The Experts below are selected from a list of 76497 Experts worldwide ranked by ideXlab platform
Zidong Wang - One of the best experts on this subject based on the ideXlab platform.
-
scalable distributed Filtering for a class of discrete time complex networks over time varying topology
IEEE Transactions on Neural Networks, 2019Co-Authors: Zidong Wang, Donghua ZhouAbstract:: This article is concerned with the distributed Filtering Problem for a class of discrete complex networks over time-varying topology described by a sequence of variables. In the developed scalable Filtering algorithm, only the local information and the information from the neighboring nodes are used. As such, the proposed filter can be implemented in a truly distributed manner at each node, and it is no longer necessary to have a certain center node collecting information from all the nodes. The aim of the addressed Filtering Problem is to design a time-varying filter for each node such that an upper bound of the Filtering error covariance is ensured and the desired filter gain is then calculated by minimizing the obtained upper bound. The filter is established by solving two sets of recursive matrix equations, and thus, the algorithm is suitable for online application. Sufficient conditions are provided under which the Filtering error is exponentially bounded in mean square. The monotonicity of the Filtering error with respect to the coupling strength is discussed as well. Finally, an illustrative example is presented to demonstrate the feasibility and effectiveness of our distributed Filtering strategy.
-
recursive Filtering for time varying systems with random access protocol
IEEE Transactions on Automatic Control, 2019Co-Authors: Lei Zou, Qing-long Han, Zidong Wang, Donghua ZhouAbstract:This paper is concerned with the recursive Filtering Problem for a class of networked linear time-varying systems subject to the scheduling of the random access protocol (RAP). The communication between the sensor nodes and the remote filter is implemented via a shared network. For the purpose of preventing the data from collisions, only one sensor node is allowed to get access to the network at each time instant. The transmission order of sensor nodes is orchestrated by the RAP scheduling, under which the selected nodes obtaining access to the network could be characterized by a sequence of independent and identically-distributed variables. The aim of the addressed Filtering Problem is to design a recursive filter such that the Filtering error covariance could be minimized by properly designing the filter gain at each time instant. The desired filter gain is calculated recursively by solving two Riccati-like difference equations. Furthermore, the boundedness issue of the corresponding Filtering error covariance is investigated. Sufficient conditions are obtained to ensure the lower and upper bounds of the Filtering error covariance. Two illustrative examples are given to demonstrate the correctness and effectiveness ofour developed recursive Filtering approach.
-
finite horizon h Filtering for switched time varying stochastic systems with random sensor nonlinearities and packet dropouts
Signal Processing, 2017Co-Authors: Yonggang Chen, Zidong Wang, Wei Qian, Fuad E AlsaadiAbstract:The finite-horizon H-infinity Filtering Problem is investigated under the average dwell-time switching.The switched time-varying systems are subject to state-dependent stochastic disturbances.The system outputs suffer from sensor nonlinearities and packet dropouts.A mode-dependent asynchronous time-varying filter is designed.Explicit characterization of the filter gains is presented via solving recursive linear matrix inequalities. This paper is concerned with the finite-horizon H Filtering Problem for a class of switched time-varying systems with state-dependent stochastic disturbances. The system outputs are subject to randomly occurring sensor nonlinearities and successive packet dropouts. Attention is focused on the design of a mode-dependent asynchronous time-varying filter such that the prescribed weighted H performance requirement can be achieved under the average dwell-time switching. By utilizing the piecewise function approach and stochastic analysis technique, sufficient conditions are first established to ensure the existence of the desired finite-horizon asynchronous H filter. Then, the explicit characterization of the filter gains is presented in terms of the solutions to certain recursive linear matrix inequalities. Finally, the effectiveness of the proposed Filtering scheme is illustrated via a simulation example.
-
recursive Filtering with fading measurements sensor delays and correlated noises
2015Co-Authors: Zidong Wang, Huijun GaoAbstract:In this chapter, the recursive Filtering Problem is firstly investigated for a class of discrete-time non-linear stochastic systems with random parameter matrices, multiple fading measurements, and correlated noises. The phenomenon of measurement fading occurs in a random way and the fading probability for each sensor is governed by an individual random variable obeying a certain probability distribution over the known interval. The purpose of the addressed Filtering Problem is to design an unbiased, recursive, and optimal filter in the minimum variance sense. Intensive stochastic analysis is carried out to obtain the filter gain characterized by the solution to a recursive matrix equation. Based on the proposed filter approach, the gain-constrained recursive Filtering Problem is studied for a class of non-linear time-varying stochastic systems with probabilistic sensor delays and correlated noises. A new recursive Filtering algorithm is developed that ensures both the local optimality and the unbiasedness of the designed filter at each sampling instant which achieving the prespecified filter gain constraint.
-
recursive Filtering with random parameter matrices multiple fading measurements and correlated noises
Automatica, 2013Co-Authors: Zidong Wang, Huijun GaoAbstract:This paper is concerned with the recursive Filtering Problem for a class of discrete-time nonlinear stochastic systems with random parameter matrices, multiple fading measurements and correlated noises. The phenomenon of measurement fading occurs in a random way and the fading probability for each sensor is governed by an individual random variable obeying a certain probability distribution over the known interval [@b"k,@c"k]. Such a probability distribution could be any commonly used discrete distribution over the interval [@b"k,@c"k] that covers the Bernoulli distribution as a special case. The process noise and the measurement noise are one-step autocorrelated, respectively. The process noise and the measurement noise are two-step cross-correlated. The purpose of the addressed Filtering Problem is to design an unbiased and recursive filter for the random parameter matrices, stochastic nonlinearity, and multiple fading measurements as well as correlated noises. Intensive stochastic analysis is carried out to obtain the filter gain characterized by the solution to a recursive matrix equation. The proposed scheme is of a form suitable for recursive computation in online applications. A simulation example is given to illustrate the effectiveness of the proposed filter design scheme.
Huijun Gao - One of the best experts on this subject based on the ideXlab platform.
-
recursive Filtering with fading measurements sensor delays and correlated noises
2015Co-Authors: Zidong Wang, Huijun GaoAbstract:In this chapter, the recursive Filtering Problem is firstly investigated for a class of discrete-time non-linear stochastic systems with random parameter matrices, multiple fading measurements, and correlated noises. The phenomenon of measurement fading occurs in a random way and the fading probability for each sensor is governed by an individual random variable obeying a certain probability distribution over the known interval. The purpose of the addressed Filtering Problem is to design an unbiased, recursive, and optimal filter in the minimum variance sense. Intensive stochastic analysis is carried out to obtain the filter gain characterized by the solution to a recursive matrix equation. Based on the proposed filter approach, the gain-constrained recursive Filtering Problem is studied for a class of non-linear time-varying stochastic systems with probabilistic sensor delays and correlated noises. A new recursive Filtering algorithm is developed that ensures both the local optimality and the unbiasedness of the designed filter at each sampling instant which achieving the prespecified filter gain constraint.
-
recursive Filtering with random parameter matrices multiple fading measurements and correlated noises
Automatica, 2013Co-Authors: Zidong Wang, Huijun GaoAbstract:This paper is concerned with the recursive Filtering Problem for a class of discrete-time nonlinear stochastic systems with random parameter matrices, multiple fading measurements and correlated noises. The phenomenon of measurement fading occurs in a random way and the fading probability for each sensor is governed by an individual random variable obeying a certain probability distribution over the known interval [@b"k,@c"k]. Such a probability distribution could be any commonly used discrete distribution over the interval [@b"k,@c"k] that covers the Bernoulli distribution as a special case. The process noise and the measurement noise are one-step autocorrelated, respectively. The process noise and the measurement noise are two-step cross-correlated. The purpose of the addressed Filtering Problem is to design an unbiased and recursive filter for the random parameter matrices, stochastic nonlinearity, and multiple fading measurements as well as correlated noises. Intensive stochastic analysis is carried out to obtain the filter gain characterized by the solution to a recursive matrix equation. The proposed scheme is of a form suitable for recursive computation in online applications. A simulation example is given to illustrate the effectiveness of the proposed filter design scheme.
-
extended kalman Filtering with stochastic nonlinearities and multiple missing measurements
Automatica, 2012Co-Authors: Zidong Wang, Huijun Gao, Lampros K StergioulasAbstract:In this paper, the extended Kalman Filtering Problem is investigated for a class of nonlinear systems with multiple missing measurements over a finite horizon. Both deterministic and stochastic nonlinearities are included in the system model, where the stochastic nonlinearities are described by statistical means that could reflect the multiplicative stochastic disturbances. The phenomenon of measurement missing occurs in a random way and the missing probability for each sensor is governed by an individual random variable satisfying a certain probability distribution over the interval [0,1]. Such a probability distribution is allowed to be any commonly used distribution over the interval [0,1] with known conditional probability. The aim of the addressed Filtering Problem is to design a filter such that, in the presence of both the stochastic nonlinearities and multiple missing measurements, there exists an upper bound for the Filtering error covariance. Subsequently, such an upper bound is minimized by properly designing the filter gain at each sampling instant. It is shown that the desired filter can be obtained in terms of the solutions to two Riccati-like difference equations that are of a form suitable for recursive computation in online applications. An illustrative example is given to demonstrate the effectiveness of the proposed filter design scheme.
-
probability guaranteed h finite horizon Filtering for a class of nonlinear time varying systems with sensor saturations
Systems & Control Letters, 2012Co-Authors: Zidong Wang, Huijun Gao, Lampros K StergioulasAbstract:In this paper, the probability-guaranteed H∞ finite-horizon Filtering Problem is investigated for a class of nonlinear time-varying systems with uncertain parameters and sensor saturations. The system matrices are functions of mutually independent stochastic variables that obey uniform distributions over known finite ranges. Attention is focused on the construction of a time-varying filter such that the prescribed H∞ performance requirement can be guaranteed with probability constraint. By using the difference linear matrix inequalities (DLMIs) approach, sufficient conditions are established to guarantee the desired performance of the designed finite-horizon filter. The time-varying filter gains can be obtained in terms of the feasible solutions of a set of DLMIs that can be recursively solved by using the semi-definite programming method. A computational algorithm is specifically developed for the addressed probability-guaranteed H∞ finite-horizon Filtering Problem. Finally, a simulation example is given to illustrate the effectiveness of the proposed Filtering scheme. © 2012 Elsevier B.V. All rights reserved.
Michael Basin - One of the best experts on this subject based on the ideXlab platform.
-
mean square Filtering Problem for stochastic polynomial systems with gaussian and poisson noises
Asian Control Conference, 2013Co-Authors: Michael Basin, Pablo RodriguezramirezAbstract:This paper presents the mean-square finite-dimensional filter for polynomial system states confused with both, Gaussian and Poisson, white noises over linear observations. Designing the mean-square filter for polynomial systems with white Gaussian and Poisson noises enables one to address the mean-square Filtering Problems for nonlinear system states confused not only with Gaussian white noises but arbitrary strictly defined white noises being weak mean-square derivatives of martingales. A procedure is established for designing the optimal Filtering equations for system states described by polynomial equations of an arbitrary finite degree. An explicit closed form of the designed filter is obtained in case of a third-order polynomial. Performance of the designed optimal filter is verified for a third degree polynomial state.
-
mean square filter design for nonlinear polynomial systems with poisson noise
American Control Conference, 2011Co-Authors: Michael Basin, Juan J. MaldonadoAbstract:This paper presents the mean-square Filtering Problem for incompletely measured polynomial system states, confused with white Poisson noises, over linear observations. The Problem is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated Filtering Problem are first derived. The procedure for obtaining a closed system of the Filtering equations for any polynomial system state with white Poisson noises over linear observations is then established, which yields the explicit closed form of the Filtering equations in the particular case of a third-order state equation. In the example, performance of the designed optimal filter is verified against the conventional mean-square polynomial filter designed for systems with white Gaussian noises.
-
joint state Filtering and parameter estimation for linear stochastic time delay systems
Signal Processing, 2011Co-Authors: Michael Basin, Peng Shi, Dario CalderonalvarezAbstract:This paper presents the joint state Filtering and parameter estimation Problem for linear stochastic time-delay systems with unknown parameters. The original Problem is reduced to the mean-square Filtering Problem for incompletely measured bilinear time-delay system states over linear observations. The unknown parameters are considered standard Wiener processes and incorporated as additional states in the extended state vector. To deal with the new Filtering Problem, the paper designs the mean-square finite-dimensional filter for incompletely measured bilinear time-delay system states over linear observations. A closed system of the Filtering equations is then derived for a bilinear time-delay state over linear observations. Finally, the paper solves the original joint estimation Problem. The obtained solution is based on the designed mean-square filter for incompletely measured bilinear time-delay states over linear observations, taking into account that the filter for the extended state vector also serves as the identifier for the unknown parameters. In the example, performance of the designed state filter and parameter identifier is verified for a linear time-delay system with an unknown multiplicative parameter over linear observations.
-
Optimal Filtering for incompletely measured polynomial states over linear observations
International Journal of Adaptive Control and Signal Processing, 2008Co-Authors: Michael Basin, Dario Calderon-alvarez, Mikhail SkliarAbstract:In this paper, the optimal Filtering Problem for polynomial system states over linear observations with an arbitrary, not necessarily invertible, observation matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated Filtering Problem are first derived. A transformation of the observation equation is introduced to reduce the original Problem to the previously solved one with an invertible observation matrix. The procedure for obtaining a closed system of the Filtering equations for any polynomial state over linear observations is then established, which yields the explicit closed form of the Filtering equations in the particular case of a third-order state equation. In the example, performance of the designed optimal filter is verified against a conventional extended Kalman–Bucy filter. Copyright © 2007 John Wiley & Sons, Ltd.
-
optimal Filtering for polynomial system states with polynomial multiplicative noise
American Control Conference, 2006Co-Authors: Michael Basin, Jose P Perez, Mikhail SkliarAbstract:In this paper, the optimal Filtering Problem for polynomial system states with polynomial multiplicative noise over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated Filtering Problem are first derived. The procedure for obtaining a closed system of the Filtering equations for any polynomial state with polynomial multiplicative noise over linear observations is then established, which yields the explicit closed form of the Filtering equations in the particular cases of of a linear state equation with linear multiplicative noise and a bilinear state equation with bilinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic state with a quadratic multiplicative noise over linear observations against the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman-Bucy filter.
Mikhail Skliar - One of the best experts on this subject based on the ideXlab platform.
-
Optimal Filtering for incompletely measured polynomial states over linear observations
International Journal of Adaptive Control and Signal Processing, 2008Co-Authors: Michael Basin, Dario Calderon-alvarez, Mikhail SkliarAbstract:In this paper, the optimal Filtering Problem for polynomial system states over linear observations with an arbitrary, not necessarily invertible, observation matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated Filtering Problem are first derived. A transformation of the observation equation is introduced to reduce the original Problem to the previously solved one with an invertible observation matrix. The procedure for obtaining a closed system of the Filtering equations for any polynomial state over linear observations is then established, which yields the explicit closed form of the Filtering equations in the particular case of a third-order state equation. In the example, performance of the designed optimal filter is verified against a conventional extended Kalman–Bucy filter. Copyright © 2007 John Wiley & Sons, Ltd.
-
optimal Filtering for polynomial system states with polynomial multiplicative noise
American Control Conference, 2006Co-Authors: Michael Basin, Jose P Perez, Mikhail SkliarAbstract:In this paper, the optimal Filtering Problem for polynomial system states with polynomial multiplicative noise over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated Filtering Problem are first derived. The procedure for obtaining a closed system of the Filtering equations for any polynomial state with polynomial multiplicative noise over linear observations is then established, which yields the explicit closed form of the Filtering equations in the particular cases of of a linear state equation with linear multiplicative noise and a bilinear state equation with bilinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic state with a quadratic multiplicative noise over linear observations against the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman-Bucy filter.
-
optimal Filtering for polynomial system states with polynomial multiplicative noise
International Journal of Robust and Nonlinear Control, 2006Co-Authors: Michael Basin, Jose P Perez, Mikhail SkliarAbstract:In this paper, the optimal Filtering Problem for polynomial system states with polynomial multiplicative noise over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated Filtering Problem are first derived. The procedure for obtaining a closed system of the Filtering equations for any polynomial state with polynomial multiplicative noise over linear observations is then established, which yields the explicit closed form of the Filtering equations in the particular cases of a linear state equation with linear multiplicative noise and a bilinear state equation with bilinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic state with a quadratic multiplicative noise over linear observations against the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman–Bucy filter. Copyright © 2006 John Wiley & Sons, Ltd.
-
optimal Filtering for partially measured polynomial system states
American Control Conference, 2005Co-Authors: Michael Basin, Mikhail SkliarAbstract:In this paper, the optimal Filtering Problem for polynomial systems with partially measured linear part over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated Filtering Problem are first derived. The procedure for obtaining a closed system of the Filtering equations for any polynomial state with partially measured linear part over linear observations with delay is then established, which yields the explicit closed form of the Filtering equations in the particular case of a bilinear system state. In the example, performance of the designed optimal filter is verified for a quadratic-linear state with unmeasured linear part over linear observations against the conventionally designed extended Kalman-Bucy filter.
Biao Huang - One of the best experts on this subject based on the ideXlab platform.
-
technical communique robust h2 optimal Filtering for continuous time stochastic systems with polytopic parameter uncertainty
Automatica, 2008Co-Authors: Kwan Ho Lee, Biao HuangAbstract:In this paper, robust H"2 optimal Filtering is addressed for continuous-time stochastic systems with polytopic parameter uncertainty. A new robust stability condition is presented. A continuous-time robust H"2 optimal filter is obtained by solving a sufficient linear matrix inequality condition characterizing a solution of a robust minimum variance Filtering Problem which takes into account the polytopic type of model uncertainties. The proposed approach is demonstrated through numerical examples.
-
Sampled-data Filtering with error covariance assignment
IEEE Transactions on Signal Processing, 2001Co-Authors: Zidong Wang, Biao HuangAbstract:We consider the sampled-data Filtering Problem by proposing a new performance criterion in terms of the estimation error covariance. An innovation approach to sampled-data Filtering is presented. First, the definition of the estimation covariance e for a sampled-data system is given, then the sampled-data Filtering Problem is reduced to the Kalman filter design Problem for a fictitious discrete-time system, and finally, an effective method is developed to design discrete-time Kalman filters in such a way that the resulting sampled-data estimation covariance achieves a prescribed value. We derive both the existence conditions and the explicit expression of the desired filters and provide an illustrative numerical example to demonstrate the directness and flexibility of the present design method.
-
Robust H/sub 2//H/sub /spl infin// Filtering for linear systems with error variance constraints
IEEE Transactions on Signal Processing, 2000Co-Authors: Biao HuangAbstract:In this correspondence, we consider the robust H/sub 2//H/sub /spl infin// Filtering Problem for linear perturbed systems with steady-state error variance constraints. The purpose of this multiobjective Problem is to design a linear filter that does not depend on the parameter perturbations such that the following three performance requirements are simultaneously satisfied. (1) The Filtering process is asymptotically stable. (2) The steady-state variance of the estimation error of each state is not more than the individual prespecified value. (3) The transfer function from exogenous noise inputs to error state outputs meets the prespecified H/sub /spl infin// norm upper bound constraint. We show that in both continuous and discrete-time cases, the addressed Filtering Problem can effectively be solved in terms of the solutions of a couple of algebraic Riccati-like equations/inequalities. We present both the existence conditions and the explicit expression of desired robust filters. An illustrative numerical example is provided to demonstrate the flexibility of the proposed design approach.