Improved robust finite-horizon Kalman filtering for uncertain networked time-varying systems

Rezaei, Hossein; Esfanjani, Reza Mahboobi; Sedaaghi, Mohammad Hossein

doi:10.1016/j.ins.2014.09.017

Cited by 60 publications

(62 citation statements)

References 19 publications

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“…τ k ≥ 2). For the com-200 pensation mechanism, the distributed robust Kalman filter for linear discrete time-varying system is transformed into the compensation by one-step prediction, which is given by the reorganized measurement sequence and reorganized innovation sequence [23,41]. For the sake of brevity, the case of τ k = 1 will be principally investigated for the measurement delayed system.…”

Section: Reorganized Innovation Sequencementioning

confidence: 99%

Distributed weighted fusion estimation for uncertain networked systems with transmission time-delay and cross-correlated noises

Liu

Yang

et al. 2017

Neurocomputing

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This paper investigates the state estimation issue for uncertain networked systems considering data transmission time-delay and cross-correlated noises. A distributed robust Kalman filtering-based perception and centralized fusion method is proposed to improve the estimation accuracy from perturbed measurement; consequently, reduce the amount of redundant information and alleviate the estimation burden. To describe the transmission time-delay and give rise to cross-correlated and state-dependent noises in the exchange measurement among neighbors, a weighted fusion reorganized innovation strategy is proposed to reduce the computational burden and suppress noise effect. Moreover, to obtain the optimal linear estimate, a fusion estimation approach is used for information collaboration by weighting the error cross-covariance matrices. Finally, an illustrative example is presented to demonstrate the effectiveness and robustness of the proposed method.

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Section: Reorganized Innovation Sequencementioning

confidence: 99%

Distributed weighted fusion estimation for uncertain networked systems with transmission time-delay and cross-correlated noises

Liu

Yang

et al. 2017

Neurocomputing

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“…Then, the optimal linear estimators for single sensor were derived. Based on the novel model in [8], [9,10] developed corresponding estimation algorithms for different systems. To be specific, [9] extended the results in [8] to the multi-sensor distributed case, and derived a distributed fusion filter.…”

Section: E-mail Addressmentioning

confidence: 99%

“…To be specific, [9] extended the results in [8] to the multi-sensor distributed case, and derived a distributed fusion filter. By means of the reorganized innovation approach, [10] investigated the optimal estimator for the linear systems with and without time-stamped data packets. However, multi-step random delays and packet losses model used in [8][9][10] can result in complete loss of packets at times, which affect the performance of proposed estimator.…”

Section: E-mail Addressmentioning

confidence: 99%

“…By means of the reorganized innovation approach, [10] investigated the optimal estimator for the linear systems with and without time-stamped data packets. However, multi-step random delays and packet losses model used in [8][9][10] can result in complete loss of packets at times, which affect the performance of proposed estimator. To overcome this deficiency, the latest measurement transmitted successfully can be used for the estimation.…”

Section: E-mail Addressmentioning

confidence: 99%

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Minimum variance estimation for linear uncertain systems with one-step correlated noises and incomplete measurements

Wang

Fang

Tian

2016

Digital Signal Processing

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“…Kalman filtering, also known as linear optimal quadratic estimation, has attracted much research interests due to its good filtering performance and simple filtering structure [5,6]. In [7], based on the minimum mean square error (MMSE) principle and the projection theory, the traditional Kalman filtering algorithm has been proposed for a class of linear discrete stochastic systems.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays

Chen

Yonglong

et al. 2015

Discrete Dynamics in Nature and Society

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This paper is concerned with the optimal Kalman filtering problem for a class of discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities are introduced to characterize the phenomena of the random two-step sensor delays which may happen during the data transmission. By using the state augmentation approach and innovation analysis technique, an optimal Kalman filter is constructed for the augmented system in the sense of the minimum mean square error (MMSE). Subsequently, the optimal Kalman filtering is derived for corresponding augmented system in initial instants. Finally, a simulation example is provided to demonstrate the feasibility and effectiveness of the proposed filtering method.

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Improved robust finite-horizon Kalman filtering for uncertain networked time-varying systems

Cited by 60 publications

References 19 publications

Distributed weighted fusion estimation for uncertain networked systems with transmission time-delay and cross-correlated noises

Distributed weighted fusion estimation for uncertain networked systems with transmission time-delay and cross-correlated noises

Minimum variance estimation for linear uncertain systems with one-step correlated noises and incomplete measurements

Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays

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