A New Multi-Sensor Target Recognition Framework based on Dempster-Shafer Evidence Theory

Wang, Kan

doi:10.23940/ijpe.18.06.p13.12241233

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…The discussion on the effectiveness of using a Gaussian probability distribution function to determine BPAs. We learned that some papers [ 22 , 23 ] used the triangular fuzzy function to accomplish this work, and the fusion performance of this method was compared. For determining the BPA using a triangular affiliation function, each feature contains a triangular affiliation function for each category, assuming that the category is A and the minimum, average and maximum values of the features under category A are

, respectively, the trigonometric function is denoted as

, and the BPA generation stage obtains the deployed by projecting the original feature values into the trigonometric function BPA.…”

Section: Comparative Analysismentioning

confidence: 99%

“…The BPA determination methods are divided into function-based BPA determination and intelligent algorithm-based BPA determination. Among the function-based BPA determination methods, the triangular fuzzy function-based BPA construction method is the most employed owing to its simple construction [ 22 , 23 ]. In addition, there are methods to generate BPA using trapezoidal fuzzy functions [ 24 ], Gaussian fuzzy functions [ 25 ], etc.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Source Information Fusion Based on Negation of Reconstructed Basic Probability Assignment with Padded Gaussian Distribution and Belief Entropy

Chen

Zhong

Tang

et al. 2022

Entropy

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Multi-source information fusion is widely used because of its similarity to practical engineering situations. With the development of science and technology, the sources of information collected under engineering projects and scientific research are more diverse. To extract helpful information from multi-source information, in this paper, we propose a multi-source information fusion method based on the Dempster-Shafer (DS) evidence theory with the negation of reconstructed basic probability assignments (nrBPA). To determine the initial basic probability assignment (BPA), the Gaussian distribution BPA functions with padding terms are used. After that, nrBPAs are determined by two processes, reassigning the high blur degree BPA and transforming them into the form of negation. In addition, evidence of preliminary fusion is obtained using the entropy weight method based on the improved belief entropy of nrBPAs. The final fusion results are calculated from the preliminary fused evidence through the Dempster’s combination rule. In the experimental section, the UCI iris data set and the wine data set are used for validating the arithmetic processes of the proposed method. In the comparative analysis, the effectiveness of the BPA determination using a padded Gaussian function is verified by discussing the classification task with the iris data set. Subsequently, the comparison with other methods using the cross-validation method proves that the proposed method is robust. Notably, the classification accuracy of the iris data set using the proposed method can reach an accuracy of 97.04%, which is higher than many other methods.

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, respectively, the trigonometric function is denoted as

, and the BPA generation stage obtains the deployed by projecting the original feature values into the trigonometric function BPA.…”

Section: Comparative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Source Information Fusion Based on Negation of Reconstructed Basic Probability Assignment with Padded Gaussian Distribution and Belief Entropy

Chen

Zhong

Tang

et al. 2022

Entropy

View full text Add to dashboard Cite

show abstract

“…The Dempster-Shafer theory of evidence, proposed by Dempster and Shafer to handle uncertainty [39,40], has a wide range of applications in risk assessments, medical diagnosis, and target recognition [41][42][43]. It is a typical method of uncertainty information fusion because of its adjustability in uncertainty modeling [44]. Some preliminaries are introduced below.…”

Section: Dempster-shafer Theory Of Evidencementioning

confidence: 99%

An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems

Wang

Wei

2022

Applied Sciences

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Multiagent systems (MASs) have a wide range of industrial applications due to agents’ advantages. However, because of the agents’ dynamic behaviors, it is a challenge to ensure the quality of service they present. In this paper, to address this problem, we propose an adaptive agent trust estimation model where agents may decide to go from genuine to malicious or the other way around. In the proposed trust model, both direct trust and indirect reputation are used. However, the indirect reputation derived from the direct experience of third-party agents must have reasonable confidence to be useful. The proposed model introduces a near-perfect measure that utilizes consistency, credibility, and certainty to capture confidence. Moreover, agents are incentivized to contribute correct information (to be honest) through a credit mechanism in the proposed model. Simulation experiments are conducted to evaluate the proposed model’s performance against some of the previous trust models reported in the literature.

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“…DST, proposed by Dempster and Shafer to handle uncertainty [11], [12], has a wide range applications in risk assessments, medical diagnosis, and target recognition. It is a typical method of uncertain information fusion [20]. Some preliminaries are introduced below.…”

Section: Dempster-shafer Theory Of Evidencementioning

confidence: 99%

An improved evidence theory-based trust model for multiagent resource allocation

Wang¹,

Zgaya-Biau²,

Mathieu³

et al. 2020

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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In a resource allocation system, resource suppliers and customers can be naturally modelled as autonomous and interactive entities. In this context, we propose in this paper a multiagent system architecture based on trust and honesty concepts between agents in order to synchronize resource allocation in a distributed environment. Indeed, agents use several means in order to allocate resources efficiently as dialogue, adaptability, cooperation, collaboration and even negotiation in addition to the notion of trust. So, individual agents have to evaluate the trustworthiness of others to select those to interact with. Moreover, resource inadequacies can exist in the resource allocation systems, and it is difficult to meet the resource requirements of all agents simultaneously. To overcome these difficulties, we propose a distributed multiagent resource allocation system that emphasizes the issues of agents trust and resource inadequacies, which is called MARA-T&R. In this system, the evidence theory is improved thanks to the Deng entropy to estimate the trustworthiness of agents. Additionally, we use the concept of reservations to solve the problem of resource inadequacies. Our simulation results highlight the excellent performances of this improved trust model and the efficiency of the proposed MARA-T&R system for resource allocation.

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A New Multi-Sensor Target Recognition Framework based on Dempster-Shafer Evidence Theory

Cited by 3 publications

References 19 publications

Multi-Source Information Fusion Based on Negation of Reconstructed Basic Probability Assignment with Padded Gaussian Distribution and Belief Entropy

Multi-Source Information Fusion Based on Negation of Reconstructed Basic Probability Assignment with Padded Gaussian Distribution and Belief Entropy

An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems

An improved evidence theory-based trust model for multiagent resource allocation

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