Study of grey model theory and neural network algorithm for improving dynamic measure precision in low cost IMU

Yu Liu; Jun Liu; Dengfeng Li; Leilei Li; Yanbin Sun; Yingjun Pan

doi:10.1109/CSIE.2009.980

Study of grey model theory and neural network algorithm for improving dynamic measure precision in low cost IMU

Yu Liu^*, Jun Liu, Dengfeng Li, Leilei Li, Yanbin Sun, Yingjun Pan

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The sensors' output data must be optimized because of the zero output varies along with time and temperature in the dynamic measuring accuracy of low cost inertial measurement unit (IMU). Two steps are done to achieve the designed precision. Firstly, the Grey model theory is proposed for the gyro's null drift output data process. Secondly, the RBF neural network is presented to compensate the gyro's null drift. Experiment proved that the mean variance of the zero drifting depresses from 0.0086°/ s to 0.0004°/ s and the deviation is only 30.8% of original sampled data, when the new error compensation algorithm is applied. The compensating algorithm raises the measure precision of IMU, whose static accuracy reaches to ±0.1° and dynamic accuracy is 1° (rms), and the cost is low.

Original language	English
Title of host publication	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
Pages	234-238
Number of pages	5
DOIs	https://doi.org/10.1109/CSIE.2009.980
Publication status	Published - 2009
Externally published	Yes
Event	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 - Los Angeles, CA, United States Duration: 31 Mar 2009 → 2 Apr 2009

Publication series

Name	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
Volume	5

Conference

Conference	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
Country/Territory	United States
City	Los Angeles, CA
Period	31/03/09 → 2/04/09

Access to Document

10.1109/CSIE.2009.980

Cite this

Liu, Y., Liu, J., Li, D., Li, L., Sun, Y., & Pan, Y. (2009). Study of grey model theory and neural network algorithm for improving dynamic measure precision in low cost IMU. In 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 (pp. 234-238). Article 5170532 (2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009; Vol. 5). https://doi.org/10.1109/CSIE.2009.980

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title = "Study of grey model theory and neural network algorithm for improving dynamic measure precision in low cost IMU",

abstract = "The sensors' output data must be optimized because of the zero output varies along with time and temperature in the dynamic measuring accuracy of low cost inertial measurement unit (IMU). Two steps are done to achieve the designed precision. Firstly, the Grey model theory is proposed for the gyro's null drift output data process. Secondly, the RBF neural network is presented to compensate the gyro's null drift. Experiment proved that the mean variance of the zero drifting depresses from 0.0086°/ s to 0.0004°/ s and the deviation is only 30.8% of original sampled data, when the new error compensation algorithm is applied. The compensating algorithm raises the measure precision of IMU, whose static accuracy reaches to ±0.1° and dynamic accuracy is 1° (rms), and the cost is low.",

author = "Yu Liu and Jun Liu and Dengfeng Li and Leilei Li and Yanbin Sun and Yingjun Pan",

year = "2009",

doi = "10.1109/CSIE.2009.980",

language = "English",

isbn = "9780769535074",

series = "2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009",

pages = "234--238",

booktitle = "2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009",

note = "2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 ; Conference date: 31-03-2009 Through 02-04-2009",

}

Liu, Y, Liu, J, Li, D, Li, L, Sun, Y & Pan, Y 2009, Study of grey model theory and neural network algorithm for improving dynamic measure precision in low cost IMU. in 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009., 5170532, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, vol. 5, pp. 234-238, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, Los Angeles, CA, United States, 31/03/09. https://doi.org/10.1109/CSIE.2009.980

Study of grey model theory and neural network algorithm for improving dynamic measure precision in low cost IMU. / Liu, Yu; Liu, Jun; Li, Dengfeng et al.
2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009. 2009. p. 234-238 5170532 (2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009; Vol. 5).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Liu, Jun

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AU - Pan, Yingjun

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AB - The sensors' output data must be optimized because of the zero output varies along with time and temperature in the dynamic measuring accuracy of low cost inertial measurement unit (IMU). Two steps are done to achieve the designed precision. Firstly, the Grey model theory is proposed for the gyro's null drift output data process. Secondly, the RBF neural network is presented to compensate the gyro's null drift. Experiment proved that the mean variance of the zero drifting depresses from 0.0086°/ s to 0.0004°/ s and the deviation is only 30.8% of original sampled data, when the new error compensation algorithm is applied. The compensating algorithm raises the measure precision of IMU, whose static accuracy reaches to ±0.1° and dynamic accuracy is 1° (rms), and the cost is low.

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Liu Y, Liu J, Li D, Li L, Sun Y, Pan Y. Study of grey model theory and neural network algorithm for improving dynamic measure precision in low cost IMU. In 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009. 2009. p. 234-238. 5170532. (2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009). doi: 10.1109/CSIE.2009.980

Study of grey model theory and neural network algorithm for improving dynamic measure precision in low cost IMU

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