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

Liu, Li; Yang, An-Shik; Tu, Xiaowei; Fei, Minrui; Naeem, Wasif

doi:10.1016/j.neucom.2017.02.095

Cited by 24 publications

(17 citation statements)

References 42 publications

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“…It is noted that the deterministic uncertainty appears in (16) and (17). Therefore, it is impossible to have the exact value of the covariance matricesΘ ( ) andΘ ( ).…”

Section: Estimation For Event-triggered Signal Selection Methodsmentioning

confidence: 99%

“…Based on Theorem 5 and Kalman-type filtering, referring to (17), suppose that the error covariance matrix is denoted as the following form:…”

Section: Lemma 3 Assume That Matrices and Have Compatible Dimementioning

confidence: 99%

“…In order to alleviate the negative influence of network-induced phenomena and improve system performance, some distributed estimation and filtering methods are proposed. Liu et al [17] presented distributed fusion estimation based on Kalmantype filtering, which introduced two level weighted fusion scheme. For unknown nonlinear system, a robust adaptive finite-time parameter estimation [18] and control based on parameter estimation errors was studied.…”

Section: Introductionmentioning

confidence: 99%

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Modelling and Estimation for Uncertain Systems with Transmission Delays, Packet Dropouts, and Out‐of‐Order Packets

et al. 2018

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The study focuses on the modelling and estimation of a class of discrete-time uncertain systems, including network-induced random delays, packet dropouts, and out-of-order packets during the data transmission from the plant to the estimator. In order to improve system performance, event-triggered signal selection method is used to establish the system model. Based on this model, a distributed measurement and centralized fusion estimation scheme is designed using a robust finite horizon Kalman-type filter. Since the phenomena caused by the network-induced deteriorate estimation accuracy, a time-based reorganization measurement is employed to design a linear delay compensation strategy based on estimation. Moreover, in order to obtain the optimal linear estimation, weighted fusion estimation approach is used to perform information collaboration through the error cross-covariance matrix. Simulation results demonstrate that the proposed method has higher estimation performance than the existing methods in this study.

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“…It is noted that the deterministic uncertainty appears in (16) and (17). Therefore, it is impossible to have the exact value of the covariance matricesΘ ( ) andΘ ( ).…”

Section: Estimation For Event-triggered Signal Selection Methodsmentioning

confidence: 99%

“…Based on Theorem 5 and Kalman-type filtering, referring to (17), suppose that the error covariance matrix is denoted as the following form:…”

Section: Lemma 3 Assume That Matrices and Have Compatible Dimementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling and Estimation for Uncertain Systems with Transmission Delays, Packet Dropouts, and Out‐of‐Order Packets

et al. 2018

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“…e method of measurement transformation developed the Kalman filter using the recombination of the innovation sequence. Due to restructuring the measurement sequence, the system with time delay is transformed into the correlative one with free time-delay [35].…”

Section: Introductionmentioning

confidence: 99%

Distributed State Estimation for Dynamic Positioning Systems with Uncertain Disturbances and Transmission Time Delays

Liu

Yao

et al. 2020

Complexity

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The dynamic positioning system of unmanned underwater vehicles (UUVs) is a complex and large-scale system mainly due to the nonlinear dynamics, uncertainty in model parameters, and external disturbances. With the aid of the bio-inspired computing (BIC) method, the designed three-dimensional (3D) spatial positioning system is used for enlarging communication constraints and increasing signal coordination processing. With the growing of measurement scales, the issue of the networked high-precision positioning has been developed rapidly. Then, an information fusion estimation approach is presented for the distributed networked systems with data random transmission time delays and lost and disordered packets. To reduce the communication burden, an adaptive signal selection scheme is employed to reorganize the measurement sequence, and the parameter uncertainties as well as cross-correlated noise are used to describe the uncertain disturbances. Moreover, a reoptimal weighted fusion state estimation is designed to alleviate the information redundancy and maintain higher measurement accuracy. An illustrative example obtained from the 3D spatial positioning system is given to validate the effectiveness of the proposed method.

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“…Liu et al [7] investigate the state estimation issue for uncertain networked systems considering data transmission time-delay and cross-correlated noises. They propose a distributed robust Kalman filtering-based perception and centralized fusion method to improve the estimation accuracy from perturbed measurement.…”

mentioning

confidence: 99%

Distributed control and optimization with resource-constrained networked systems

Cheng

et al. 2017

Neurocomputing

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

Cited by 24 publications

References 42 publications

Modelling and Estimation for Uncertain Systems with Transmission Delays, Packet Dropouts, and Out‐of‐Order Packets

Modelling and Estimation for Uncertain Systems with Transmission Delays, Packet Dropouts, and Out‐of‐Order Packets

Distributed State Estimation for Dynamic Positioning Systems with Uncertain Disturbances and Transmission Time Delays

Distributed control and optimization with resource-constrained networked systems

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