Robust Fusion Estimation for Multisensor Uncertain Systems With State Delay Based on Data-Driven Communication Strategy

Wang, Jing; Mao, Yao; Li, Ziqiang; Gao, Junwei; Liu, Huabo

doi:10.1109/access.2020.3017631

Cited by 7 publications

(4 citation statements)

References 34 publications

(40 reference statements)

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“…Researchers have developed many different approaches for data fusion to improve data accuracy, include ordered weighted averaging (OWA) (Rezamand et al [44]), maximum likelihood estimation (Monte-Morenoet al [45]), Bayes Estimation (Gai and Wang [46]), Kalman filtering (Lanckriet Gert et al [47]; Caron et al [48]), robust information fusion (Wang et al [49]) and Dempster-Shafer (D-S) evidence theory (Varshney [50]; Kam et al [51]; Radman et al [52]; Guo and Xu [53]), etc. Moreover, Kordestani et al [54] proposed a mixed data fusion method based on OWA and Kalman filtering.…”

Section: Literature Reviewmentioning

confidence: 99%

A Procurement Method in Oil Marketing Company Based on Forecast Model and Expectation Criterion

Zhi-hong

Wang

2021

Mathematical Problems in Engineering

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Within the oil marketing operation, various entities compete and attempt to maximize their proﬁts by providing sufficient supply to meet needs of market. It is an optimal method for oil marketing company operation with a dynamically reasonable inventory to maximize proﬁt under oil price fluctuation and inventory sales lag. In this paper, we study the optimal procurement method of oil marketing company which confirms the reasonable inventory. We build a data fusion model for the GMDH- (group method of data handling-) type neural network and normal distribution forecast results, what is trying to confirm the safety stock (SS). On the basis of the expectation criterion of risk decision and safety stock limit, oil marketing companies can make scientific purchase decision for inventory income. Numerical results reveal that this method has a good effect for inventory income.

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Section: Literature Reviewmentioning

confidence: 99%

A Procurement Method in Oil Marketing Company Based on Forecast Model and Expectation Criterion

Zhi-hong

Wang

2021

Mathematical Problems in Engineering

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“…The state augmentation method in [24] converts time-delay systems into non-time-delay systems with excellent results. The method in [24] was used in [25] for a multi-sensor system, but random packet drop was not considered.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Robust Fusion Estimation for Multi-Sensor Time-Delay Systems with Packet Drops

Du,

Liu,

2023

Applied Sciences

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This paper investigates the robust fusion estimation problem for multi-sensor systems with communication constraints, parameter uncertainty, d-step state delays, and deterministic control inputs. The multi-sensor system consists of a fusion center and some sensor nodes with computational capabilities, between which there are random packet drops. The state augmentation method is utilized to transform a time-delay system into a non-time-delay one. The robust state estimation algorithm is derived based on the sensitivity penalty for each sensor node to reduce the impact of modelling errors, and modelling errors here are not limited to a unique form, which implies that the fusion estimator applies to a wide range of situations. An event-triggered transmission strategy has been adopted to effectively alleviate the communication burden from the sensor node to the fusion center. Moreover, the fusion estimator handles packet drops arising from unreliable channels, and the corresponding pseudo-cross-covariance matrix is provided. Some conditions are given to ensure that the estimation error of the robust fusion estimator is uniformly bounded. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator.

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“…For the sake of solving this problem, many math theories are talked over and applied to treat indeterminate, fuzzy as well as inaccurate sensor data. These theories contain Bayesian principle [11], fuzzy set principle [12], Rough set [13], evidence theory [14], evidential reasoning [15], Z number [16], D number [17], etc [18][19][20]. In this study, we mainly study the evidence theory was on behalf of belief function to treat multi-sensor information fusion.…”

Section: Introductionmentioning

confidence: 99%

A Weighted Evidence Combination Method for Multisensor Data Fusion

Liu¹,

Liu²

2022

Journal of Internet Technology

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<p>Multisensor information fusion exerts a key part in lots of practical usages. Dempster-shafer evidence theory has drawn extensive attention in many scopes of information fusion due to its flexibility and effectiveness in dealing with uncertain data without aforehand data. But when combining highly contradictory evidence with Dempster’s combinatorial principles, it can result in counterintuitive results. To solve the issue, the study proposes a multi-sensor data weighted evidence combination fusion method based on inter-evidence difference measure. Firstly, different measures including evidence distance and conflict are with the definition of characterizing distinctions between the two pieces of evidence. Then, according to the difference between each evidence and the average evidence, the weight coefficients of each evidence are calculated. In the end, initial evidence is discounted according to weighting factor, as well as Dempster’s combination principle is adopted to discount the evidence for fusion. Many instances show that this way can efficiently treat highly conflicting evidence and has good convergence performance.</p> <p> </p>

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Robust Fusion Estimation for Multisensor Uncertain Systems With State Delay Based on Data-Driven Communication Strategy

Cited by 7 publications

References 34 publications

A Procurement Method in Oil Marketing Company Based on Forecast Model and Expectation Criterion

A Procurement Method in Oil Marketing Company Based on Forecast Model and Expectation Criterion

Event-Triggered Robust Fusion Estimation for Multi-Sensor Time-Delay Systems with Packet Drops

A Weighted Evidence Combination Method for Multisensor Data Fusion

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