FPGA‐based embedded platform for fiber optic gyroscope signal denoising

This paper presents System on Chip (SoC) implementation of a proposed denoising algorithm for fiber optic gyroscope (FOG) signal. The SoC is developed using an Auxillary Processing Unit of the proposed algorithm and implemented in the Xilinx Virtex‐5‐FXT‐1136 field programmable gate array. SoC implementation of this application is first of its kind. The proposed algorithm namely adaptive moving average‐based dual‐mode Kalman filter (AMADMKF) is a hybrid of adaptive moving average and Kalman filter (KF) technique. The performance of the proposed AMADMKF algorithm is compared with the discrete wavelet transform and KF of different gains. Allan variance analysis, standard deviation and signal to noise ratio (SNR) are used to measure the efficiency of the algorithm. The experimental result shows that AMADMKF algorithm reduces the standard deviation or drift of the signal by an order of 100 and improves the SNR approximately by 80 dB. The Allan variance analysis result shows that this algorithm also reduces different types of random errors of the signal significantly. The proposed algorithm is found to be the best suited algorithm for denoising the FOG signal in both the static and dynamic environments. Copyright © 2013 John Wiley & Sons, Ltd.     

Self‐tuning weighted fusion Kalman filter for ARMA signal with colored measurement noise and its convergence analysis

For the multisensor single‐channel autoregressive moving average (ARMA) signal with colored measurement noise, when the partial model parameters and the noise variance are unknown, a self‐tuning fusion Kalman filter weighted by scalar is presented based on the ARMA innovation model by the modern time series analysis method. With the application of the recursive instrumental variable algorithm and the Gevers–Wouters iterative algorithm with dead band, the information fusion estimators for the unknown model parameters and noise variance are obtained, and their consistence is proved by the existence and continuity theorem of implicit function. Then, substituting them into the optimal weighted fusion Kalman filter, one can obtain the corresponding self‐tuning weighted fusion Kalman filter. Further, with the application of the dynamic variance error system analysis method, the convergence of the self‐tuning Lyapunov equations for filtering error cross‐covariances is proved. With the application of the dynamic error system analysis method, it is rigorously proved that the self‐tuning weighted fusion Kalman filter converges to the optimal weighted fusion Kalman filter in a realization; that is, it has asymptotic optimality. A simulation example shows its effectiveness.Copyright © 2012 John Wiley & Sons, Ltd.     

Distributed Kalman filters with adaptive strategy for linear time‐varying interconnected systems

We consider the problem of distributed state estimation over a sensor network in which a set of nodes collaboratively estimates the state of a continuous‐time linear time‐varying system. In particular, our work focuses on the benefits of weight adaptation of the interconnection gains in distributed Kalman filters. To this end, an adaptation strategy is proposed with the adaptive laws derived via a Lyapunov‐redesign approach. The justification for the gain adaptation stems from a desire to adapt the pairwise difference of state estimates as a function of their agreement, thereby enforcing an interconnection‐dependent gain. In the proposed scheme, an adaptive gain for each pairwise difference of the interconnection terms is used in order to address edge‐dependent differences in the state estimates. Accounting for node‐specific differences, a special case of the scheme is also presented, where it uses a single adaptive gain in each node estimate and which uniformly penalizes all pairwise differences of state estimates in the interconnection term. The filter gains can be designed either by standard Kalman filter or Luenberger observer to construct the adaptive distributed Kalman filter or adaptive distributed Luenberger observer. Stability of the schemes has been shown, and it is not restricted by the graph topology and therefore the schemes are applicable to both directed and undirected graphs. The proposed algorithms offer a significant reduction in communication costs associated with information flow by the nodes. Finally, numerical studies are presented to illustrate the performance and effectiveness of the proposed adaptive distributed Kalman filters. Copyright © 2015 John Wiley & Sons, Ltd.     

Hierarchical fusion robust Kalman filter for clustering sensor network time‐varying systems with uncertain noise variances

For the clustering time‐varying sensor network systems with uncertain noise variances, according to the minimax robust estimation principle, based on the worst‐case conservative system with conservative upper bounds of noise variances, applying the optimal Kalman filtering, the two‐level hierarchical fusion time‐varying robust Kalman filter is presented, where the first‐level fusers consist of the local decentralized robust fusers for the clusters, and the second‐level fuser is a global decentralized robust fuser for the cluster heads. It can reduce the communication load and save energy resources of sensors. Its robustness is proved by the proposed Lyapunov equation method. The concept of robust accuracy is presented, and the robust accuracy relations of the local, decentralized, and centralized fused robust Kalman filters are proved. Specially, the corresponding steady‐state robust local and fused Kalman filters are also presented, and the convergence in a realization between the time‐varying and steady‐state robust Kalman filters is proved by the dynamic error system analysis method. A simulation example shows correctness and effectiveness. Copyright © 2013 John Wiley & Sons, Ltd.     

Image‐Based Crack Detection for Real Concrete Surfaces

In this paper, we introduce a novel image‐based approach to detect cracks in concrete surfaces. Crack detection is important for the inspection, diagnosis, and maintenance of concrete structures. However, conventional image‐based approaches cannot achieve precise detection since the image of the concrete surface contains various types of noise due to different causes such as concrete blebs, stain, insufficient contrast, and shading. In order to detect the cracks with high fidelity, we assume that they are composed of thin interconnected textures and propose an image‐based percolation model that extracts a continuous texture by referring to the connectivity of brightness and the shape of the percolated region, depending on the length criterion of the scalable local image processing techniques. Additionally, noise reduction based on the percolation model is proposed. We evaluated the validity of the proposed technique by using precision recall and receiver operating characteristic (ROC) analysis by means of some experiments with actual concrete surface images. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Robust time‐varying Kalman estimators for systems with packet dropouts and uncertain‐variance multiplicative and linearly correlated additive white noises

This paper is concerned with robust estimation problem for a class of time‐varying networked systems with uncertain‐variance multiplicative and linearly correlated additive white noises, and packet dropouts. By augmented state method and fictitious noise technique, the original system is converted into one with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case system with conservative upper bounds of uncertain noise variance, the robust time‐varying Kalman estimators (filter, predictor, and smoother) are presented. A unified approach of designing the robust Kalman estimators is presented based on the robust Kalman predictor. Their robustness is proved by the Lyapunov equation approach in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Their accuracy relations are proved. The corresponding robust steady‐state Kalman estimators are also presented, and the convergence in a realization between the time‐varying and steady‐state robust Kalman estimators is proved. Finally, a simulation example applied to uninterruptible power system shows the correctness and effectiveness of the proposed results.     

Threshold computation for fault detection in linear discrete‐time Markov jump systems

This paper proposes a threshold computation scheme for an observer‐based fault detection (FD) in linear discrete‐time Markovian jump systems. An observer‐based FD scheme typically consists of two stages known as residual generation and residual evaluation. Even information of faults is contained inside a residual signal, a decision of faults occurrence is consequently made by a residual evaluation stage, which consists of residual evaluation function and threshold setting. For this reason, a successful FD strongly depends on a threshold setting for a given residual evaluation function. In this paper, Kalman filter (KF) is used as a residual generator. Based on an accessibility of Markov chain to KF, two types of residual generations are considered, namely mode‐dependent and mode‐independent residual generation. After that threshold is computed in a residual evaluation stage such that a maximum fault detection rate is achieved, for a given false alarm rate. Without any knowledge of a probability density function of residual signal before and after fault occurrence, a threshold is computed by using an estimation of residual evaluation function variance in a fault‐free case. Finally, a detection performance is demonstrated by a numerical example. Copyright © 2013 John Wiley & Sons, Ltd.     

基于卡尔曼滤波的电能质量分析方法综述

首先对电能质量问题及其分析检测方法进行简单介绍;然后主要综述了常规卡尔曼滤波、扩展卡尔曼滤波和无迹卡尔曼滤波这3种卡尔曼滤波的基本原理,并对其在电能质量分析中的应用进行了系统的总结,对比分析了各种方法的利弊;最后对卡尔曼滤波方法现存的问题及今后的发展趋势进行了展望。     

Optimal and self‐tuning weighted measurement fusion Kalman filters and their asymptotic global optimality

For the multisensor linear discrete time‐invariant stochastic systems with unknown noise variances, using the correlation method, the information fusion noise variance estimators with consistency are given by taking the average of the local noise variance estimators. Substituting them into two optimal weighted measurement fusion steady‐state Kalman filters, two new self‐tuning weighted measurement fusion Kalman filters with a self‐tuning Riccati equation are presented. By the dynamic variance error system analysis (DVESA) method, it is rigorously proved that the self‐tuning Riccati equation converges to the steady‐state optimal Riccati equation. Further, by the dynamic error system analysis (DESA) method, it is proved that the steady‐state optimal and self‐tuning Kalman fusers converge to the global optimal centralized Kalman fuser, so that they have the asymptotic global optimality. Compared with the centralized Kalman fuser, they can significantly reduce the computational burden. A simulation example for the target tracking systems shows their effectiveness. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

Optimal load frequency control through combined state and control gain estimation for noisy measurements

Combined estimation of state and feed-back gain for optimal load frequency control is proposed. Load frequency control (LFC) addresses the problem of controlling system frequency in response to disturbance, and is one of main research areas in power system operation. A well acknowledged solution to this problem is feedback stabilization, where the Linear Quadratic Regulator (LQR) based controller computes the feedback gain K from the known system parameters and implements the control, assuming the availability of all the state variables. However, this approach restricts control to cases where the state variables are readily available and the system parameters are steady. Alternatively, by estimating the states continuously from available measurements of some of the states, it can accommodate dynamic changes in the system parameters. The paper proposes the technique of augmenting the state variables with controller gains. This introduces a non-linearity to the augmented system and thereby the estimation is performed using an Extended Kalman Filter. This results in producing controller gains that are capable of controlling the system in response to changes in load demand, system parameter variation and measurement noise.     

The Kalman filter in the context of adaptive filter theory

Model‐based adaptive algorithms are usually derived with the help of the Wiener‐Hopf equation based on empirical statistics. They are often interpreted as an extension to their model‐independent counterparts, i.e. the stochastic‐gradient based adaptive filters. As a consequence, it is generally not considered worthwhile to show the analogy between Kalman filters and adaptive filters. This article pursues just these two goals. First, it tries to remove the notion that the Kalman filter is a complicated and unnecessary detour from the subject of adaptive filtering. Second, the advantage of a deeper insight into adaptive algorithms from Kalman's viewpoint emerges from our treatment. Based on a time‐varying FIR filter model, the Kalman filter is completely derived and serves as a general framework for the special case of model‐based adaptive filters. Copyright © 2004 John Wiley & Sons, Ltd.     

Kalmen estimator as a robust solution for output power maximization of wave energy converter

In this paper, the application of linear quadratic Gaussian (LQG) control for a buoy‐type point absorber of a wave energy converter (PA‐WEC) system is investigated. The proposed wave energy conversion is considered as a two‐body system, which is taut‐anchored to the sea floor using three cables. The main goal of this study is to extract the maximum available power from the ocean wave. This is accomplished via determining the optimal value of the force exerted on the power take‐off (PTO) system taking in account the physical constraints on the position and velocity. First, the reduced nonlinear dynamical model of the WEC system is obtained. The nonlinearity in the mooring force is replaced by a linear law to yield the state space linear model of the system. Then, the standard Kalman filter technique is employed to estimate the full states of the system. Based on the LQG control approach, the optimal PTO force is computed at which the maximum output power can be easily harvested. The computational burden is minimized to a great extent by computing the optimal state feedback gains and the Kalman state space model offline. The feasibility of the proposed control approach in extracting the optimal power of the ocean wave is validated via the simulation example even under different values of the mooring constant and without violating the system limitation. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

电力系统动态状态估计的研究现状和展望

综述了电力系统动态状态估计DSE(Dynamic State Estimation)的研究现状,对目前常用的DSE方法作了简明对比。。描述了基于扩展卡尔曼滤波EKF(Extended Kalman Filter)算法的DSE数学模型,并介绍了3类改进算法,用以提高EKF算法的自适性性、鲁棒性和准确性。针对不良数据的检测和辨识,在简要分析传统量测量残差检测和突变检测方法优缺点的基础上,又介绍了一些新的理论。总结了外部网络模型等值的一些观点。最后,提出了DSE研究中几个方面的构想以供参考。     

Sensor fault detection and isolation for wind turbines based on subspace identification and Kalman filter techniques

This paper aims at the blade root moment sensor fault detection and isolation issue for three‐bladed wind turbines with horizontal axis. The underlying problem is crucial to the successful application of the individual pitch control system, which plays a key role for reducing the blade loads of large offshore wind turbines. In this paper, a wind turbine model is built based on the closed loop identification technique, where the wind dynamics is included. The fault detection issue is investigated based on the residuals generated by dual Kalman filters. Both additive faults and multiplicative faults are considered in this paper. For the additive fault case, the mean value change detection of the residuals and the generalized likelihood ratio test are utilized respectively. For multiplicative faults, they are handled via the variance change detection of the residuals. The fault isolation issue is proceeded with the help of dual sensor redundancy. Simulation results show that the proposed approach can be successfully applied to the underlying issue. Copyright © 2009 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.     

Fault diagnosis of a simulated industrial gas turbine via identification approach

In this paper, a model‐based procedure exploiting the analytical redundancy principle for the detection and isolation of faults on a simulated process is presented. The main point of the work consists of using an identification scheme in connection with dynamic observer and Kalman filter designs for diagnostic purpose. The errors‐in‐variables identification technique and output estimation approach for residual generation are in particular advantageous in terms of solution complexity and performance achievement. The proposed tools are analysed and tested on a single‐shaft industrial gas turbine MATLAB/SIMULINK^® simulator in the presence of disturbances, i.e. measurement errors and modelling mismatch. Selected performance criteria are used together with Monte‐Carlo simulations for robustness and performance evaluation. The suggested technique can constitute the design methodology realising a reliable approach for real application of industrial process FDI. Copyright © 2006 John Wiley & Sons, Ltd.     

三相电网信号的双重Kalman滤波测量方法

提出一种基于Kalman滤波器的三相电网信号的谐波分析方法。该算法针对正负序分量的幅值相位和基波的频率,分别建立一个Kalman滤波器,并在两个滤波器之间形成联系。前者的状态变量可以用来计算基波瞬时频率并作为后者的测量;后者的状态变量可以提供前者模型参数。给出了两个滤波器的初始化过程与检测信号突变的方法,从而加快算法的响应速度。仿真验证了三相电网信号的幅值、相位和频率发生突变,幅值和频率同时连续变化,以及处于三相不平衡条件下,双重Kalman滤波测量方法依然准确高效。实验对非理想电网的三相电压、电流信号进行谐波分析,根据分析结果重构的信号误差平均值分别仅为0.67%和1.04%。     

Discrete time filters for doubly stochastic poisson processes and other exponential noise models

The well‐known Kalman filter is the optimal filter for a linear Gaussian state‐space model. Furthermore, the Kalman filter is one of the few known finite‐dimensional filters. In search of other discrete‐time finite‐dimensional filters, this paper derives filters for general linear exponential state‐space models, of which the Kalman filter is a special case. One particularly interesting model for which a finite‐dimensional filter is found to exist is a doubly stochastic discrete‐time Poisson process whose rate evolves as the square of the state of a linear Gaussian dynamical system. Such a model has wide applications in communications systems and queueing theory. Another filter, also with applications in communications systems, is derived for estimating the arrival times of a Poisson process based on negative exponentially delayed observations. Copyright © 1999 John Wiley & Sons, Ltd.     

Predicting a moving object position for visual servoing: theory and experiments

In order to perform visual servoing tasks in a robotic system, one is confronted with the low sampling rate of standard cameras and the time delay introduced by image processing. One way to circumvent the time‐delay problem is to estimate future positions of the moving object of interest employing prediction techniques. In this work, three prediction techniques, namely Kalman filtering and two adaptive techniques employing least squares with forgetting factor and the projection algorithm, respectively, are evaluated in terms of their prediction error. Experimental results show that the adaptive techniques give best results and the Kalman filter‐based predictor shows a high sensitivity to velocity changes of the moving object. Copyright © 2001 John Wiley & Sons, Ltd.