Kalman Filter (kalman + filter)

Distribution by Scientific Domains
Distribution within Engineering

Kinds of Kalman Filter

  • extended kalman filter


  • Selected Abstracts


    Speed Estimation from Single Loop Data Using an Unscented Particle Filter

    COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 7 2010
    Zhirui Ye
    The Kalman filters used in past speed estimation studies employ a Gaussian assumption that is hardly satisfied. The hybrid method that combines a parametric filter (Unscented Kalman Filter) and a nonparametric filter (Particle Filter) is thus proposed to overcome the limitations of the existing methods. To illustrate the advantage of the proposed approach, two data sets collected from field detectors along with a simulated data set are utilized for performance evaluation and comparison with the Extended Kalman Filter and the Unscented Kalman Filter. It is found that the proposed method outperforms the evaluated Kalman filter methods. The UPF method produces accurate speed estimation even for congested flow conditions in which many other methods have significant accuracy problems. [source]


    Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition

    COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2007
    Yuanchang Xie
    Short-term traffic volume data are often corrupted by local noises, which may significantly affect the prediction accuracy of short-term traffic volumes. Discrete wavelet decomposition analysis is used to divide the original data into several approximate and detailed data such that the Kalman filter model can then be applied to the denoised data and the prediction accuracy can be improved. Two types of wavelet Kalman filter models based on Daubechies 4 and Haar mother wavelets are investigated. Traffic volume data collected from four different locations are used for comparison in this study. The test results show that both proposed wavelet Kalman filter models outperform the direct Kalman filter model in terms of mean absolute percentage error and root mean square error. [source]


    Political and Regulatory Risk in Water Utilities: Beta Sensitivity in the United Kingdom

    JOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 7-8 2001
    Roger Buckland
    UK utilities are generally regulated by the periodic setting of a price cap (the RPI-X mechanism). To establish these caps, regulators must determine what returns are appropriate on the capital employed by utilities. This paper addresses the issue of the level of risk inherent in investment in the equity of regulated water utilities in the UK. It uses the techniques of the Kalman Filter to estimate daily betas for the major utilities in the period from privatisation to mid-1999. The paper demonstrates that water utilities' risk is time-variant. It demonstrates, also, that there have been significant political and regulatory influences in the systematic risk faced by water utility shareholders. It finds beta to display little evidence of cyclical variation across the regulatory review cycle. The paper also confirms that significant excess returns have been generated over the history of the privatised water sector and suggests that over-estimation of systematic risk faced by investors in the sector may imply further excess returns in the next regulatory review period. [source]


    2D map-building and localization in outdoor environments

    JOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 1 2005
    R. Madhavan
    Determining the pose (position and orientation) of a vehicle at any time is termed localization and is of paramount importance in achieving reliable and robust autonomous navigation. Knowing the pose it is possible to achieve high level tasks such as path planning. A new map-based algorithm for the localization of vehicles operating in harsh outdoor environments is presented in this article. A map building algorithm using observations from a scanning laser rangefinder is developed for building a polyline map that adequately captures the geometry of the environment. Using this map, the Iterative Closest Point (ICP) algorithm is employed for matching laser range images from the rangefinder to the polyline map. Once correspondences are established, an Extended Kalman Filter (EKF) algorithm provides reliable vehicle state estimates using a nonlinear observation model based on the vertices of the polyline map. Data gathered during field trials in an outdoor environment is used to test the efficiency of the proposed ICP-EKF algorithm in achieving the localization of a four-wheel drive (4WD) vehicle. © 2005 Wiley Periodicals, Inc. [source]


    Robust Navigation and Mapping Architecture for Large Environments

    JOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 10 2003
    Favio Masson
    This paper addresses the problem of Simultaneous Localization and Mapping (SLAM) for very large environments. A hybrid architecture is presented that makes use of the Extended Kalman Filter to perform SLAM in a very efficient form and a Monte Carlo localizer to resolve data association problems potentially present when returning to a known location after a large exploration period. Algorithms to improve the convergence of the Monte Carlo filter are presented that consider vehicle and sensor uncertainty. The proposed algorithm incorporates significant integrity to the standard SLAM algorithms by providing the ability to handle multimodal distributions over robot pose in real time during the re-localization process. Experimental results in outdoor environments are presented to demonstrate the performance of the algorithm proposed. © 2003 Wiley Periodicals, Inc. [source]


    Assimilation of satellite-derived soil moisture from ASCAT in a limited-area NWP model

    THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 648 2010
    Jean-François Mahfouf
    Abstract A simplified Extended Kalman Filter is developed for the assimilation of satellite-derived surface soil moisture from the Advanced Scatterometer (ASCAT) instrument (on board the polar-orbiting satellite METOP) in a limited-area NWP model where soil water vertical transfers are described by a force,restore method. An analytic formulation of the land surface scheme Jacobians is derived to simplify the coupling between land surface and atmospheric data assimilation systems. Various steps necessary before the assimilation of ASCAT products are defined: projection of satellite data on the model grid, screening based on various criteria, bias correction using a CDF matching technique, and specification of model and observation errors. Three-dimensional variational data assimilation experiments are then performed during a four-week period in May 2009 over western Europe. A control assimilation is also run where the soil moisture evolves freely. Forecasts from these analyses show that the assimilation of ASCAT data slightly reduces the daytime low-level relative humidity positive bias of the control run. Forecast skill scores with respect to other variables are rather neutral. A comparison of the control run with the operational system where soil moisture is corrected from short-range forecast errors of screen-level observations show similar improvements but are more pronounced. These differences come from the fact that the number of screen-level observations from the surface network over Europe is significantly larger than those provided by a polar-orbiting satellite. These results are consistent with those obtained at ECMWF using soil moisture products derived from other satellite instruments (X-band radiometer TMI and C-band scatterometer ERS). Several avenues for improving this preliminary methodology are proposed. Copyright © 2010 Royal Meteorological Society [source]


    Issues in targeted observing

    THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 613 2005
    (Invited paper for the Q. J. R. Meteorol.
    Abstract This paper summarizes successes and limitations of targeted observing field programmes starting from the Fronts and Atlantic Storm-Track Experiment in 1997 through recent programmes targeting winter storms and tropical cyclones. These field programmes have produced average reductions in short-range forecast errors of about 10 per cent over regional verification areas, and maximum forecast error reductions as large as 50 per cent in certain cases. The majority of targeting cases investigated so far involve sets of dropsondes and other observation data that provide partial coverage of target areas. The primary scientific challenges for targeting include the refinement of objective methods that can identify optimal times and locations for targeted observations, as well as identify the specific types of satellite and in situ measurements that are required for the improvement of numerical weather forecasts. The most advanced targeting procedures, at present, include: the ensemble transform Kalman Filter, Hessian singular vectors, and observation-space targeting using the adjoint of a variational data assimilation procedure. Targeted observing remains an active research topic in numerical weather prediction, with plans for continued refinement of objective targeting procedures, and field tests of new satellite and in situ observing systems. Copyright © 2005 Royal Meteorological Society [source]


    Tropical Pacific Ocean model error covariances from Monte Carlo simulations

    THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 613 2005
    O. Alves
    Abstract As a first step towards the development of an Ensemble Kalman Filter (EnKF) for ocean data assimilation in the tropical oceans, this article investigates a novel technique for explicitly perturbing the model error in Monte Carlo simulations. The perturbation technique involves perturbing the surface zonal stress. Estimates of the characteristics of the wind stress errors were obtained from the difference between zonal wind fields from the NCEP and ECMWF re-analyses. In order to create random zonal wind stress perturbations, an EOF analysis was performed on the intraseasonally time-filtered difference between the two re-analysis products. The first 50 EOFs were retained and random wind stress fields for each ensemble member were created by combining random amounts of each EOF. Ensemble runs were performed using a shallow-water model, with both short forecasts and long simulations. Results show covariance patterns characteristic of Kelvin wave and Rossby wave dynamics. There are interesting differences between covariances using short forecasts and those using long simulations. The use of the long simulations produced non-local covariances (e.g. negative covariances between east and west Pacific), whereas short forecasts produced covariances that were localized by the time it takes Kevin and Rossby waves to travel over the forecast period and the scales of spatial covariance in the wind stress errors. The ensembles of short forecasts produced covariances and cross-covariances that can be explained by the dynamics of equatorial Rossby and Kevin waves forced by wind stress errors. The results suggest that the ensemble generation technique to explicitly represent the model error term can be used in an EnKF. Copyright © 2005 Royal Meteorological Society [source]


    Wavelets in state space models

    APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2003
    Eliana Zandonade
    Abstract In this paper, we consider the utilization of wavelets in conjunction with state space models. Specifically, the parameters in the system matrix are expanded in wavelet series and estimated via the Kalman Filter and the EM algorithm. In particular this approach is used for switching models. Two applications are given, one to the problem of detecting the paths of targets using an array of sensors, and the other to a series of daily spreads between two Brazilian bonds. Copyright © 2003 John Wiley & Sons, Ltd. [source]


    Modelling the Equity Beta Risk of Australian Financial Sector Companies

    AUSTRALIAN ECONOMIC PAPERS, Issue 3 2000
    Frida Lie
    In this paper we apply the generalised auto-regressive conditional heteroskedasticity (GARCH) and Kalman Filter approaches to modelling the equity beta risk of a sample of fifteen Australian financial sector companies. A de-regulated environment in which strong competitive forces are at play typifies the period of investigation. Consistent with the existing literature, we find that these modelling techniques perform well and, in particular, that the Kalman Filter approach is preferred. Further, we find that considerable variability of risk occurs throughout the sample period. Thus, extending the evidence of Harper and Scheit (1992); Brooks and Faff (1995) and Brooks, Faff and McKenzie (1997), we find evidence consistent with the hypothesis that deregulation has impacted the risk of banking sector stocks. [source]


    The Kalman filter for the pedologist's tool kit

    EUROPEAN JOURNAL OF SOIL SCIENCE, Issue 6 2006
    R. Webster
    Summary The Kalman filter is a tool designed primarily to estimate the values of the ,state' of a dynamic system in time. There are two main equations. These are the state equation, which describes the behaviour of the state over time, and the measurement equation, which describes at what times and in what manner the state is observed. For the discrete Kalman filter, discussed in this paper, the state equation is a stochastic difference equation that incorporates a random component for noise in the system and that may include external forcing. The measurement equation is defined such that it can handle indirect measurements, gaps in the sequence of measurements and measurement errors. The Kalman filter operates recursively to predict forwards one step at a time the state of the system from the previously predicted state and the next measurement. Its predictions are optimal in the sense that they have minimum variance among all unbiased predictors, and in this respect the filter behaves like kriging. The equations can also be applied in reverse order to estimate the state variable at all time points from a complete series of measurements, including past, present and future measurements. This process is known as smoothing. This paper describes the ,predictor,corrector' algorithm for the Kalman filter and smoother with all the equations in full, and it illustrates the method with examples on the dynamics of groundwater level in the soil. The height of the water table at any one time depends partly on the height at previous times and partly on the precipitation excess. Measurements of the height of water table and their errors are incorporated into the measurement equation to improve prediction. Results show how diminishing the measurement error increases the accuracy of the predictions, and estimates achieved with the Kalman smoother are even more accurate. Le filtre de Kalman comme outil pour le pédologue Résumé Le filtre de Kalman est un outil conçu essentiellement pour estimer les valeurs de l'état d'un système dynamique dans le temps. Il comprend deux équations principales. Celles-ci sont l'équation d'état, qui décrit l'évolution de l'état pendant le temps, et l'équation de mesure qui decrit à quel instants et de quelle façon on observe l'état. Pour le filtre discret de Kalman, décrit dans cet article, l'équation d'état est une équation stochastique différentielle qui comprend une composante aléatoire pour le bruit dans le système et qui peut inclure une force extérieure. On définit l'équation de mesure de façon à ce qu'elle puisse traiter des mesures indirectes, des vides dans des séquences de mesures et des erreurs de mesure. Le filtre de Kalman fonctionne récursivement pour prédire en avance une démarche à temps l'état du système de la démarche prédite antérieure plus l'observation prochaine. Ses prédictions sont optimales dans le sens qu'elles minimisent la variance parmi toutes les prédictions non-biasées, et à cet égard le filtre se comporte comme le krigeage. On peut appliquer, aussi, les équations dans l'ordre inverse pour estimer la variable d'état à toutes pointes à toutes les instants d'une série complète d'observations, y compris les observations du passé, du présent et du futur. Ce processus est connu comme ,smoothing'. Cet article décrit l'algorithme ,predictor,corrector' du filtre de Kalman et le ,smoother' avec toutes les équations entières. Il illustre cette méthode avec des exemples de la dynamique du niveau de la nappe phréatique dans le sol. Le niveau de la nappe à un instant particulier dépend en partie du niveau aux instants précédents et en partie de l'excès de la précipitation. L'équation d'état fournit la relation générale entre les deux variables et les prédictions. On incorpore les mesures du niveau de la nappe et leurs erreurs pour améliorer les prédictions. Les résultats mettent en évidence que lorsqu'on diminue l'erreur de mesure la précision des prédictions augmente, et aussi que les estimations avec le ,smoother' de Kalman sont encore plus précises. [source]


    Automatic tuning of L2 -SVM parameters employing the extended Kalman filter

    EXPERT SYSTEMS, Issue 2 2009
    Tingting Mu
    Abstract: We show that tuning of multiple parameters for a 2-norm support vector machine (L2 -SVM) could be viewed as an identification problem of a nonlinear dynamic system. Benefiting from the reachable smooth nonlinearity of an L2 -SVM, we propose to employ the extended Kalman filter to tune the kernel and regularization parameters automatically for the L2 -SVM. The proposed method is validated using three public benchmark data sets and compared with the gradient descent approach as well as the genetic algorithm in measures of classification accuracy and computing time. Experimental results demonstrate the effectiveness of the proposed method in higher classification accuracies, faster training speed and less sensitivity to the initial settings. [source]


    Are stock assessment methods too complicated?

    FISH AND FISHERIES, Issue 3 2004
    A J R Cotter
    Abstract This critical review argues that several methods for the estimation and prediction of numbers-at-age, fishing mortality coefficients F, and recruitment for a stock of fish are too hard to explain to customers (the fishing industry, managers, etc.) and do not pay enough attention to weaknesses in the supporting data, assumptions and theory. The review is linked to North Sea demersal stocks. First, weaknesses in the various types of data used in North Sea assessments are summarized, i.e. total landings, discards, commercial and research vessel abundance indices, age-length keys and natural mortality (M). A list of features that an ideal assessment should have is put forward as a basis for comparing different methods. The importance of independence and weighting when combining different types of data in an assessment is stressed. Assessment methods considered are Virtual Population Analysis, ad hoc tuning, extended survivors analysis (XSA), year-class curves, catch-at-age modelling, and state-space models fitted by Kalman filter or Bayesian methods. Year-class curves (not to be confused with ,catch-curves') are the favoured method because of their applicability to data sets separately, their visual appeal, simple statistical basis, minimal assumptions, the availability of confidence limits, and the ease with which estimates can be combined from different data sets after separate analyses. They do not estimate absolute stock numbers or F but neither do other methods unless M is accurately known, as is seldom true. [source]


    Some Thoughts on Monetary Targeting vs.

    GERMAN ECONOMIC REVIEW, Issue 3 2001
    Inflation Targeting
    We offer some empirical evidence on the likely scale of control and indicator problems surrounding alternative monetary targets and a direct inflation target. The links between monetary policy actions and inflation are estimated in dynamic linear models using the Kalman filter. We compare alternative intermediate-target and final-target monetary strategies using German data from the end of the Bretton Woods system until 1997. The estimation results show that broad money dominates narrow money as an intermediate target, while control problems involved in targeting broad money are larger than for direct inflation targets. [source]


    Parameter identification for leaky aquifers using global optimization methods

    HYDROLOGICAL PROCESSES, Issue 7 2007
    Hund-Der Yeh
    Abstract In the past, graphical or computer methods were usually employed to determine the aquifer parameters of the observed data obtained from field pumping tests. Since we employed the computer methods to determine the aquifer parameters, an analytical aquifer model was required to estimate the predicted drawdown. Following this, the gradient-type approach was used to solve the nonlinear least-squares equations to obtain the aquifer parameters. This paper proposes a novel approach based on a drawdown model and a global optimization method of simulated annealing (SA) or a genetic algorithm (GA) to determine the best-fit aquifer parameters for leaky aquifer systems. The aquifer parameters obtained from SA and the GA almost agree with those obtained from the extended Kalman filter and gradient-type method. Moreover, all results indicate that the SA and GA are robust and yield consistent results when dealing with the parameter identification problems. Copyright © 2006 John Wiley & Sons, Ltd. [source]


    Kalman filter finite element method applied to dynamic ground motion

    INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, Issue 9 2009
    Yusuke Kato
    Abstract The purpose of this paper is to investigate the estimation of dynamic elastic behavior of the ground using the Kalman filter finite element method. In the present paper, as the state equation, the balance of stress equation, the strain,displacement equation and the stress,strain equation are used. For temporal discretization, the Newmark ¼ method is employed, and for the spatial discretization the Galerkin method is applied. The Kalman filter finite element method is a combination of the Kalman filter and the finite element method. The present method is adaptable to estimations not only in time but also in space, as we have confirmed by its application to the Futatsuishi quarry site. The input data are the measured velocity, acceleration, etc., which may include mechanical noise. It has been shown in numerical studies that the estimated velocity, acceleration, etc., at any other spatial and temporal point can be obtained by removing the noise included in the observation. Copyright © 2008 John Wiley & Sons, Ltd. [source]


    A computer method based on simulated annealing to identify aquifer parameters using pumping-test data

    INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, Issue 3 2008
    Yen-Chen Huang
    Abstract Conventional graphical or computer methods for identifying aquifer parameters have their own inevitable limitations. This paper proposes a computer method based on a drawdown model and a heuristic approach of simulated annealing (SA) to determine the best-fit aquifer parameters of the confined and unconfined aquifer systems. The drawdown model for the confined aquifer is the Theis solution and the unconfined aquifer is the Neuman solution. The estimated results of proposed method have better accuracy than those of the graphical methods and agree well with those of the computer methods based on the extended Kalman filter and Newton's method. Finally, the sensitivity analyses for the control parameters of SA indicate that the proposed method is very robust and stable in parameter identification procedures. Copyright © 2007 John Wiley & Sons, Ltd. [source]


    The variational Kalman filter and an efficient implementation using limited memory BFGS

    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 3 2010
    H. Auvinen
    Abstract In the field of state space estimation and data assimilation, the Kalman filter (KF) and the extended Kalman filter (EKF) are among the most reliable methods used. However, KF and EKF require the storage of, and operations with, matrices of size n×n, where n is the size of the state space. Furthermore, both methods include inversion operations for m×m matrices, where m is the size of the observation space. Thus, KF methods become impractical as the dimension of the system increases. In this paper, we introduce a variational Kalman filter (VKF) method to provide a low storage, and computationally efficient, approximation of the KF and EKF methods. Furthermore, we introduce a variational Kalman smoother (VKS) method to approximate the fixed-lag Kalman smoother (FLKS) method. Instead of using the KF formulae, we solve the underlying maximum a posteriori optimization problem using the limited memory Broyden,Fletcher,Goldfarb,Shanno (LBFGS) method. Moreover, the LBFGS optimization method is used to obtain a low storage approximation of state estimate covariances and prediction error covariances. A detailed description of the VKF and VKS methods with LBFGS is given. The methodology is tested on linear and nonlinear test examples. The simulated results of the VKF method are presented and compared with KF and EKF, respectively. The convergence of BFGS/LBFGS methods is tested and demonstrated numerically. Copyright © 2009 John Wiley & Sons, Ltd. [source]


    Prior knowledge processing for initial state of Kalman filter

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 3 2010
    E. Suzdaleva
    Abstract The paper deals with a specification of the prior distribution of the initial state for Kalman filter. The subjective prior knowledge, used in state estimation, can be highly uncertain. In practice, incorporation of prior knowledge contributes to a good start of the filter. The present paper proposes a methodology for selection of the initial state distribution, which enables eliciting of prior knowledge from the available expert information. The proposed methodology is based on the use of the conjugate prior distribution for models belonging to the exponential family. The normal state-space model is used for demonstrating the methodology. The paper covers processing of the prior knowledge for state estimation, available in the form of simulated data. Practical experiments demonstrate the processing of prior knowledge from the urban traffic control area, which is the main application of the research. Copyright © 2009 John Wiley & Sons, Ltd. [source]


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

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2008
    Shu-Li Sun
    Abstract 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. [source]


    The stability analysis of the adaptive two-stage Kalman filter

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2007
    Kwang Hoon Kim
    Abstract Recently, the adaptive two-stage Kalman filter, which can track unknown random bias, was proposed. This filter can be used for systems with unknown random bias on the assumption that the stochastic information of a random bias is incomplete. This paper analyses the stability of the adaptive two-stage Kalman filter. Copyright © 2007 John Wiley & Sons, Ltd. [source]


    Optimality for the linear quadratic non-Gaussian problem via the asymmetric Kalman filter

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 1 2004
    Rosario Romera
    Abstract In the linear non-Gaussian case, the classical solution of the linear quadratic Gaussian (LQG) control problem is known to provide the best solution in the class of linear transformations of the plant output if optimality refers to classical least-squares minimization criteria. In this paper, the adaptive linear quadratic control problem is solved with optimality based on asymmetric least-squares approach, which includes least-squares criteria as a special case. Our main result gives explicit solutions for this optimal quadratic control problem for partially observable dynamic linear systems with asymmetric observation errors. The main difficulty is to find the optimal state estimate. For this purpose, an asymmetric version of the Kalman filter based on asymmetric least-squares estimation is used. We illustrate the applicability of our approach with numerical results. Copyright © 2004 John Wiley & Sons, Ltd. [source]


    Exponential convergence of the Kalman filter based parameter estimation algorithm

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2003
    Liyu Cao
    Abstract In this paper we shall present a new method to analyse the convergence property of the Kalman filter based parameter estimation algorithms. This method for convergence analysis is mainly based on some matrix inequalities and is more simple than some of the existing approaches in the literature. This method can simultaneously provide both lower and upper bounds on the exponential convergence rate as the functions of bounds of the related matrices, such as the covariance matrices. A simulation example is provided to illustrate the convergence property of the Kalman filter based algorithms. Copyright © 2003 John Wiley & Sons, Ltd. [source]


    Sliding,window neural state estimation in a power plant heater line

    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 8 2001
    A. Alessandri
    Abstract The state estimation problem for a section of a real power plant is addressed by means of a recently proposed sliding-window neural state estimator. The complexity and the nonlinearity of the considered application prevent us from successfully using standard techniques as Kalman filtering. The statistics of the distribution of the initial state and of noises are assumed to be unknown and the estimator is designed by minimizing a given generalized least-squares cost function. The following approximations are enforced: (i) the state estimator is a finite-memory one, (ii) the estimation functions are given fixed structures in which a certain number of parameters have to be optimized (multilayer feedforward neural networks are chosen from among various possible nonlinear approximators), (iii) the algorithms for optimizing the parameters (i.e., the network weights) rely on a stochastic approximation. Extensive simulation results on a complex model of a part of a real power plant are reported to compare the behaviour of the proposed estimator with the extended Kalman filter. Copyright © 2001 John Wiley & Sons, Ltd. [source]


    Kalman filter-based channel estimation and ICI suppression for high-mobility OFDM systems

    INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 10 2008
    Prerana Gupta
    Abstract The use of orthogonal frequency division multiplexing (OFDM) in frequency-selective fading environments has been well explored. However, OFDM is more prone to time-selective fading compared with single-carrier systems. Rapid time variations destroy the subcarrier orthogonality and introduce inter-carrier interference (ICI). Besides this, obtaining reliable channel estimates for receiver equalization is a non-trivial task in rapidly fading systems. Our work addresses the problem of channel estimation and ICI suppression by viewing the system as a state-space model. The Kalman filter is employed to estimate the channel; this is followed by a time-domain ICI mitigation filter that maximizes the signal-to-interference plus noise ratio (SINR) at the receiver. This method is seen to provide good estimation performance apart from significant SINR gain with low training overhead. Suitable bounds on the performance of the system are described; bit error rate (BER) performance over a time-invariant Rayleigh fading channel serves as the lower bound, whereas BER performance over a doubly selective system with ICI as the dominant impairment provides the upper bound. Copyright © 2008 John Wiley & Sons, Ltd. [source]


    Adaptive predistortion of COFDM signals for a mobile satellite channel

    INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 2 2003
    Nibaldo Rodriguez
    Abstract In this paper, we consider the optimization of the performance of QPSK and 16-QAM coded orthogonal frequency division multiplexing (COFDM) signals over the non-linear and mobile satellite channel. A high power amplifier and Rician flat fading channel produces non-linear and linear distortions; an adaptive predistortion technique combined with turbo codes will reduce both types of distortion. The predistorter is based on a feedforward neural network, with the coefficients being derived using an extended Kalman filter (EKF). The conventional turbo code is used to mitigate Rician flat fading distortion and Gaussian noise. The performance over a non-linear satellite channel indicates that QPSK COFDM followed by a predistorter provides a gain of about 1.7 dB at a BER of 3×10,3 when compared to QPSK COFDM without the predistortion scheme and 16-QAM COFDM provides a gain of 0.5 dB output back-off and 1.2 dB signal to noise ratio at a BER of 3×10,5 when compared with an adaptive predistorter based on the Harmmerstein model. We also investigate the influence of the guard time interval and Doppler frequency effect on the BER performance. When the guard interval increases from 0 to 0.125T samples and the normalized Doppler frequency is 0.001, there is a gain of 0.7 and 1 dB signal to noise ratio at a BER of 6×10,4 for QPSK and 16-QAM COFDM, respectively. Copyright © 2003 John Wiley & Sons, Ltd. [source]


    Tracking of multiple target types with a single neural extended Kalman filter

    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 5 2010
    Kathleen A. Kramer
    The neural extended Kalman filter is an adaptive state estimation routine that can be used in target-tracking systems to aid in the tracking through maneuvers without prior knowledge of the targets' dynamics. Within the neural extended Kalman filter, a neural network is trained using a Kalman filter training paradigm that is driven by the same residual as the state estimator. The difference between the a priori model used in the prediction steps of the estimator and the actual target dynamics is approximated. An important benefit of the technique is its versatility because little if any a priori knowledge of the target dynamics is needed. This allows the technique to be used in a generic tracking system that will encounter various classes of targets. In this paper, the neural extended Kalman filter is applied simultaneously to three separate classes of targets, each with different maneuver capabilities. The results show that the approach is well suited for use within a tracking system with multiple possible or unknown target characteristics. © 2010 Wiley Periodicals, Inc. [source]


    Flexible models with evolving structure

    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 4 2004
    Plamen P. Angelov
    A type of flexible model in the form of a neural network (NN) with evolving structure is discussed in this study. We refer to models with amorphous structure as flexible models. There is a close link between different types of flexible models: fuzzy models, fuzzy NN, and general regression models. All of them are proven universal approximators and some of them [Takagi-Sugeno fuzzy model with singleton outputs and radial-basis function] are interchangeable. The evolving NN (eNN) considered here makes use of the recently introduced on-line approach to identification of Takagi-Sugeno fuzzy models with evolving structure (eTS). Both TS and eNN differ from the other model schemes by their gradually evolving structure as opposed to the fixed structure models, in which only parameters are subject to optimization or adaptation. The learning algorithm is incremental and combines unsupervised on-line recursive clustering and supervised recursive on-line output parameter estimation. eNN has potential in modeling, control (if combined with the indirect learning mechanism), fault detection and diagnostics etc. Its computational efficiency is based on the noniterative and recursive procedure, which combines the Kalman filter with proper initializations and on-line unsupervised clustering. The eNN has been tested with data from a real air-conditioning installation. Applications to real-time adaptive nonlinear control, fault detection and diagnostics, performance analysis, time-series forecasting, knowledge extraction and accumulation, are possible directions of their use in future research. © 2004 Wiley Periodicals, Inc. [source]


    Tracking a partially occluded target with a cluster of Kalman filters

    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 6 2002
    Dae-Sik Jang
    Tracking moving objects is one of the most important techniques in motion analysis and understanding, and it has many difficult problems to solve. Estimating and identifying moving objects, when the background and moving objects vary dynamically, are especially difficult. It is possible under such a complex environment that targets might disappear totally or partially due to occlusion by other objects. The Kalman filter has been used to estimate motion information and use the information in predicting the appearance of targets in succeeding frames. In this article, we propose another version of the Kalman filter, to be called the structural Kalman filter, which can successfully accomplish its role of estimating motion information under such a deteriorating condition as occlusion. Experimental results show that the suggested approach is very effective in estimating and tracking non-rigid moving objects reliably. © 2002 Wiley Periodicals, Inc. [source]


    Kalman filter-based adaptive control for networked systems with unknown parameters and randomly missing outputs

    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 18 2009
    Y. Shi
    Abstract This paper investigates the problem of adaptive control for networked control systems with unknown model parameters and randomly missing outputs. In particular, for a system with the autoregressive model with exogenous input placed in a network environment, the randomly missing output feature is modeled as a Bernoulli process. Then, an output estimator is designed to online estimate the missing output measurements, and further a Kalman filter-based method is proposed for parameter estimation. Based on the estimated output and the available output, and the estimated model parameters, an adaptive control is designed to make the output track the desired signal. Convergence properties of the proposed algorithms are analyzed in detail. Simulation examples illustrate the effectiveness of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd. [source]