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Sensors 2018, 18(6), 1902; https://doi.org/10.3390/s18061902

An Extension to Deng’s Entropy in the Open World Assumption with an Application in Sensor Data Fusion

1
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
2
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
*
Authors to whom correspondence should be addressed.
Received: 6 May 2018 / Revised: 8 June 2018 / Accepted: 8 June 2018 / Published: 11 June 2018
(This article belongs to the Collection Multi-Sensor Information Fusion)
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Abstract

Quantification of uncertain degree in the Dempster-Shafer evidence theory (DST) framework with belief entropy is still an open issue, even a blank field for the open world assumption. Currently, the existed uncertainty measures in the DST framework are limited to the closed world where the frame of discernment (FOD) is assumed to be complete. To address this issue, this paper focuses on extending a belief entropy to the open world by considering the uncertain information represented as the FOD and the nonzero mass function of the empty set simultaneously. An extension to Deng’s entropy in the open world assumption (EDEOW) is proposed as a generalization of the Deng’s entropy and it can be degenerated to the Deng entropy in the closed world wherever necessary. In order to test the reasonability and effectiveness of the extended belief entropy, an EDEOW-based information fusion approach is proposed and applied to sensor data fusion under uncertainty circumstance. The experimental results verify the usefulness and applicability of the extended measure as well as the modified sensor data fusion method. In addition, a few open issues still exist in the current work: the necessary properties for a belief entropy in the open world assumption, whether there exists a belief entropy that satisfies all the existed properties, and what is the most proper fusion frame for sensor data fusion under uncertainty. View Full-Text
Keywords: Dempster-Shafer evidence theory (DST); uncertainty measure; open world; closed world; Deng entropy; extended belief entropy; sensor data fusion Dempster-Shafer evidence theory (DST); uncertainty measure; open world; closed world; Deng entropy; extended belief entropy; sensor data fusion
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Tang, Y.; Zhou, D.; Chan, F.T.S. An Extension to Deng’s Entropy in the Open World Assumption with an Application in Sensor Data Fusion. Sensors 2018, 18, 1902.

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