2006 John Wiley & Sons, Ltd.     

Truncation filtering method for envelope analysis

Envelope analysis is an effective tool for bearing fault diagnosis. In most cases, the fault signal of the bearing that is located deeply inside the machine is always covered by the huge wide‐band noise. However, the common envelope analysis cannot effectively extract the fault feature from the frequency band containing huge wide‐band noise. Therefore, a modified envelope analysis that contains a new band‐pass filter based on resonance band and a new matched filtering computing method is proposed. The common envelope analysis is always concentrated on the selection of the filtering band and filter's other parameters, which can just control some parts of the filter. However, the whole shape of the filter has great effects on every element content of the filtered signal. Thus, according to the distribution characteristic of fault impact spectrum lines, the shape of the freedom damping vibration excited by single impact in frequency domain can be used as a filter for the truncation filtering to obtain the optimal envelope spectrum. Since the envelope shape of practical fault impact spectrum lines is not exactly like the shape of single impact in frequency domain, a scan filtering method is added to the band‐pass filtering, which is to use different central frequencies with the same interval and different bandwidths for filtering. A complicated structure test rig that is self‐innovate is used for experimental validation. The experimental result shows that fault feature extracted by the proposed method is better than the one extracted by the common envelope analysis.     

应用多级有源滤波系统的分布式电源谐波治理

有源滤波器是配电网谐波治理的动态补偿装置,但分布式电源的接入造成配电网谐波变得复杂,单一的有源滤波器在补偿线路谐波方面难以达到效果。设计了一种多级有源滤波系统,将补偿源设置在各电源母线处,得到一组含多个补偿源的分布式电源接入的配电网模型,通过调节滞环宽度,稳定开关频率。采用改进后的滞环空间矢量控制对各有源滤波单元分别进行控制,并基于MATLAB/Simulink仿真平台,分别搭建了含单一滤波器和两级有源滤波系统的分布式电源接入的配电网仿真模型,仿真对比分析了谐波治理的效果,仿真结果验证了多级有源滤波系统对分布式电源接入的配电网谐波治理的有效性和可行性。     

Optimal state filter from sequential unknown input reconstruction: Application to distributed state filtering

This paper generalizes Kalman filtering with an intermittent unknown input problem to be left invertible discrete‐time stochastic linear systems with zero, one, or more structural delays. Contrary to the state filtering–based system inversion where the unknown input vector is reconstructed with a time delay that is equal to the structural delay of the plant, we propose an optimal state filtering by reconstructing some linear combinations of the unknown input vector with a time delay less than the structural delay. Designed under a sequential unknown input decoupling constraint, which has never been previously studied in the literature, all presented filters are very computationally efficient. The proposed state filtering is used to solve the autonomous distributed state filtering problem in large‐scale networked control systems when the unknown input vector represents interactions between subsystems and when each subsystem receives intermittent information about the interaction from unreliable networks. The stochastic stability conditions of the extended intermittent unknown input Kalman filter are established when the arrival binary sequence of packet dropouts follows a random Bernoulli process.     

基于改进EEMD方法的数字滤波器

提出一种基于改进的整体平均经验模态分解( EEMD)方法的数字滤波器.针对经验模态分解( EMD)滤波方法的不足,采用对含噪信号先用中值滤波法平滑处理,再对处理后的信号EEMD的方法.改进后的方法结合了平滑处理滤除脉冲噪声以及EEMD方法滤除随机噪声和高频连续噪声的优点,且不存在模态混叠.通过数字仿真,研究了其滤波性能...     

Quantized H∞ filtering for state‐delayed systems with finite frequency specifications

This paper presents a novel design approach for the finite frequency (FF) H_∞ filtering problem for discrete‐time state‐delayed systems with quantized measurements. The system state and output are assumed affected by FF external noises. Attention is focused on the design of a stable filter that guarantees the stability and a prescribed ?₂ gain performance level for the filtering error system in the FF domain of input noises. Sufficient conditions for the solvability of this problem are developed by choosing an appropriate Lyapunov‐Krasovskii functional based on the delay partitioning technique and using the FF ?₂ gain definition combined with the generalized S‐procedure. Then, by means of Finsler's lemma, the derived conditions are linearized and additional slack variables are further introduced to more flexible result. Final filter design conditions are consequently established in terms of linear matrix inequalities in three different frequency ranges, ie, low‐, middle‐ and high‐frequency range. Finally, a simulation example is presented to illustrate the effectiveness and the merits of the proposed approach.     

Implementation of a square‐root filtering approach in marginalized particle filters for mixed linear/nonlinear state‐space models

Marginalized particle filter (MPF) takes advantage of both Kalman filter and particle filter frameworks to estimate nonlinear state‐space models with reduced number of calculations in comparison to particle filter. However, due to existence of Kalman filter framework inside MPF, some limitations are introduced in implementation of MPF especially in embedded systems with finite numerical accuracies. In this paper, for the first time, we propose a novel square‐root filtering strategy for MPFs to alleviate these restrictions using modified factorization. Typical square‐root Kalman filters cannot be employed inside MPF due to the presence of minus operations in some equations of MPF. However, our method can be easily implemented inside the MPF structure. The proposed method can be used in any application that employs MPFs to estimate the mixed linear/nonlinear state‐space models. In order to demonstrate its usefulness, we employed the proposed square‐root filtering method inside a marginalized particle extended Kalman filter (MP‐EKF) structure, which was specifically designed for ECG denoising. The experimental results showed that, in the field of ECG denoising, the square‐root MP‐EKF performs more consistently than MP‐EKF in white Gaussian noises.     

横向时延滤波和IQ正交滤波的滤波效果仿真

由于横向时延滤波和IQ正交滤波在稳定性、收敛速度和误差率等方面的性能差异对于最小均方算法(LMS,Least Mean Squares)在工程上的实际应用具有参考意义,采用数值仿真的方法模拟了两者实现自适应最小均方算法的具体过程,并对比分析了它们的滤波性能。仿真结果显示IQ正交滤波具有更高的稳定性、更快的收敛速度、更小的误差率,其滤波性能优于横向时延滤波。IQ正交滤波器的阶数为2阶,而横向时延滤波器的阶数通常>10,滤波器阶数越多引入的权噪声也会相对增加,对于稳定性、收敛速度和误差率都是不利的,因此实际应用中优先选择阶数较少的IQ正交滤波器。     

基于正交滤波函数的快速窄带滤波算法

在分析窄带滤波算法的基波提取能力和响应时延特性的基础上,结合正交滤波算法的滤波性能,提出了一种基于正交滤波函数的提取基波分量的快速窄带滤波算法。采用两个与数据窗长有关的三角函数作为正交滤波函数,求得其不同数据窗长下相量的实虚部,推导得到各数据窗长下窄带滤波器初值的计算式,从而求取窄带滤波器初值,缩短其响应时延。该滤波算法滤波效果好,响应速度快,能够大大提高电网保护和控制系统的快速性。     

融合自适应滤波和归一化PGC-Arctan的激光干涉测振信号解调算法研究

针对正弦相位调制激光干涉仪测振信号的解调,本文提出一种融合自适应滤波和归一化PGC-Arctan的解调方法。该方法在传统PGC-Arctan算法的基础上,通过准确识别载波相位延迟和相位调制深度对正交干涉信号对进行归一化处理,从而减小相位解调的非线性误差,同时引入基于最小均方算法的自适应滤波器对解调信号进行滤波降噪,进一步提高信号的SNDR（信噪失真比）。通过数值仿真和实验测试验证了算法的有效性,在实验室条件下,对频率为100Hz-3KHz声波激励的固体表面微振动进行了探测和解调。结果表明本文所述方法能够实现振动信号的精确解调,经自适应滤波后解调信号的SNDR平均提升了12dB。 关键词：激光测振;归一化;PGC-Arctan; 自适应滤波     

Distributed H2/H∞ filtering over infinite horizon

This paper studies distributed H₂/H_∞ filtering problem with the aid of neighbors’ information. It is assumed that there are both bounded power uncertainty and stochastic white noise in the model of the considered system. A 2‐step design approach is proposed to calculate the observer gain and the coupling gain in the proposed observers. In order to reduce the computation load of solving coupled matrix equations, a simplified design procedure is also proposed. Simulation of 2 examples shows the effectiveness of the proposed filter design procedure.     

Optimal linear filtering for networked systems with communication constraints,fading measurements,and multiplicative noises

This paper is concerned with the filtering problem for discrete‐time networked systems with communication constraints, fading measurements, and multiplicative noises. The communication constraint is that, at each sampling instant, at most one of the various transmission nodes in the networked systems is permitted to access a shared communication channel, and then the received data are transmitted to a remote filter to perform the filtering task. The phenomenon of measurement fading appears in a random way when measurements are transmitted via the communication channel that undergoes slow fading. Using the innovation analysis approach and some results developed in this study, an optimal linear filter is proposed. The proposed optimal filter has a recursive structure and does not increase computation and storage load with time. Computer simulations are conducted to examine the performance of the proposed optimal filter. Copyright © 2016 John Wiley & Sons, Ltd.     

Kalman滤波新息正交性抗野值法研究

在Kalman滤波应用过程中，观测值中的野值是影响滤波效果的重要因素。当观测中含有野值时，破坏了Kalman滤波新息的正交性，从而造成估计不准，滤波精度下降。本文提出了修正Kalman滤波新息正交性的方法，使修正后的Kalman滤波新息能够保持修正前的新息正交性。仿真结果表明，本文提出的方法有效地抑制观测中的野值对系统滤波的不利影响。     

基于LMS算法的分布式光纤测温系统中信号处理研究

光纤测温技术已基本趋于成熟,但系统的测量精度还有待提高,所以分布式光纤测温系统的信号处理对系统具有极其重要的意义,也是近年来的研究热点。在分析了系统的信号特点后,提出采用自适应滤波算法对反斯托克斯信号进行去噪处理并进行MATLAB仿真,验证该算法的可行性性。还将该滤波算法的不同种类进行多种组合实现对拉曼散射信号的二次去噪处理,并比较几种不同的自适应滤波算法及其组合的仿真效果。     

基于复合结构元素的新型自适应形态滤波器设计

针对电力系统信号采集中常见的噪声干扰问题,提出一种使用复合结构元素的自适应形态学滤波器。这种滤波器融合两种及以上结构元素作为复合结构元素,统计分析输入信号与拟输出滤波信号之间的滤波误差,寻找复合结构元素的整体最优尺度,从而优化滤波效果。根据滤波误差极大值原理,调整组成复合结构元素的两种元素的参数占比,进一步优化,可得最优滤波使用的复合结构元素。仿真实验对包含随机白噪声的电力信号进行自适应滤波,结果表明,在面向随机噪声时,所提出的自适应滤波器能够准确寻求到最优结构元素,滤波性能优于使用单一结构元素的传统形态滤波器,具有良好的应用前景。     

基于进化硬件的自适应形态滤波器设计

针对电力信号监测中可能遇到的各种干扰,提出了一种基于进化硬件的自适应电力系统形态滤波器的设计方法。不仅滤波算法可自适应调节,而且结构元素形状和尺寸也可自动调整。以遗传算法作为优化工具,在Xilinx XCV2000-E 现场可编程门阵列上对滤波器结构和参数进行在线优化。对典型噪声的在线滤波结果表明,该滤波器能够根据噪声的不同自适应调整结构和参数,因而鲁棒性好,适用范围广,滤波性能比均值滤波器和中值滤波器更好,且该滤波器是采用硬件实时实现的,便于在嵌入式系统中应用。     

应用切比雪夫多项式滤波的高渗透率光伏分布式控制策略

分布式控制只需要局部通信,在地域分散的光伏功率控制中具有良好的鲁棒性和灵活性。提出一种基于一致性算法的光伏分布式控制策略,各控制节点与相邻节点交换当前时刻光伏最大功率点和负荷信息,通过一致性算法迭代估计有功输出参考点。在此基础上,利用由二次插值方法得到的P-V曲线计算逆变器直流侧电压参考值。同时,为解决控制对象较多引起的一致性算法收敛速度慢的问题,使用切比雪夫滤波多项式对一致性算法进行滤波加速。该方法在不违背算法分布式特征的前提下,加快了收敛速度,并对变化的通信拓扑具有较好的鲁棒性能。最后,通过算例验证了所提方法的有效性。