Multi-sensor data fusion algorithm based on the improved weighting factor

Aiming at the decrease of the accuracy of fusion data caused by the abnormal value and noise interference in the multi-sensor observations, this paper proposes a multi-sensor data fusion algorithm based on improved weighting factors. Firstly, the Dixon criterion is used to eliminate outliers in obse...

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Vydáno v:Journal of physics. Conference series Ročník 1754; číslo 1; s. 12227 - 12232
Hlavní autoři: Yu, Yongjin, Liu, Xiaoqing, Xu, Chuannuo, Cheng, Xuezhen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.02.2021
Témata:
Adaptive algorithms
Algorithms
Data integration
Kalman filters
Multisensor fusion
Noise reduction
Outliers (statistics)
Physics
Sensors
Soil analysis
Weighting
ISSN:1742-6588, 1742-6596
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Shrnutí:Aiming at the decrease of the accuracy of fusion data caused by the abnormal value and noise interference in the multi-sensor observations, this paper proposes a multi-sensor data fusion algorithm based on improved weighting factors. Firstly, the Dixon criterion is used to eliminate outliers in observations to avoid data containing gross errors. Then the Kalman filter algorithm is used to effectively reduce the noise impact caused by various reasons and provides the optimal data for weighted data fusion. Finally, an improved weighted fusion algorithm is used to comprehensively consider the nature of the sensor and the influence of various factors in the measurement process to obtain the best fusion data. The simulation analysis of the soil humidity in the greenhouse shows that the error of the multi-sensor data fusion algorithm based on the improved weighting factor is maintained at 0.04%-0.18%. Compared with the adaptive weighted fusion algorithm, the error of this algorithm is reduced by 0.12%, which verifies the algorithm's effectiveness.
Bibliografie:ObjectType-Conference Proceeding-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1754/1/012227