A numerically stable,finite memory,fast array recursive least squares filter for broadband active noise control

For broadband active noise control applications with a rapidly changing primary path, it is desirable to find algorithms with a rapid convergence, a fast tracking performance, and a low computational cost. Recently, a promising algorithm has been presented, called the fast‐array Kalman filter, which uses rotation matrices to calculate the filter parameters. However, when this algorithm is implemented, it can show unstable behavior because of finite precision error propagation. In this paper, a novel algorithm is presented, which exhibits the fast convergence and tracking properties and the linear calculation complexity of the fast‐array Kalman filter but does not suffer from the mentioned numerical problems. This is accomplished by running two finite length growing memory recursive least squares filters in parallel and using a convex combination of the two filters when the control signal is calculated. A reset of the filter parameters with proper re‐initialization is enforced periodically. The mixing parameters will be chosen in such a way that the total available information used for the calculation of the control signal will be approximately equal at every time instance. The performance of the filter is shown in numerical simulations and real‐time lab experiments. The numerical experiments show that the algorithm performs better numerically than the fast‐array sliding window recursive least squares filter, while achieving a comparable convergence rate and tracking performance. The real‐time lab experiments confirm the behavior shown in the simulations. Copyright © 2015 John Wiley & Sons, Ltd.     

Nonmyopic sensor scheduling for target tracking with emission control

Active sensors obtain the measurements of targets by emitting energy that can be intercepted by enemy surveillance sensors. To satisfy the target tracking requirement and control the whole system emission, we propose a nonmyopic sensor scheduling to minimize the emission cost while maintaining a desired tracking accuracy. The processes of target tracking and emission control are formulated as a partially observable Markov decision process. Then, we translate our scheduling problem to a discrete unconstrained optimization problem, which consists of multistep emission cost and multistep tracking accuracy cost. Furthermore, the cubature Kalman filter is utilized to update the target belief state and predict the multistep tracking accuracy cost, whereas the multistep emission cost is obtained by hidden Markov model filter. Scheduling is implemented efficiently by constructing a decision tree and using a search algorithm, which combines uniform cost search with augmented branch and bound technique. Simulation results demonstrate the effectiveness of our proposed method.     

Self‐tuning fusion Kalman filter weighted by scalars and its convergence analysis for multi‐channel autoregressive moving average signals

For the multi‐sensor multi‐channel autoregressive (AR) moving average signals with white measurement noises and an AR‐colored measurement noise, a multi‐stage information fusion identification method is presented when model parameters and noise variances are partially unknown. The local estimators of model parameters and noise variances are obtained by the multidimensional recursive instrumental variable algorithm and correlation method, and the fused estimators are obtained by taking the average of the local estimators. They have the strong consistency. Substituting them into the optimal information fusion Kalman filter weighted by scalars, a self‐tuning fusion Kalman filter for multi‐channel AR moving average signals is presented. Applying the dynamic error system analysis method, it is proved that the proposed self‐tuning fusion Kalman filter converges to the optimal fusion Kalman filter in a realization, so that it has asymptotic optimality. A simulation example for a target tracking system with three sensors shows its effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.     

基于Kalman滤波器的车辆检测与跟踪系统的实现

智能交通系统是解决城市交通拥挤最有效的方式,其中交通信息采集设备是交通系统管理的基础与前提,而基于视频图像处理的交通信息检测器较其他类型检测器,具有信息量丰富,安装和维护成本低廉的特点.本文用基于Kalman滤波器的方法实现了交通信息采集设备中的车辆检测与跟踪.它采用了一种自适应背景更新算法,通过分割、二值化、腐蚀膨胀得出前景图像,以包含前景图像的矩形框的中心作为Kalman滤波器的跟踪特征,对运动车辆进行跟踪估计得出车辆的运动轨迹和速度.一系列的视频实验表明,该方法简单可行而且对天气、光照变化、阴影有很强的适应能力.     

Achievement method of high‐speed response of error prediction tracking control for optical disk system

This paper proposes the new design method of error‐prediction control systems combining the ZPET control and the robust feedback control. The error‐prediction control system based on ZPET control can reduce the tracking error caused by periodic disturbances more efficiently than the conventional repetitive control technique. However, the proposed tracking servo system does not reduce the residual tracking error caused by the low‐pass filter of the feedforward compensation and by the long sampling time. In order to overcome this problem, this paper proposes the new structure of the feedforward tracking control system. The modified ZPET control tracking servo system for optical disk recording system does not include the low‐pass filter and its sampling time becomes faster. On the other hand, the sudden disturbance observer reduces the influence of nonperiodic disturbances. The experimental results point out that the proposed tracking servo system has a precise tracking response against both the periodic disturbances and the nonperiodic disturbances. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 170(4): 51–59, 2010; Published online in Wiley InterScience ( www.interscience. wiley.com ). DOI 10.1002/eej.20800     

一种图像确认目标的多目标跟踪方法

为了解决回波的复杂性和不确定性导致雷达多目标跟踪精度降低的问题,给出一种图像确认的多目标跟踪方法。该方法通过序列图像获取目标部分相关参数,从而剔除与参数不相符的虚假回波和杂波;然后根据保留的目标回波更新所跟踪目标的测量新计算,从而减少了多目标跟踪中由无关回波和杂波干扰所造成的测量更新误差,提高了目标跟踪的精度。仿真实验结果表明,本算法在去除部分或全部杂波后,能够减小多目标跟踪的误差,使跟踪精度得到较大改善。     

Fault‐tolerant control using command‐filtered adaptive back‐stepping technique: Application to hypersonic longitudinal flight dynamics

In this paper, the adaptive back‐stepping controller is investigated for a class of strict‐feedback systems using the command filter technique. Adaptive laws are designed for updating the controller parameters when both the plant parameters and actuator‐failure parameters are unknown. Furthermore, the auxiliary dynamics is developed to deal with the input constraints. Closed‐loop stability and asymptotic‐state tracking are ensured. The method is applied to the longitudinal dynamics of a generic hypersonic aircraft in the presence of actuator faults and input constraints. Based on the parameter estimation, the command‐filtered adaptive back‐stepping control is presented. Simulation results on the control‐oriented model show that the proposed approach achieves good tracking performance. Copyright © 2015 John Wiley & Sons, Ltd.     

利用跟踪微分器提高独立电源系统有源滤波器滤波性能的研究

利用跟踪微分器,对谐波补偿信号进行相位超前校正,进而补偿谐波提取算法以及系统引起的谐波信号相位滞后,实现有源滤波器的零相位差滤波,最终提高其谐波滤除性能。通过比较普通微分器与跟踪微分器,选择合适的参数使跟踪微分器满足有源滤波器谐波信号超前校正的需求。在原理分析的基础上,利用原始数据对有源滤波器谐波信号提取以及相位超前校正进行了仿真研究。最后设计并实现了基于跟踪微分器的独立电网有源滤波器控制系统,和原有的系统进行比较,验证了跟踪微分器在提高有源滤波器谐波滤除性能方面的作用。     

Multiple visual targets tracking via probability hypothesis density filter and feature measurement

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multiple‐targets filter based on random finite sets (RFS). It propagates the posterior intensity of the random sets of targets. In this paper, we apply the PHD filter to track a random number of moving targets in visual sequences. The PHD filter is implemented using a Gaussian mixture. Obtaining the PHD only for one visual frame at a time remains a challenge. To meet this challenge, we propose a method to approximate the posterior intensity using feature measurement. To improve the representability of tracking target, we adopt an adaptive weight to fuse the color and local binary pattern (LBP) features which are extracted by the Monte Carlo method. Experimental results demonstrate the effectiveness of our method. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

An adaptive non-linear estimate for radar target tracking

The detection of targets with a radar system is inherently a non-linear stochastic process. Thus the application of non-linear estimation becomes inevitable. A review of four approaches—the extended Kalman filter (EKF), the Gaussian second-order filter (GSF), the iterative filter (IF) and the adaptive filter (AF)—is given in this paper. The superiority of AF in tracking manoeuvring targets is quite clear in comparison with the other filters.     

基于BP神经网络递推积分PI-重复控制在微电网APF中的研究

为改善有源滤波器(APF)在微电网运行中对谐波起到抑制作用及补偿电能质量的能力,提出了一种适用于微电网有源电力滤波器中的BP神经网络递推积分PI-重复控制策略。结合并联型APF拓扑结构,在传统PI控制算法基础之上引进BP神经网络算法及递推积分函数,根据跟踪误差变化趋向将PI控制参数进行实时、快速整定,进而满足最优化要求。并与重复控制进行并联,提高跟踪稳态误差能力,保证系统运行的稳定性。建立并联型APF仿真模型和实验装置,通过仿真对比验证了所提出的控制策略能够很好地提高响应速度和补偿精度,提高了有源电力滤波器的鲁棒性。     

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.     

Performance enhancement of two-degree-of-freedom controllers via adaptive techniques

This paper generalizes earlier results on adaptive disturbance estimate (innovations) feedback regulation to the case of adaptive tracking, refining some of the techniques and formulations in the process. The adaptations proposed search within the class of all stabilizing two-degree-of-freedom controllers for a nominal plant and minimize a reference signal/disturbance rejection measure. The emphasis in the paper is on issues relevant to the tracking task and simulation results to demonstrate the effectiveness of the approach. The adaptive scheme is based on results concerning the convenient characterization of the class of all stabilizing two-degree-of-freedom controllers in terms of an arbitrary filter Q with a stable proper transfer function matrix Q(s). The fact that the closed-loop transfer matrices are affine in Q(s) permits a straightforward on-line least-squares update of the parameters of the filter Q on-line. The theory for the class of stabilizing controllers is used to set up appropriate signal preprocessing. The direct adaptive schemes proposed turn out to be recursive prediction error schemes which have local convergence properties. When convergent, they ensure enhancement of the performance of a fixed controller in other than the nominal plant case.     

Output‐feedback co‐ordinated decentralized adaptive tracking: The case of MIMO subsystems with delayed interconnections

Exact decentralized output‐feedback Lyapunov‐based designs of direct model reference adaptive control (MRAC) for linear interconnected delay systems with MIMO subsystems are introduced. The design process uses a co‐ordinated decentralized structure of adaptive control with reference model co‐ordination which requires an exchange of signals between the different reference models. It is shown that in the framework of the reference model co‐ordination zero residual tracking error is possible, exactly as in the case with SISO subsystems. We develop decentralized MRAC on the base of a priori information about only the local subsystems gain frequency matrices without additional a priori knowledge about the full system gain frequency matrix. To achieve a better adaptation performance we propose proportional, integral time‐delayed adaptation laws. The appropriate Lyapunov–Krasovskii type functional is suggested to design the update mechanism for the controller parameters, and in order to prove stability. Two different adaptive DMRAC schemes are proposed, being the first asymptotic exact zero tracking results for linear interconnected delay systems with MIMO subsystems. Copyright © 2005 John Wiley & Sons, Ltd.     

Nonlinear system modeling and identification using Volterra‐PARAFAC models

Discrete‐time Volterra models are widely used in various application areas. Their usefulness is mainly because of their ability to approximate to an arbitrary precision any fading memory nonlinear system and to their property of linearity with respect to parameters, the kernels coefficients. The main drawback of these models is their parametric complexity implying the need to estimate a huge number of parameters. Considering Volterra kernels of order higher than two as symmetric tensors, we use a parallel factor (PARAFAC) decomposition of the kernels to derive Volterra‐PARAFAC models that induce a substantial parametric complexity reduction. We show that these models are equivalent to a set of Wiener models in parallel. We also show that Volterra kernel expansions onto orthonormal basis functions (OBF) can be viewed as Tucker models that we shall call Volterra‐OBF‐Tucker models. Finally, we propose three adaptive algorithms for identifying Volterra‐PARAFAC models when input–output signals are complex‐valued: the extended complex Kalman filter, the complex least mean square (CLMS) algorithm and the normalized CLMS algorithm. Some simulation results illustrate the effectiveness of the proposed identification methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Composite learning fuzzy synchronization for incommensurate fractional‐order chaotic systems with time‐varying delays

This paper presents a composite learning fuzzy control to synchronize two different uncertain incommensurate fractional‐order time‐varying delayed chaotic systems with unknown external disturbances and mismatched parametric uncertainties via the Takagi‐Sugeno fuzzy method. An adaptive controller together with fractional‐order composite learning laws is designed based on both a parallel distributed compensation technology and a fractional Lyapunov criterion. The boundedness of all variables in the closed‐loop system and the Mittag‐Leffler stability of tracking error can be guaranteed. T‐S fuzzy systems are provided to tackle unknown nonlinear functions. The distinctive features of the proposed approach consist in the following: (1) a supervisory control law is designed to compensate the lumped disturbances; (2) both the prediction error and the tracking error are used to estimate the unknown fuzzy system parameters; (3) parameter convergence can be ensured by an interval excitation condition. Finally, the feasibility of the proposed control strategy is demonstrated throughout an illustrative example.     

System identification using discrete orthogonal functions

In this paper a novel method is described for the design of adaptive IIR filters used in system identification. the adaptive filter is implemented as a parallel connection of subsections whose transfer functions constitute a set of discrete orthogonal systems. the adaptation algorithm used, which is of the Gauss-Newton type, adjusts the parameters of these discrete orthogonal systems in order to match the desired output data of the unknown plant in a least squares sense. Owing to the orthogonality property, which ensures complete independence of subsequent sections, convergence is very rapid. Closed-form expressions for the gradient signals required to update the filter are given. Illustrative examples have shown that this method always results in much improved adaptation properties compared with the already existing approaches.     

Image denoising using pulse coupled neural network with an adaptive Pareto genetic algorithm

Recently, pulse coupled neural network (PCNN) attracts much attention in image denoising as a nonlinear filtering technique. The PCNN‐based anisotropic diffusion (PCNN‐AD) method has been proposed previously for flicker noise reduction and its effectiveness has been demonstrated. Using the visual characteristics of PCNN, PCNN‐AD has also solved the problem of AD that AD is not able to suppress the isolate noise. However, there are still two drawbacks in PCNN‐AD, that is, time consuming and PCNN parameters' estimation. In order to improve the efficiency and the denoising performance of PCNN‐AD, a PCNN‐based method with an adaptive Pareto genetic algorithm (GA‐PCNN) has been proposed to restrain from additive white Gaussian noise (AWGN) in this paper. GA‐PCNN firstly integrates the PCNN and AD as a parallel system, then, optimizes the parameters of a simplified PCNN by the adaptive Pareto GA. Experimental results indicate that GA‐PCNN has better performances than the previous denoising techniques, i.e. median filter, Wiener filter, AD filter, and PCNN‐AD. The effectiveness of GA‐PCNN on AWGN reduction and edge preservation are shown finally. The results will also contribute to denoising in CMOS image sensors in the future. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Distributed estimation based on information‐based covariance intersection algorithms

A distributed estimation approach is developed in this paper using information matrix filter on a distributed tracking system in which multiple sensors are tracking the same target. The information matrix filter version is derived from covariance intersection, weighted covariance and Kalman‐like particle filter, respectively. The steady performance of these filters is evaluated with different feedback strategies. The developed filters are then validated on an industrial utility boiler. Copyright © 2015 John Wiley & Sons, Ltd.     

基于强跟踪滤波器的自适应励磁控制器

该文引入强跟踪滤波器用于发电机励磁控制,建立了一种新的自适应励磁控制器.该控制器根据发电机运行状态由强跟踪滤波器实时估计系统的扰动信息,准确地获得单机无穷大模型中的系统参数,以调整控制器,使之得以适应系统运行点的变化以及系统遭受的干扰.仿真研究表明,文中所建立的自适应励磁控制器可以有效地提高电力系统的稳定性,在系统遭受不同干扰下均表现出良好的动、静态性能.