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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Study on Data Fusion Processing Algorithm of Marine Sensor Based on Information Entropy
verfasst von : Hao Gao, Lin Cao, Lei Yang
Erschienen in: Communications, Signal Processing, and Systems
Verlag: Springer Singapore
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Abstract
Addressing the issue of data processing in the measurement of optical dissolved oxygen sensor, a data fusion processing algorithm based on information entropy was designed and presented. Firstly, the probability distribution of discrete sample data was estimated based on the maximum entropy method. Then, the effective confidence interval was calculated based on the sample measurement uncertainty, which would be used to eliminate the gross error. Lastly, the effective sample data were fused based on information entropy. The calibration experiment of typical marine sensor was taken as an example to verify the effectiveness of the algorithm. Compared with other methods, the result of this algorithm had the absolute error of 0.01 and mean square error of 0.0189. The result indicates that it could overcome the calibration data pollution caused by various subjective factors and improve the stability and reliability of sensor measurement data.