Second‐order blind algorithms for adaptive recursive linear equalizers: analysis and lattice implementation

Adaptive recursive linear equalizers present important advantages in terms of performance and robustness compared to more standard finite impulse response structures, and provide a means for blindly initializing the decision feedback structure. We present an analysis of a pair of algorithms for the adaptation of the recursive part of the equalizer, which are based on the second‐order statistics of the received signal, in a multichannel complex‐valued setting with spatially coloured noise. When the number of equalizer poles is no less than the channel order, both algorithms enjoy a unique stationary point, which in addition is locally convergent; global convergence properties, on the other hand, can be quite different. When the optimum setting presents poles close to the stability boundary, the lattice structure is preferred for ease of stability monitoring. Lattice versions of the two algorithms are developed and their convergence properties discussed. Copyright © 2006 John Wiley & Sons, Ltd.     

An adaptive digital filter for noise attenuation in sampled control systems

A recursive smoothing filter employing a bank of fading‐memory polynomial sub‐filters is presented. Variance estimates are used to mix the outputs of the sub‐filters, imparting variable gain and phase characteristics that permit it to automatically adapt to signal parameter changes. The proposed adaptive technique does not involve the estimation of plant parameters; therefore, it may be used in both open‐loop and closed‐loop configurations. In open‐loop estimation problems, variable gain/bandwidth allows it to reduce the impact of random errors caused by sensor noise and the impact of bias errors caused by model mismatch during ‘maneuvers’. In feedback control problems, variable phase/delay allows it to act as a lag filter for an improved steady‐state response (i.e. greater noise attenuation) and act as a lead filter, or a proportional‐derivative controller, for an improved transient response (i.e. increased closed‐loop damping) for input discontinuities. Copyright © 2015 John Wiley & Sons, Ltd.     

On orthogonal realizations for adaptive IIR filters

Convergence speed is one of the main concerns in adaptive IIR filters. Fast convergence can be closely related to adaptive filter realization. However, with the exception of the lattice realization that is based on the nice properties of Szëgo orthonormal polynomials, no other adaptive IIR filter realization using orthonormal characteristics seems to be extensively studied in the literature. Furthermore, many orthogonal realizations for adaptive FIR filters, that are particularly suitable for rational modelling, have been proposed in the past years. Since rational orthogonal basis functions are a powerful tool for efficient system representation they seem attractive for adaptive IIR filters. In this paper, we present some theoretical results related to the properties of a generalized orthonormal realization when used for mean‐square output error minimization in a system identification application. One result is related to the low computational complexity of the updating gradient algorithm when some properties of the orthonormal realization are used. An additional result establishes conditions for the stationary points of the proposed updating algorithm. In order to confirm the expected performance of the new realization, some simulations and comparisons with competing realizations in terms of computational complexity and convergence speed are presented. Copyright © 2000 John Wiley & Sons, Ltd.     

基于自适应滤波器的地雷背景噪声抑制

作为一种新的探雷方法,探地雷达在探雷领域得到了广泛的应用。由于雷达回波信号包含有很多杂散信号,目标信号被强噪声信号所淹没,为了提高雷达回波信号的信噪比,有利于地雷的识别和检测,文章提出了一种基于自适应滤波器的背景噪声消除方法对回波信号进行杂波抑制。采用该方法对DeTeC的数据进行分析的结果表明,该方法能有效地去除直达波,抑制背景噪声,分离出目标信号。     

基于巴特沃思滤波器的有源电力滤波研究

叙述了基于自适应滤波中的自适应干扰抵消原理,从负载电流中消去基波有功分量,从而得到所需补偿的电流值的自适应检测法.介绍了ⅡR(无限冲击响应)数字滤波器的算法结构,并给出了直接Ⅱ型滤波器结构图,设计了3阶巴特沃思低通数字滤波器,利用Matlab对其进行建模和仿真,验证了该滤波器能有效地补偿谐波,表明了该控制方法的可行性.     

A hybrid gradient‐based and differential evolution algorithm for infinite impulse response adaptive filtering

Global optimization algorithms (GO) had been applied to solve the adaptive infinite impulse response filtering problem, which is known to have multimodal error surface under certain conditions. However, although GO may be able to search multimodal surfaces, they have certain disadvantages. They may not converge to any minimum point, the convergence speed is reduced as the solution vectors move closer, and tracking ability for non‐stationary environment is lacking. The traditional gradient descent method does not have these limitation but is not able to search multimodal surfaces. In this work, we propose a hybrid algorithm combining gradient descent and differential evolution (DE) for adapting the coefficients of infinite impulse response adaptive filters. DE is run in a block‐based manner. The coefficient vector with the lowest error surface value (the best member) of the current block is updated via gradient descent for the duration of the next block. Thus combining the ability to search multimodal surface of DE and fast local search of gradient descent. As with all GO, global search capacity is gradually lost as the coefficient vectors converge together. Thus, re‐initialization is also incorporated into the hybrid algorithm to provide continuous global search capacity for non‐stationary environment. All the coefficient vectors except the best member are re‐initialized when the normalized mean Euclidean distance between each pair of vectors falls below a threshold value. Simulation results show that the proposed algorithm achieves better solution quality and convergence speed than classic DE and GO for stationary and non‐stationary environments. Copyright © 2013 John Wiley & Sons, Ltd.     

Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output-error systems

This article considers the parameter estimation problem of Hammerstein nonlinear autoregressive output-error systems with autoregressive moving average noises. Applying the key term separation technique, the original system is decomposed into three subsystems: the first subsystem contains the unknown parameters related to the output, the second subsystem contains the unknown parameters related to the input, and the third subsystem contains the unknown parameters related to the noise model. A hierarchical recursive least squares algorithm is proposed based on the hierarchical identification principle for interactively identifying each subsystem. The simulation results confirm that the proposed algorithm is effective in estimating the parameters of Hammerstein nonlinear autoregressive output-error systems.     

Application of adaptive lattice filters to internal model control

An adaptive internal model control (IMC) framework is proposed in this article for infinite impulse response systems. The innovation in this study stems from the relaxed assumption that the controller needs to know a priori the system order. To bypass this restriction, a lattice filter identifies the system's order as well as its reflection coefficients. Within the IMC structure, a lattice‐based controller is utilized in the forward path in cascade with a low‐pass detuning filter. The controller self‐configures its structure according to the estimated system order, while the filter's bandwidth increases as the identifier estimates more accurately the system dynamics. Copyright © 2007 John Wiley & Sons, Ltd.     

Design of a near‐optimal adaptive filter in digital signal processor for active noise control

Adaptive filter has been applied in adaptive feedback and feedforward control systems, where the filter dimension is often determined by trial‐and‐error. The controller design based on a near‐optimal adaptive filter in digital signal processor (DSP) is developed in this paper for real‐time applications. The design integrates the adaptive filter and the experimental design such that their advantages in stability and robustness can be combined. The near‐optimal set of controller parameters, including the sampling rate, the dimension of system identification model, the dimension (order) of adaptive controller in the form of an FIR filter, and the convergence rate of adaptation is shown to achieve the best possible system performance. In addition, the sensitivity of each design parameter can be determined by analysis of means and analysis of variance. Effectiveness of the adaptive controller on a DSP is validated by an active noise control experiment. Copyright © 2007 John Wiley & Sons, Ltd.     

Adaptive filtering‐based multi‐innovation gradient algorithm for input nonlinear systems with autoregressive noise

In this paper, by means of the adaptive filtering technique and the multi‐innovation identification theory, an adaptive filtering‐based multi‐innovation stochastic gradient identification algorithm is derived for Hammerstein nonlinear systems with colored noise. The new adaptive filtering configuration consists of a noise whitening filter and a parameter estimator. The simulation results show that the proposed algorithm has higher parameter estimation accuracies and faster convergence rates than the multi‐innovation stochastic gradient algorithm for the same innovation length. As the innovation length increases, the filtering‐based multi‐innovation stochastic gradient algorithm gives smaller parameter estimation errors than the recursive least squares algorithm.     

Nonlinear adaptive filtering using kernel‐based algorithms with dictionary adaptation

Nonlinear adaptive filtering has been extensively studied in the literature, using, for example, Volterra filters or neural networks. Recently, kernel methods have been offering an interesting alternative because they provide a simple extension of linear algorithms to the nonlinear case. The main drawback of online system identification with kernel methods is that the filter complexity increases with time, a limitation resulting from the representer theorem, which states that all past input vectors are required. To overcome this drawback, a particular subset of these input vectors (called dictionary) must be selected to ensure complexity control and good performance. Up to now, all authors considered that, after being introduced into the dictionary, elements stay unchanged even if, because of nonstationarity, they become useless to predict the system output. The objective of this paper is to present an adaptation scheme of dictionary elements, which are considered here as adjustable model parameters, by deriving a gradient‐based method under collinearity constraints. The main interest is to ensure a better tracking performance. To evaluate our approach, dictionary adaptation is introduced into three well‐known kernel‐based adaptive algorithms: kernel recursive least squares, kernel normalized least mean squares, and kernel affine projection. The performance is evaluated on nonlinear adaptive filtering of simulated and real data sets. As confirmed by experiments, our dictionary adaptation scheme allows either complexity reduction or a decrease of the instantaneous quadratic error, or both simultaneously. Copyright © 2015 John Wiley & Sons, Ltd.     

有源滤波器参考电流的自适应检测方法

介绍了一种自适应滤波系统，用于快速准确地检测出有源江波器的参考电流，该方法采用干扰自适应对消原理实现自适应谐波及无功检测电路，具有较强的自适应性，不受负载或电网状态的变化影响，能满足有源能力滤波器对谐波及无功检测的要求，仿真结果证明了这种检测方法的有效性。     

A robust optimal design for strictly positive realness in recursive parameter adaptation

This paper provides an optimization‐based approach to assure the strict positive real (SPR) condition in a set of recursive parameter adaptation algorithms (PAA). The developed algorithms and tools enable a multiobjective formulation of the SPR problem, creating new controls of the stability and parameter convergence in PAAs. In addition to assuring the SPR condition for global stability in PAAs, we provide an algorithmic solution for uniform convergence under performance constraints in PAAs. Several new aspects of parameter convergence were observed with the adoption of the algorithm in a narrow‐band identification problem. The proposed algorithm is verified in simulation and experiments on a precision motion control platform in advanced manufacturing.     

Online identification of time-delay jump Markov autoregressive exogenous systems with recursive expectation-maximization algorithm

This article considers the identification problem of the jump Markov autoregressive exogenous (JMARX) systems with unknown invariant time-delay under the framework of recursive expectation-maximization (REM) algorithm. In this article, a recursive Q-function is formulated for the JMARX systems, based on which the recursive sufficient statistics are obtained. Then, the parameter vectors, variance, transition probability matrix, and time-delay are recursively estimated. A numerical example and a simulated continuous fermentation reactor process are employed to illustrate the effectiveness of the proposed algorithm.     

水风光互补适应性调度的集合卡尔曼滤波方法北大核心CSCD

水风光互补是实现能源转型、助力碳中和的重要途径。传统的水风光互补中长期调度模型,忽略了气候变化和人类活动引起的水风光资源变异,得到的静态调度规则难以适应变化环境。为此,本研究采用集合卡尔曼滤波技术开展适应性调度研究。首先进行确定性优化调度,得到最优运行轨迹;其次识别调度函数的决策变量与自变量,构建线性调度函数;最后采用集合卡尔曼滤波动态更新调度函数的参数,形成适应性调度规则。以锦屏一级水电站及周边风、光电站为实例,研究表明适应性调度规则较常规调度和静态调度,在检验期年均发电量分别提高6.45亿k W·h和0.53亿k W·h,说明论文所提方法可有效追踪最优轨迹的变化来适应变化环境。     

基于自适应强跟踪滤波器的汽车行驶状态软测量

针对汽车底盘控制系统中一些关键汽车行驶状态难以准确直接测量以及测量成本较高的问题,在自适应卡尔曼滤波算法和强跟踪滤波算法的基础上,提出基于自适应强跟踪滤波器的汽车行驶状态估计方法.结合纵向、侧向和横摆3自由度非线性汽车模型,将其应用于汽车行驶状态的软测量之中,并与扩展卡尔曼滤波算法进行比较分析.通过Carsim和Matlab/SIMULINK联合仿真的双移线试验的结果表明,在非线性区域内,自适应强跟踪滤波器能快速、准确跟踪汽车状态.该算法在估计精确度、跟踪速度、抑制噪声等方面均优于扩展卡尔曼滤波算法,满足汽车状态估计器的软件性能要求.     

An integrated approach to modeling and adaptive control

In the book (Adaptive Identification, Prediction and Control—Multi Level Recursive Approach), the concept of dynamical linearization of nonlinear systems has been presented. This dynamical linearization is formal only, not a real linearization. From the linearization procedure, we can find a new approach of system identification, which is on-line real-time modeling and real-time feedback control correction. The modeling and real-time feedback control have been integrated in the identification approach, with the parameter adaptation model being abandoned. The structure adaptation of control systems has been achieved, which avoids the complex modeling steps. The objective of this paper is to introduce the approach of integrated modeling and control. __________ Translated from Acta Automatica Sinica, 2004, 30(3): 380–389 (in Chinese)     

A fast RLS transversal filter for adaptive linear phase filtering

A fast transversal filter algorithm is presented for the adaptive recursive least-squares design of filters with linear phase. Connections with a popular method of spectrum estimation are explored. The algorithm is derived in a new framework, for the prewindowed given data case. Exact and soft-constraint initialization are discussed and an improved rescue procedure is proposed. Data sequence weighting is introduced to allow for arbitrarily time-varying weighting strategies, facilitating the tracking of arbitrarily non-stationary phenomena.     

基于系统辨识的汽包水位自适应控制

针对汽包锅炉给水过程的特点,采用了一种带在线辨识的自适应P ID控制器。利用递推最小二乘法循环调整P ID参数,并将该控制器应用于锅炉汽包水位三冲量控制系统。MATLAB仿真研究表明,与传统的P ID算法比较,该控制方案表现出了良好的动、静态性能,并能适应被控制对象参数的变化,具有较强的鲁棒性和自适应能力。     

Selective partial update and set‐membership improved proportionate normalized subband adaptive filter algorithms

In this article, the concept of proportionate adaptation is extended to the selective partial update (SPU) and set‐membership (SM) normalized subband adaptive filters (NSAFs), and three proportionate normalized subband adaptive filter algorithms are established. The proposed algorithms are the improved proportionate NSAF (IPNSAF), the SPU improved proportionate NSAF (SPU‐IPNSAF), and the SM‐IPNSAF which are suitable for sparse system identification. When the impulse response of the echo path is sparse, the IPNSAF algorithm has faster convergence than NSAF. The performance of IPNSAF is also suitable for dispersive impulse responses. In SPU‐IPNSAF, the filter coefficients are partially updated rather than the entire filter at every adaptation which reduces the computational complexity of IPNSAF. The SM‐IPNSAF exhibits good performance with significant reduction in the overall computational complexity compared with the ordinary IPNSAF. The simulation results show good performance of the proposed algorithms. Copyright © 2010 John Wiley & Sons, Ltd.