Cosine distance measure in Dempster-Shafer evidence theory based on fractal entropy

Yanfei Wei; Xiaozhuan Gao; Mingqi Shan; XiaoLin Liu; Yiling Liu

doi:10.1117/12.3033030

5 July 2024 Cosine distance measure in Dempster-Shafer evidence theory based on fractal entropy

Yanfei Wei, Xiaozhuan Gao, Mingqi Shan, XiaoLin Liu, Yiling Liu

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318434 (2024) https://doi.org/10.1117/12.3033030
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Multi-sensor data fusion technology plays a crucial role in practical applications. Dempster-Schafer's evidence theory is widely employed in the field of information fusion due to its flexibility and effectiveness in modeling and processing uncertain information. To better quantify the differences among different pieces of evidence, this white paper presents illustrative examples within the framework of Dempster-Shafer's proof theory, utilizing fractal entropy as an intuitive indicator of changes in information. We propose a technique for measuring sine distance that utilizes maximum belief entropy to partition the mass function, allowing for one-time preprocessing using fractal entropy division. Subsequently, we utilize the cosine function to analyze disparities in the mass function post-preprocessing. Furthermore, this paper elucidates several characteristics of our novel distance measurement method and analyzes its advantages through numerical examples. Finally, we apply the proposed measurement approach to information fusion and evaluate its efficacy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanfei Wei, Xiaozhuan Gao, Mingqi Shan, XiaoLin Liu, and Yiling Liu "Cosine distance measure in Dempster-Shafer evidence theory based on fractal entropy", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318434 (5 July 2024); https://doi.org/10.1117/12.3033030

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KEYWORDS

Distance measurement

Fractal analysis

Information fusion

Data fusion

Data modeling

Sensors

Data processing

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