ArticleSensors (Basel, Switzerland)2019
Paradox Elimination in Dempster-Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Deng Entropy-Based Heuristic Method to Determine Discounting Coefficient in Dempster-Shafer Evidence Fusion.Entropy (Basel, Switzerland) · 2026Article
- Hybrid Mode Sensor Fusion for Accurate Robot Positioning.Sensors (Basel, Switzerland) · 2025Review
- Hybrid Machine Learning-Based Fault-Tolerant Sensor Data Fusion and Anomaly Detection for Fire Risk Mitigation in IIoT Environment.Sensors (Basel, Switzerland) · 2025Article
- Fault Diagnosis Method for Space Fluid Loop Systems Based on Improved Evidence Theory.Entropy (Basel, Switzerland) · 2024Article
- Land Cover Classification Based on Airborne Lidar Point Cloud with Possibility Method and Multi-Classifier.Sensors (Basel, Switzerland) · 2023Article
- Multisensor Data Fusion in IoT Environments in Dempster-Shafer Theory Setting: An Improved Evidence Distance-Based Approach.Sensors (Basel, Switzerland) · 2023Article
- Article
- Time-Domain Data Fusion Using Weighted Evidence and Dempster-Shafer Combination Rule: Application in Object Classification.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster-Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a successful fusion algorithm is proposed which addresses these limitations of the original Dempster-Shafer (DS) framework. A novel entropy function is proposed based on Shannon entropy, which is better at capturing uncertainties compared to Shannon and Deng entropy. An 8-step algorithm has been developed which can eliminate the inherent paradoxes of classical DS theory. Multiple examples are presented to show that the proposed method is effective in handling conflicting information in spatial domain. Simulation results showed that the proposed algorithm has competitive convergence rate and accuracy compared to other methods presented in the literature.
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Registered trials
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