Distributed compressed sensing based joint detection and tracking for multistatic radar system

Liu, Jing; Liu, Feng; Mallick, Mahendra

doi:10.1016/j.ins.2016.06.032

Cited by 35 publications

(16 citation statements)

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“…In the real world, some connections among things can be expressed by crisp relations due to its complexity and uncertainty . How to model the relation in the complexity is still an open issue, though some intelligent algorithms are presented to address this issue . Two examples are provided here to demonstrate crisp relations.…”

Section: Preliminariesmentioning

confidence: 99%

Evaluation method based on fuzzy relations between Dempster-Shafer belief structure

Zheng

Deng

2018

Int. J. Intell. Syst.

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Many relations in the real world can be described by mathematical language. Fuzzy set theory can transform human language into mathematical language and use membership degree function to describe relations between events. Dempster–Shafer evidence theory provides basic probability assignment (BPA), which can describe the occurrence rate of attributes in basic events. Based on the known membership degree function and BPA distribution, a new evaluation method is proposed in this paper to analyze decision making. Given the relations among relevant events, which are expressed by BPA distribution and membership degree function, the relations among basic events and top event can be obtained. The Dempster's combination rule and pignistic probability transformation are used to transform BPA distribution into probability distribution. The belief measure is applied to deal with these fuzzy relations. Some numerical examples are given in this paper to illustrate the proposed evaluation methodology.

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Section: Preliminariesmentioning

confidence: 99%

Evaluation method based on fuzzy relations between Dempster-Shafer belief structure

Zheng

Deng

2018

Int. J. Intell. Syst.

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“…Failure mode and effects analysis (FMEA) is widely used to define, identify, and eliminate known or potential failures, errors, etc from a system, design, or process to prevent an unexpected failure . Because of its reliability, safety, and simplicity, FMEA plays an important role in the design of industrial products such as structures operating in power, aeronautics, and astronautics . Failure mode and effects analysis can be a solution for various reliability problems in many industries including aerospace, nuclear, chemical, and manufacturing …”

Section: Introductionmentioning

confidence: 99%

Failure mode and effects analysis based on D numbers and TOPSIS

Bian

Zheng

Yin

et al. 2018

Quality & Reliability Eng

175

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Failure mode and effects analysis (FMEA) is a widely used technique for assessing the risk of potential failure modes in designs, products, processes, system, and services. One of the main problems with FMEA is the need to address a variety of assessments given by FMEA team members and the sequence of the failure modes according to the degree of risk factors. Many different methods have been proposed to improve the traditional FMEA, which is impractical when the risk assessments given by multiple experts to one failure mode are imprecise, incomplete, or inconsistent. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, a new risk priority model based on D numbers and technique for the order of preference by similarity to ideal solution (TOPSIS) is proposed to evaluate the risk in FMEA. In the proposed model, the assessments given by the FMEA team members are represented by D numbers, where a new feasible and effective method can effectively represent the uncertain information. The TOPSIS method, a multicriteria decision‐making method is presented to rank the preference of failure modes with respect to risk factors. Finally, an application of the failure modes of the rotor blades of an aircraft turbine is provided to illustrate the efficiency of the proposed method.

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“…Modeling complex systems are very complicated since there are many factors interacting with each other [4][5][6][7][8]. A key component of model validation is the specific, rigorous disposal of numerous sources and different types of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Evidential Model Validation under Epistemic Uncertainty

Deng

2018

Mathematical Problems in Engineering

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This paper proposes evidence theory based methods to both quantify the epistemic uncertainty and validate computational model. Three types of epistemic uncertainty concerning input model data, that is, sparse points, intervals, and probability distributions with uncertain parameters, are considered. Through the proposed methods, the given data will be described as corresponding probability distributions for uncertainty propagation in the computational model, thus, for the model validation. The proposed evidential model validation method is inspired by the idea of Bayesian hypothesis testing and Bayes factor, which compares the model predictions with the observed experimental data so as to assess the predictive capability of the model and help the decision making of model acceptance. Developed by the idea of Bayes factor, the frame of discernment of Dempster-Shafer evidence theory is constituted and the basic probability assignment (BPA) is determined. Because the proposed validation method is evidence based, the robustness of the result can be guaranteed, and the most evidence-supported hypothesis about the model testing will be favored by the BPA. The validity of proposed methods is illustrated through a numerical example.

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Distributed compressed sensing based joint detection and tracking for multistatic radar system

Cited by 35 publications

References 50 publications

Evaluation method based on fuzzy relations between Dempster-Shafer belief structure

Evaluation method based on fuzzy relations between Dempster-Shafer belief structure

Failure mode and effects analysis based on D numbers and TOPSIS

Evidential Model Validation under Epistemic Uncertainty

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