Minimum variance generalized state estimators for multiple sensors with different delay rates

Hounkpevi, Franck O.; Yaz, E.

doi:10.1016/j.sigpro.2006.06.017

Cited by 67 publications

(51 citation statements)

References 17 publications

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“…Remark 2: In real-time systems, the measurement data may be transferred through multiple sensors. For different sensor, if there exists the data loss (also called packet dropout or measurement missing) phenomenon, the data missing probability may be different [9], [10], [12], [13], [23]. In this sense, it would be more reasonable to assume that the data missing law for each individual sensor satisfies individual probabilistic distribution.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Setting , we subsequently obtain an augmented system as follows: (8) where (9) The state covariance matrix of the augmented system (8) can be defined as (10) Our aim in this paper is to design a finite-horizon filter in the form of (7) such that the following two requirements are satisfied simultaneously:…”

Section: Problem Formulationmentioning

confidence: 99%

“…Such a missing measurement phenomenon that typically occurs in networked control systems has attracted considerable attention during the past few years, see [4], [9], [10], [12], [13], [19], [20], [23], and the references therein. Up to now, in most reported paper concerning missing measurements, a common assumption is that the measurement signal is either completely missing or completely available, and all the sensors have the same data missing probability [7], [22].…”

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confidence: 99%

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Variance-Constrained ${\cal H}_{\infty}$ Filtering for a Class of Nonlinear Time-Varying Systems With Multiple Missing Measurements: The Finite-Horizon Case

Dong

Wang

et al. 2010

IEEE Trans. Signal Process.

137

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Abstract-This paper is concerned with the robust H1 finitehorizon filtering problem for a class of uncertain nonlinear discrete time-varying stochastic systems with multiple missing measurements and error variance constraints. All the system parameters are time-varying and the uncertainty enters into the state matrix. The measurement missing phenomenon occurs in a random way, and the missing probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution in the interval [0 1]. The stochastic nonlinearities under consideration here are described by statistical means which can cover several classes of well-studied nonlinearities. Sufficient conditions are derived for a finite-horizon filter to satisfy both the estimation error variance constraints and the prescribed H 1 performance requirement. These conditions are expressed in terms of the feasibility of a series of recursive linear matrix inequalities (RLMIs). Simulation results demonstrate the effectiveness of the developed filter design scheme.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

mentioning

confidence: 99%

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Variance-Constrained ${\cal H}_{\infty}$ Filtering for a Class of Nonlinear Time-Varying Systems With Multiple Missing Measurements: The Finite-Horizon Case

Dong

Wang

et al. 2010

IEEE Trans. Signal Process.

137

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“…[3, 8-10, 22, 34]. To be more specific, the optimal estimation problems have been investigated in [8,22] for linear systems with multiple packet dropouts and the random sensor delays have been taken into account in [9,34]. It is worth mentioning that, in most reported results, the measurement signal has been assumed to be either completely lost or successfully transferred, and a typical way is to model the missing measurements by the Bernoulli distribution.…”

Section: Introductionmentioning

confidence: 99%

Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements

et al. 2012

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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.

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“…Such a description, however, does have its limitations since it cannot cover some practical cases, for example, the case when only partial information is missing and the case when the individual sensor has different missing probability. Note that the latter case has been dealt with in [6,7] where the minimum variance state estimators have been designed for linear systems with multiple sensors with different failure/delay rates. However, to the best of the authors' knowledge, the filtering problem has not yet been addressed for uncertain stochastic nonlinear time-delay systems with probabilistic missing measurements, which still remains as a challenging problem.…”

Section: Introductionmentioning

confidence: 99%

Robust filtering with stochastic nonlinearities and multiple missing measurements

2009

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This paper is concerned with the filtering problem for a class of discrete-time uncertain stochastic nonlinear time-delay systems with both the probabilistic missing measurements and external stochastic disturbances. The measurement missing phenomenon is assumed to occur in a random way, and the missing probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution over the interval [0 1]. Such a probabilistic distribution could be any commonly used discrete distribution over the interval [0 1]. The multiplicative stochastic disturbances are in the form of a scalar Gaussian white noise with unit variance. The purpose of the addressed filtering problem is to design a filter such that, for the admissible random measurement missing, stochastic disturbances, norm-bounded uncertainties as well as stochastic nonlinearities, the error dynamics of the filtering process is exponentially mean-square stable. By using the linear matrix inequality (LMI) method, sufficient conditions are established that ensure the exponential mean-square stability of the filtering error, and then the filter parameters are characterized by the solution to a set of LMIs. Illustrative examples are exploited to show the effectiveness of the proposed design procedures.

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Minimum variance generalized state estimators for multiple sensors with different delay rates

Cited by 67 publications

References 17 publications

Variance-Constrained ${\cal H}_{\infty}$ Filtering for a Class of Nonlinear Time-Varying Systems With Multiple Missing Measurements: The Finite-Horizon Case

Variance-Constrained ${\cal H}_{\infty}$ Filtering for a Class of Nonlinear Time-Varying Systems With Multiple Missing Measurements: The Finite-Horizon Case

Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements

Robust filtering with stochastic nonlinearities and multiple missing measurements

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