Multi-Sensor Data Fusion Method Based on Improved Evidence Theory

To achieve autonomous navigation in complex marine environments, unmanned surface vehicles are equipped with a variety of sensors for sensing the surrounding environment and their own state. To address the issue of unsatisfactory multi-sensor information fusion in stochastic uncertain systems with u...

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Bibliographic Details
Published inJournal of marine science and engineering Vol. 11; no. 6; p. 1142
Main Authors Qiao, Shuanghu, Fan, Yunsheng, Wang, Guofeng, Zhang, Haoyan
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2023
Subjects
Accuracy
Algorithms
Analysis
Autonomous navigation
Conflicts
data fusion
Data integration
Decision making
Dempster–Shafer evidence theory
divergence measure
Fuzzy set theory
Fuzzy sets
Marine environment
Methods
multi-sensors
Multisensor fusion
Navigation
Neural networks
Probability
Sensors
Stochasticity
Surface vehicles
Theories
Unmanned vehicles
Vehicles
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Summary:To achieve autonomous navigation in complex marine environments, unmanned surface vehicles are equipped with a variety of sensors for sensing the surrounding environment and their own state. To address the issue of unsatisfactory multi-sensor information fusion in stochastic uncertain systems with unknown disturbances, an improved evidence theory multi-sensor data fusion method is proposed in this article. First, the affiliation function in fuzzy set theory is introduced as a support function to assign initial evidence for multi-sensor data, and the initial evidence is corrected according to the degree of data bias. Second, a divergence measure is employed to measure the degree of conflict and discrepancy among the evidence, and each piece of evidence is allocated proportional weight based on the conflict allocation principle. Finally, the evidence is synthesized through the evidence combination rule, and the data are weighted and summed to obtain the data fusion results. Since it is difficult to obtain dynamic information from multiple sensors carried by unmanned surface vehicles in practical applications, and considering that the proposed method has universal applicability, practical application experiments using previous research demonstrate that the proposed method has higher fusion accuracy than other existing data fusion methods.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse11061142