The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform

Yu Hen Hu - One of the best experts on this subject based on the ideXlab platform.

  • Sequential acoustic energy based source localization using particle filter in a distributed sensor network
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 2004
    Co-Authors: Xiaohong Sheng, Yu Hen Hu
    Abstract:

    A sequential source localization method using a particle filter is presented to estimate and track multiple-target locations. This method is designed to make use of an acoustic signal measured at multiple acoustic sensors randomly deployed in a wireless distributed sensor network. By using the particle filter, a non-Gaussian probability density function of the target locations is represented by a discrete set of "particles". The positions of these particles are propagated sequentially using known State Transition Equation, and updated using new location estimates via the observation Equation. Compared to a previously proposed maximum likelihood source localization algorithm, this new approach is computationally effective and more robust to parameter perturbation.

  • ICASSP (3) - Sequential acoustic energy based source localization using particle filter in a distributed sensor network
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 2004
    Co-Authors: Xiaohong Sheng, Yu Hen Hu
    Abstract:

    A sequential source localization method using a particle filter is presented to estimate and track multiple-target locations. This method is designed to make use of an acoustic signal measured at multiple acoustic sensors randomly deployed in a wireless distributed sensor network. By using the particle filter, a non-Gaussian probability density function of the target locations is represented by a discrete set of "particles". The positions of these particles are propagated sequentially using known State Transition Equation, and updated using new location estimates via the observation Equation. Compared to a previously proposed maximum likelihood source localization algorithm, this new approach is computationally effective and more robust to parameter perturbation.

Xuedong Wu - One of the best experts on this subject based on the ideXlab platform.

  • Multi-step prediction of chaotic time-series with intermittent failures based on the generalized nonlinear filtering methods
    Applied Mathematics and Computation, 2013
    Co-Authors: Xuedong Wu, Zhihuan Song
    Abstract:

    There are many practical situations that the chaotic signal appears in a random manner so that there are intermittent failures in the observation mechanism at certain times. These random interruptions, which are called as multiplicative noises, can be modeled by a sequence of independent Bernoulli random variables. Considering the observed chaotic signal perturbed by additive and multiplicative noises at the same time, this paper generalizes the original extended Kalman filtering (EKF), the Unscented Kalman filtering (UKF) and the Gaussian particle filtering (GPF) to the case in which there is a positive probability that the observation with intermittent failures in each time consists of additive noises alone. The shortened forms of these generalized new filtering algorithms are written as GEKF, GUKF and GGPF correspondingly. Using weights and network output of perceptron neural network to constitute State Transition Equation and observation Equation, the input vector to the network is composed of predicted chaotic signal with given length (see Section 2 for details), and the multi-step prediction results are represented by the predicted observation value of nonlinear filtering methods. To show the advantage of these generalized new filtering algorithms, we applied them to the five-step prediction of Mackey-Glass time-series and equipment's temperature (The corresponding time series can be found at http://robjhyndman.com/TSDL) with additive and multiplicative noises, respectively and compared them with the original EKF, UKF and GPF. Experimental results have demonstrated that the GEKF, GUKF and GGPF are proportionally superior to the original EKF, UKF and GPF. Moreover, GGPF is a better choice for multi-step prediction in comparison with GEKF and GUKF.

  • Online chaotic time-series' prediction using EKF, UKF and GPF
    2010 3rd International Congress on Image and Signal Processing, 2010
    Co-Authors: Xuedong Wu
    Abstract:

    This work uses the weights and network output of neural networks (NN) as State Equation and measurement Equation for chaotic time-series' prediction to obtain the linear State Transition Equation which is different from the previous filtering methods based chaotic time-series' prediction, and the prediction results of chaotic time series is represented by the predicted measurement value. An efficient algorithm with continuous update prediction scheme for chaotic time-series is suggested. This scheme is tested using simulated data based on the EKF, UKF and Gaussian particle filtering (GPF), respectively. Simulation results have proved that the GPF is superior to EKF and UKF for Mackey-Glass time-series' prediction with the proposed model proposed in this paper.

  • Generalized extended Kalman filter for prediction of chaotic time-series with intermittent failures
    2008 7th World Congress on Intelligent Control and Automation, 2008
    Co-Authors: Xuedong Wu, Jin Huang, Zhihuan Song
    Abstract:

    There are many practical situations in which the chaotic signal appears in the observation in a random manner so that there are intermittent failures in the observation mechanism at certain times. Using weights and network output of neural network as State Equation and observation Equation to obtain the linear State Transition Equation, and the chaotic time-series prediction results represented by the predicted observation value, this paper generalizes the extended Kalman filter (EKF) to the case for the prediction of chaotic time-series with intermittent observations when random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables. Finally, we test this scheme using simulated data based on the generalized EKF with different Bernoulli distribution probability for uncertain observations. Simulation results of Lorenz time-series prediction with synthetic data prove that the proposed algorithm in this paper has satisfactory prediction precision as well as good robustness.

  • Online chaotic time-series prediction with the derivative-free extended Kalman filter
    2008 7th World Congress on Intelligent Control and Automation, 2008
    Co-Authors: Xuedong Wu, Zhihuan Song
    Abstract:

    Derivative-free extended Kalman filter (DEKF) represented uncertainty by an ensemble set of State vectors rather than by the traditional mean and covariance measures, avoiding the need for the calculation of Jacobian matrices. This paper used weights and network output of multilayer perceptron as State Equation and measurement Equation to obtain the linear State Transition Equation, and the prediction results of chaotic time-series were represented by the predicted measurement value, which was different from the previous filtering methods based chaotic time-series prediction, an efficient algorithm was suggested for chaotic time-series prediction scheme. Finally, we test this scheme using simulated data based on the extended Kalman filtering (EKF) and DEKF, respectively. Simulation results of EKF and DEKF based Mackey-Glass time-series prediction with synthetic data prove that the prediction accuracy of DEKF is close to EKF as parameter alpha tends to some value, but the run time of DEKF is much longer than EKF.

Harry H. Asada - One of the best experts on this subject based on the ideXlab platform.

  • A reduced order systems approach to prediction of emergent behaviors of cellular systems
    2016 American Control Conference (ACC), 2016
    Co-Authors: Michaëlle N. Mayalu, Harry H. Asada
    Abstract:

    Populations of cells interacting within an extracellular matrix (ECM) exhibit spatiotemporal behaviors that are usually described through complex and extensive mechanisms. However, mechanistic computational models become intractable as the cell population increases. Here, we develop a reduced order agent-based framework derived from the empirical treatment of simulation data obtained from detailed single-cell mechanistic computational models. The key construct within this approach is the linearization and subsequent superposition of single-cell agent models to explain the emergent behavior among multiple cells. First, simulation data is obtained from a detailed single-cell mechanistic computational model. In order to increase linear variation, nonlinear force terms are considered within the data. Then, the technique of Partial Least Squares Regression (PLSR) is used to linearly approximate the simulation data. A linear State Transition Equation representing a single-cell agent is formulated in latent variable space using the results from the PLSR approximation. Finally, an algorithm is developed were linearized agents are superposed to predict multi-cellular interactions. Using this method, computational expense and time are decreased significantly and sufficient mechanistic detail is retained in the simulation. The method is applied to cell-ECM interactions, and ECM remodeling during cell migration.

  • Kalman filter for inhomogeneous population Markov chains with application to stochastic recruitment control of muscle actuators
    Proceedings of the 2010 American Control Conference, 2010
    Co-Authors: Lael Odhner, Harry H. Asada
    Abstract:

    A population of stochastic agents, as seen in swarm robots and some biological systems, can be modeled as a population Markov chain where the Transition probability matrix is time-varying, or inhomogeneous. This paper presents a Kalman filter approach to estimating the population State, i.e., the headcount of the number of agents in each possible agent-State. The probabilistic State Transition formalism originated in Markov chain modeling is recast as a standard State Transition Equation perturbed by an additive random process with a multinomial distribution. An optimal linear filter is derived for the recast State Equation; the resultant optimal filter is a type of Kalman filter with a modified covariance propagation law. Convergence properties are examined, and the State estimation error covariance is guaranteed to converge. The State estimation method is applied to stochastic control of muscle actuators, where individual artificial muscle fibers are stochastically recruited with probabilities broadcasted from a central controller. The system output is the resultant force generated by the population of muscle fibers, each of which takes a discrete level of output force. The linear optimal filter estimates the population State (the headcount of agents producing each level of force) from the aggregate output alone. Experimental results demonstrate that stochastic recruitment control works effectively with the linear optimal filter.

Zhihuan Song - One of the best experts on this subject based on the ideXlab platform.

  • Multi-step prediction of chaotic time-series with intermittent failures based on the generalized nonlinear filtering methods
    Applied Mathematics and Computation, 2013
    Co-Authors: Xuedong Wu, Zhihuan Song
    Abstract:

    There are many practical situations that the chaotic signal appears in a random manner so that there are intermittent failures in the observation mechanism at certain times. These random interruptions, which are called as multiplicative noises, can be modeled by a sequence of independent Bernoulli random variables. Considering the observed chaotic signal perturbed by additive and multiplicative noises at the same time, this paper generalizes the original extended Kalman filtering (EKF), the Unscented Kalman filtering (UKF) and the Gaussian particle filtering (GPF) to the case in which there is a positive probability that the observation with intermittent failures in each time consists of additive noises alone. The shortened forms of these generalized new filtering algorithms are written as GEKF, GUKF and GGPF correspondingly. Using weights and network output of perceptron neural network to constitute State Transition Equation and observation Equation, the input vector to the network is composed of predicted chaotic signal with given length (see Section 2 for details), and the multi-step prediction results are represented by the predicted observation value of nonlinear filtering methods. To show the advantage of these generalized new filtering algorithms, we applied them to the five-step prediction of Mackey-Glass time-series and equipment's temperature (The corresponding time series can be found at http://robjhyndman.com/TSDL) with additive and multiplicative noises, respectively and compared them with the original EKF, UKF and GPF. Experimental results have demonstrated that the GEKF, GUKF and GGPF are proportionally superior to the original EKF, UKF and GPF. Moreover, GGPF is a better choice for multi-step prediction in comparison with GEKF and GUKF.

  • Generalized extended Kalman filter for prediction of chaotic time-series with intermittent failures
    2008 7th World Congress on Intelligent Control and Automation, 2008
    Co-Authors: Xuedong Wu, Jin Huang, Zhihuan Song
    Abstract:

    There are many practical situations in which the chaotic signal appears in the observation in a random manner so that there are intermittent failures in the observation mechanism at certain times. Using weights and network output of neural network as State Equation and observation Equation to obtain the linear State Transition Equation, and the chaotic time-series prediction results represented by the predicted observation value, this paper generalizes the extended Kalman filter (EKF) to the case for the prediction of chaotic time-series with intermittent observations when random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables. Finally, we test this scheme using simulated data based on the generalized EKF with different Bernoulli distribution probability for uncertain observations. Simulation results of Lorenz time-series prediction with synthetic data prove that the proposed algorithm in this paper has satisfactory prediction precision as well as good robustness.

  • Online chaotic time-series prediction with the derivative-free extended Kalman filter
    2008 7th World Congress on Intelligent Control and Automation, 2008
    Co-Authors: Xuedong Wu, Zhihuan Song
    Abstract:

    Derivative-free extended Kalman filter (DEKF) represented uncertainty by an ensemble set of State vectors rather than by the traditional mean and covariance measures, avoiding the need for the calculation of Jacobian matrices. This paper used weights and network output of multilayer perceptron as State Equation and measurement Equation to obtain the linear State Transition Equation, and the prediction results of chaotic time-series were represented by the predicted measurement value, which was different from the previous filtering methods based chaotic time-series prediction, an efficient algorithm was suggested for chaotic time-series prediction scheme. Finally, we test this scheme using simulated data based on the extended Kalman filtering (EKF) and DEKF, respectively. Simulation results of EKF and DEKF based Mackey-Glass time-series prediction with synthetic data prove that the prediction accuracy of DEKF is close to EKF as parameter alpha tends to some value, but the run time of DEKF is much longer than EKF.

Sangjoo Kwon - One of the best experts on this subject based on the ideXlab platform.

  • Robust Kalman filtering with perturbation estimation process
    2006 American Control Conference, 2006
    Co-Authors: Sangjoo Kwon
    Abstract:

    An advanced Kalman filtering method is investigated by considering a perturbation estimation process in the standard Kalman filter, which reconstructs uncertainty with respect to the nominal State Transition Equation. The predictor and corrector are reformulated with the perturbation estimator, which has the intrinsic property of integrating innovations in the recursion of combined Kalman filter-perturbation estimator (CKF). The State/perturbation estimation error dynamics and the corresponding error covariance propagation Equations are derived as well. A numerical example for mobile robot localization is shown to demonstrate the effectiveness of CKF

  • IROS - Robust mobile robot localization with combined Kalman filter-perturbation estimator
    2005 IEEE RSJ International Conference on Intelligent Robots and Systems, 2005
    Co-Authors: Sangjoo Kwon, Kwangwoong Yang, Sangdeok Park, Youngsun Ryuh
    Abstract:

    In this paper, a robust localization method for mobile robot based on the combination of Kalman filter and perturbation estimator is presented. It remarkably enhances the robustness of localization performance, specifically when large odometric errors are occurred. The perturbation estimator in the combined Kalman filter (CKF) is to estimate systematic errors which perturbs the behavior of nominal State Transition Equation. Intrinsically, it has the property of integrating the innovation, i.e., the difference between measurement and predicted measurement and thus gives a chance of more reducing the gap between real States and their estimates. After formulation of the CKF recursion, we show how the design parameters can be determined and how much beneficial it is through simulation and experiment for a two-wheeled mobile robot under indoor GPS.

  • Robust mobile robot localization with combined Kalman filter-perturbation estimator
    2005 IEEE RSJ International Conference on Intelligent Robots and Systems, 2005
    Co-Authors: Sangjoo Kwon, Kwangwoong Yang, Sangdeok Park, Youngsun Ryuh
    Abstract:

    In this paper, a robust localization method for mobile robot based on the combination of Kalman filter and perturbation estimator is presented. It remarkably enhances the robustness of localization performance, specifically when large odometric errors are occurred. The perturbation estimator in the combined Kalman filter (CKF) is to estimate systematic errors which perturbs the behavior of nominal State Transition Equation. Intrinsically, it has the property of integrating the innovation, i.e., the difference between measurement and predicted measurement and thus gives a chance of more reducing the gap between real States and their estimates. After formulation of the CKF recursion, we show how the design parameters can be determined and how much beneficial it is through simulation and experiment for a two-wheeled mobile robot under indoor GPS.