Research on improved evidence theory based on multi-sensor information fusion

Lin, Zhen Rong; Jinye, Xie

doi:10.1038/s41598-021-88814-3

Cited by 11 publications

(3 citation statements)

References 9 publications

(2 reference statements)

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“…Given the lack of an effective information fusion model for heterogeneous multi-sensors, an improved Dempster/Shafer evidence theory algorithm is proposed to fuse heterogeneous multi-sensor information. The algorithm solves the problem of DS evidence theory in dealing with highly conflicting evidence to a certain extent [14]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Weak Pulse Signal Distributed Fusion Detection with Multidimensional Nonlinear Correlation Subjected to Broad Learning

Su,

Long

2024

Preprint

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Due to highly complex distributed sensors chaotic environments in sea monitoring, machinery fault diagnosis, and EEG weak signal detection, the data available to realize the effective detecting tasks of neural networks are often insufficient. Compared with machine learning models, a statistical approach with multidimensional nonlinear correlation exhibits a unique and extraordinary signal pattern predicting capacity and has a simple and powerful framework for signal processing. However, the application of multidimensional nonlinear correlation directly to weak pulse signal detection remains limited. To overcome these limitations and reach highly precision signal detection, a novel multidimensional nonlinear correlation (MNC) based on phase reconstructing and manifold broad learning is investigated for distributed sensor fusion detection under chaotic noise. Firstly, the distributed observation data is reconstructed into fixed-size arrays with phase space reconstructing, and the high dimensional sequence of the manifold broad learning of these tuples is used as an input of nonlinear correlation for extracting spatio-temporal features. Subsequently, a multidimensional nonlinear correlation with a QRS detector layer is developed to predict and classify the existence of a weak pulse signal. The MNC is integrated in the high feature enhanced space of the source domain and the detection fusion of distributed sensors is realized through the majority principle. Simulation experiments results show illustrates the effectiveness and robustness of the proposed MNC method combined with phase reconstructing and manifold broad learning strategy for distributed sensors weak pulse signal fusion detection in a chaotic background.

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Section: Introductionmentioning

confidence: 99%

Weak Pulse Signal Distributed Fusion Detection with Multidimensional Nonlinear Correlation Subjected to Broad Learning

Su,

Long

2024

Preprint

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“…Then, the obtained weights are applied to modify the original evidence to reduce the influence of the evidence containing large conflicts, and make the fusion result more accurate. On the other hand, the improvement method of evidence combination rules believes that conflicts are caused by unreasonable combination rules, and in order to eliminate conflicts, the combination method needs to be changed [25]. In the process of data fusion, for each focal element there is a belief function and a plausibility function.…”

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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“…On the one hand, one is to improve the combination rules, and the representative methods are Yager method [11], Smets method [12], Lefevre method [13] and PCR method [14] etc. In [15], an improved Dempster's combination rule was designed, which introduced the compatibility coefficient to modify the evidence and utilized the average support degree to improve the combination rule. In [16], a new combination rule was proposed based on the analysis and illustration of similarity collision.…”

Section: Introductionmentioning

confidence: 99%

Research on driving behaviour assessment based on evidence theory in 5G‐VANET

Zhang

Tang²,

Liu

2021

IET Communications

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With the rapid development of 5G and vehicular ad hoc network (VANET), the data fusion plays an increasingly important role in the driving behaviour assessment. However, the unreasonable distribution of conflicting evidence remains a significant issue for decision fusion based on evidence theory. By analysing the parameter information of driving behaviour recognition, this paper proposed a new weighted evidence combination approach to fuse the highly conflicting information. In short, the proposed combination method not only retains the excellent mathematical characteristics of Dempster's combination rule, but also fully considers the mutual relations between evidences and the influence of the characteristics of evidence body. Firstly, a new dissimilarity measure is put forward to quantify the conflict degree between the evidences. Furthermore, the uncertainty measure and internal conflict are adopted to determine the weight of each evidence. Based on the weighted averaging combination method, the reasonable combined results are obtained. According to the experimental results, the proposed method has better performance than the existing methods, and provides an effective solution for driving behaviour assessment.

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Research on improved evidence theory based on multi-sensor information fusion

Cited by 11 publications

References 9 publications

Weak Pulse Signal Distributed Fusion Detection with Multidimensional Nonlinear Correlation Subjected to Broad Learning

Weak Pulse Signal Distributed Fusion Detection with Multidimensional Nonlinear Correlation Subjected to Broad Learning

A Weighted Evidence Combination Method for Multisensor Data Fusion

Research on driving behaviour assessment based on evidence theory in 5G‐VANET

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