Research on the Zero Drift of Sensor Based on Data Fusion Technology

Xiao Ming Li; Zhi Shu Feng; Zhong Chun Cai; Bin Xie; Chang Jie Liu

doi:10.4028/www.scientific.net/AMR.546-547.696

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 546-547Research on the Zero Drift of Sensor Based on Data...

Research on the Zero Drift of Sensor Based on Data Fusion Technology

Abstract:

The zero drift of sensor and its solution are presented in this paper. The basic principle of data fusion with two-dimensional regression analysis is expounded and the experimental data of any pressure sensor are fused with the two-dimensional regression analysis. Besides, the input-output mathematical model of sensor under the influence of temperature is established. At last, the linear, fitting and fusion methods are compared.

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Periodical:

Advanced Materials Research (Volumes 546-547)

Pages:

696-701

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.546-547.696

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Online since:

July 2012

Authors:

Xiao Ming Li, Zhi Shu Feng, Zhong Chun Cai, Bin Xie, Chang Jie Liu

Keywords:

Data Fusion, Sensor, Zero Drift

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References

[1] Georgiadis Leonidas, Tassiulas Leandros. Optimal overload response in sensor networks[J]. IEEE Transactions on Information Theory, V. 52, N. 6, June, 2006: 2684~2696.

DOI: 10.1109/tit.2006.874401

Google Scholar

[2] Eason Richard, Kamata Seiichiro. Least squares-based data fusion strategies and robotic applications[J]. Proceedings of SPIE-The International Society for Optical Engineering, V. 1383, 1991: 566-573.

Google Scholar

[3] Imam Neena, Barhen Jacob, Glover Charles W. Performance evaluation of time-weighted backvalues least squares vs. NOGA track estimators via sensor data fusion and track fusion for small target detection applications[J]. Proceedings of SPIE-The International Society for Optical Engineering, V. 5913, Signal and Data Processing of Small Targets 2005, 2005: 1-12.

DOI: 10.1117/12.617690

Google Scholar

[4] Odin Taylor, John Tait, John MacIntyre. Improved classification for a data fusing Kononen self organizing map using dynamic thresholding technique[J]. The 16th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 1999: 1-10.

Google Scholar

[5] Mohan Nirala. Multi-sensor data fusion and companson of total column ozone[J]. International Journal of Remote Sensing, (2008).

DOI: 10.1080/01431160801927202

Google Scholar