Abstract
The Internet of things expands the ability of human beings to perceive the surrounding environment, bringing a great challenge to the multi-sensor data processing. Evidence theory, one of the most effective processing technologies, is commonly employed in the multi-sensor information fusion. However, many counter-intuitive results of multi-sensor data fusion may be obtained when fused evidence is highly conflicting. In this study, a new comprehensive method for calculating the entropy of each evidence is proposed, with the goal of improving information volume measurement. In addition, a conflict measure method of multi-sensor evidence is introduced, which can calculate the weighted average evidence, by synthesizing vector space and evidence distribution. Finally, the pre-processed body of evidences have been merged based on the evidence theory. The proposed multi-sensor fusion approach based on comprehensive conflict measurement produces a more credible fusion outcome compared to other approaches, according to experimental results.
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Acknowledgments
This study was supported by National Development and Reform Commission integrated data service system infrastructure platform construction project (JZNYYY001) and Application of collaborative precision positioning service for mass users (2016YFB0501805-1).
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Zhao, K., Li, L., Chen, Z., Sun, R., Yuan, G. (2022). A Novel Method of Multi-sensor Information Fusion Based on Comprehensive Conflict Measurement. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1491. Springer, Singapore. https://doi.org/10.1007/978-981-19-4546-5_31
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DOI: https://doi.org/10.1007/978-981-19-4546-5_31
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