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Design of fuzzy set-based deep fusion algorithm for multi-sensor data

Weibing Li

Weibing Li

College of Integrated Circuits, Beijing PolytechnicBeijing, China
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Li, Weibing
 e 
Shenggang Wu

Shenggang Wu

College of Integrated Circuits, Beijing PolytechnicBeijing, China
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Wu, Shenggang
  
21 mar 2025
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Pubblicato online: 21 mar 2025
Ricevuto: 18 ott 2024
Accettato: 01 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0676
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© 2025 Weibing Li et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

The Process Chart of Data Fusion
The Process Chart of Data Fusion

Figure 2.

Filtering effect
Filtering effect

Figure 3.

The fusion result is compared to the original data
The fusion result is compared to the original data

Figure 4.

The target recognition success rate of four evidence was compared
The target recognition success rate of four evidence was compared

Dynamic bending distance calculation and weighted value

Dynamic bending distance Node 1 Node 2 Node 3 Support value The value of its own reliability Final fusion weighted value
Node 1 0 11.23 5.70 0.220 0.322 0.213
Node 2 11.23 0 6.40 0.299 0.333 0.291
Node 2 5.70 6.40 0 0.481 0.345 0.496

Comparison of the four evidence of the fusion

Method m(A) m(B) m(C) m(Φ)
Yager 0.3880 0 0.000034 0.6120
Dempster 0.9991 0 0.000086 0
Murphy 0.9994 0.000022 0.000648 0
Zhang 0.9994 0.000022 0.000322 0
Ghiasi 0.5146 0 0.000495 0
Ours 0.9996 0.00000009 0.000006 0
Lingua:
Inglese
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Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro
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