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An Extension to Deng's Entropy in the Open World Assumption with an Application in Sensor Data Fusion
Yongchuan Tang1,2, Deyun Zhou3, Felix T S Chan4
1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China. tangyongchuan@mail.nwpu.edu.cn.
Sensors (Basel, Switzerland)
|June 13, 2018
Summary
This study extends belief entropy to the open world assumption in Dempster-Shafer theory, addressing uncertainty quantification. The new method, EDEOW, improves information fusion for sensor data under uncertain circumstances.
Area of Science:
- Information Theory
- Artificial Intelligence
- Decision Support Systems
Background:
- Quantifying uncertainty in Dempster-Shafer theory (DST) under open world assumptions remains a challenge.
- Existing DST uncertainty measures are confined to closed-world scenarios where the frame of discernment is complete.
Purpose of the Study:
- To extend belief entropy to the open world assumption within the DST framework.
- To propose a novel entropy measure that accounts for uncertainty represented by the frame of discernment and the empty set's mass function.
Main Methods:
- An extension to Deng's entropy, termed EDEOW (Entropy in the Open World Assumption), was developed.
- An EDEOW-based information fusion approach was formulated and applied to sensor data fusion.
Main Results:
- The proposed EDEOW measure generalizes Deng's entropy and can revert to it in closed-world cases.
- Experimental results validated the effectiveness of the EDEOW measure and the modified fusion approach for sensor data fusion under uncertainty.
Conclusions:
- The extended belief entropy (EDEOW) offers a viable solution for uncertainty quantification in DST under open world assumptions.
- The study highlights remaining open issues, including defining necessary properties for open-world belief entropy and optimizing fusion frames for uncertain sensor data.
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