Signal estimation with multiple delayed sensors using covariance information

Caballero-Águila, Raquel; Hermoso-Carazo, Aurora; Jiménez‐López, J. D.; Linares-Pérez, Josefa; Nakamori, Seiichi

doi:10.1016/j.dsp.2009.06.011

Cited by 42 publications

(30 citation statements)

References 10 publications

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“…When the state-space model of the signal is unknown, some estimation algorithms for linear networked systems can be found in the literature [47], [48]. To be specific, based on the innovation analysis approach, the linear recursive filtering and smoothing algorithms have been presented in [47] to handle the phenomenon of multiple random delayed measurements with different delay rates, and the recursive leastsquares linear estimation algorithms have been given in [48] to deal with uncertain observations, one-step delay and packet dropouts in a unified framework.…”

Section: A Linear Networked Systemsmentioning

confidence: 99%

“…To be specific, based on the innovation analysis approach, the linear recursive filtering and smoothing algorithms have been presented in [47] to handle the phenomenon of multiple random delayed measurements with different delay rates, and the recursive leastsquares linear estimation algorithms have been given in [48] to deal with uncertain observations, one-step delay and packet dropouts in a unified framework. On the other hand, by employing the linear matrix inequality technique, the design problems of optimal H ∞ and H 2 filters have been investigated in [24], [42] for linear networked systems with multiple packet dropouts.…”

Section: A Linear Networked Systemsmentioning

confidence: 99%

“…Linear matrix inequality method time-invariant complex dynamical systems feasible [25], [40]- [44] Difference linear matrix inequality method time-varying complex dynamical systems feasible [21], [27], [45], [46] Innovation analysis approach linear time-invariant/time-varying systems optimal [30]- [32], [39], [47], [48] Hamilton-Jacobi-Isaacs inequality approach general nonlinear time-invariant systems feasible [35] Backward Riccati difference equation method nonlinear time-varying systems sub-optimal [37], [49], [50] Forward Riccati difference equation method nonlinear time-varying systems sub-optimal [7], [16], [51]- [54] [49], [56], [57], the N-order Rice fading channel has been modeled by sequences of independent and identically distributed Gaussian random variables with known means and variances, where the multi-path induced fading stemming mainly from multi-path propagation has been considered when dealing with the control and estimation problems for networked systems and the impact from the fading measurements onto the control/estimation performance has been examined.…”

Section: Applications Solutions Referencesmentioning

confidence: 99%

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Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

et al. 2016

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In this paper, some recent advances on the estimation, filtering and fusion for networked systems are reviewed. Firstly, the network-induced phenomena under consideration are briefly recalled including missing/fading measurements, signal quantization, sensor saturations, communication delays, and randomly occurring incomplete information. Secondly, the developments of the estimation, filtering and fusion for networked systems from four aspects (linear networked systems, nonlinear networked systems, complex networks and sensor networks) are reviewed comprehensively. Subsequently, some recent results on the estimation, filtering and fusion for systems with the network-induced phenomena are reviewed in great detail. In particular, some latest results on the multiobjective filtering problems for time-varying nonlinear networked systems are summarized. Finally, conclusions are given and several possible research directions concerning the estimation, filtering, and fusion for networked systems are highlighted.

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Section: A Linear Networked Systemsmentioning

confidence: 99%

Section: A Linear Networked Systemsmentioning

confidence: 99%

Section: Applications Solutions Referencesmentioning

confidence: 99%

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Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

et al. 2016

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“…Owing to the ever-increasing popularity of communication networks, more and more control tasks are executed over communication links [7][8][9][10][11][12][13]. It should be mentioned that, in the interest of energy saving, the traditional time-based control scheme might be a conservative choice.…”

Section: Introductionmentioning

confidence: 99%

Event‐based security control for discrete‐time stochastic systems

Ding

Wang

Wei

et al. 2016

IET Control Theory & Applications

191

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This paper is concerned with the event-based security control problem for a class of discrete-time stochastic systems with multiplicative noises subject to both randomly occurring Denial-of-Service (DoS) attacks and randomly occurring deception attacks. An event-triggered mechanism is adopted with hope to reduce the communication burden, where the measurement signal is transmitted only when a certain triggering condition is violated. A novel attack model is proposed to reflect the randomly occurring behaviors of the DoS attacks as well as the deception attacks within a unified framework via two sets of Bernoulli distributed white sequences with known conditional probabilities. A new concept of mean-square security domain is put forward to quantify the security degree. We aim to design an output feedback controller such that the closed-loop system achieves the desired security. By using the stochastic analysis techniques, some sufficient conditions are established to guarantee the desired security requirement and the control gain is obtained by solving some linear matrix inequalities with nonlinear constraints. A simulation example is utilized to illustrate the usefulness of the proposed controller design scheme.

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“…It is generally known that the traditional Kalman filter algorithm [1], [2], [6], [21], [22] is a recursive least mean square (LMS) one dealing with a single node and is optimal for linear systems with exact system models. On the other hand, to make use of the spatial information of the sensor nodes, distributed filtering problems have recently gained much research attention.…”

Section: Introductionmentioning

confidence: 99%

Event-Based Recursive Distributed Filtering Over Wireless Sensor Networks

Liu

Wang

et al. 2015

IEEE Trans. Automat. Contr.

253

112

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Abstract-In this paper, the distributed filtering problem is investigated for a class of discrete time-varying systems with an event-based communication mechanism. Each intelligent sensor node transmits the data to its neighbors only when the local innovation violates a predetermined Sendon-Delta (SoD) data transmission condition. The aim of the proposed problem is to construct a distributed filter for each sensor node subject to sporadic communications over wireless networks. In terms of an event indicator variable, the triggering information is utilized so as to reduce the conservatism in the filter analysis. An upper bound for the filtering error covariance is obtained in form of Riccati-like difference equations by utilizing the inductive method. Subsequently, such an upper bound is minimized by appropriately designing the filter parameters iteratively, where a novel matrix simplification technique is developed to handle the challenges resulting from the sparseness of the sensor network topology and filter structure preserving issues. The effectiveness of the proposed strategy is illustrated by a numerical simulation.Index Terms-Distributed filtering, wireless sensor networks, eventbased mechanism, Send-on-Delta concept.

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Signal estimation with multiple delayed sensors using covariance information

Cited by 42 publications

References 10 publications

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

Event‐based security control for discrete‐time stochastic systems

Event-Based Recursive Distributed Filtering Over Wireless Sensor Networks

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