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A Reliability-Based Method to Sensor Data Fusion
Wen Jiang1, Miaoyan Zhuang2, Chunhe Xie3
1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China. jiangwen@nwpu.edu.cn.
Sensors (Basel, Switzerland)
|July 6, 2017
Summary
This study introduces a new reliability-based basic belief assignment (BBA) method for multi-sensor data fusion. It addresses the limitations of existing approaches by incorporating information source reliability alongside uncertainty.
Area of Science:
- Computer Science
- Information Science
- Engineering
Background:
- Multi-sensor data fusion is crucial in various applications.
- Dempster-Shafer evidence theory is a common framework.
- Determining basic belief assignments (BBAs) remains a challenge.
Purpose of the Study:
- To address the limitation of existing BBA methods that overlook information source reliability.
- To propose a novel method that accounts for both uncertainty and partial reliability in information.
- To enhance the accuracy and robustness of data fusion.
Main Methods:
- Developed a new method for representing BBAs that incorporates associated reliabilities.
- Introduced the concept of 'reliability-based BBA'.
- Validated the proposed method through several examples.
Main Results:
- The proposed reliability-based BBA method effectively integrates information source reliability.
- Demonstrated the validity of the new approach in handling uncertain and partially reliable information.
- Showcased improved performance in data fusion scenarios.
Conclusions:
- The reliability-based BBA method offers a more comprehensive approach to data fusion.
- This method provides a practical solution for determining BBAs in real-world scenarios.
- The findings contribute to advancing multi-sensor data fusion techniques.
