MEMS based IMU for tilting measurement: comparison of complementary and kalman filter based data fusion

Pengfei Gui, Liqiong Tang, Subhas Mukhopadhyay

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

Abstract

This research investigates real time tilting measurement using Micro-Electro-Mechanical-system (MEMS) based inertial measurement unit (IMU). Accelerometers suffer from errors caused by external accelerations that sums to gravity and make accelerometers based tilting sensing unreliable and inaccurate. Gyroscopes can offset such drawbacks but have data drifting problems. This paper presents a study on complementary and Kalman filter for tilting measurement using MEMS based IMU. The complementary filter algorithm uses low-pass filter and high-pass filter to deal with the data from accelerometer and gyroscope while Kalman filter takes the tilting angle and gyroscope bias as system states, combining the angle derived from the accelerometer to estimate the tilting angle. The study carried out both static and dynamic experiments. The results showed that both Complementary and Kalman filter were less sensitive to variations and almost no signal coupling phenomenon and able to obtain smooth and accurate results.

LanguageEnglish
Title of host publicationProceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2004-2009
Number of pages6
ISBN (Electronic)9781467373173, 9781479983896
ISBN (Print)9781479984671
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015 - Auckland, New Zealand
Duration: 15 Jun 201517 Jun 2015

Other

Other10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015
CountryNew Zealand
CityAuckland
Period15/06/1517/06/15

Fingerprint

Units of measurement
Data fusion
Accelerometers
Kalman filters
Gyroscopes
High pass filters
Wave filters
Low pass filters
Time measurement
Gravitation
Experiments

Keywords

  • complementary filter
  • IMU
  • tilt measurement
  • Kalman filter
  • data fusion

Cite this

Gui, P., Tang, L., & Mukhopadhyay, S. (2015). MEMS based IMU for tilting measurement: comparison of complementary and kalman filter based data fusion. In Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015) (pp. 2004-2009). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICIEA.2015.7334442
Gui, Pengfei ; Tang, Liqiong ; Mukhopadhyay, Subhas. / MEMS based IMU for tilting measurement : comparison of complementary and kalman filter based data fusion. Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015). Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2015. pp. 2004-2009
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Gui, P, Tang, L & Mukhopadhyay, S 2015, MEMS based IMU for tilting measurement: comparison of complementary and kalman filter based data fusion. in Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015). Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 2004-2009, 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015, Auckland, New Zealand, 15/06/15. https://doi.org/10.1109/ICIEA.2015.7334442

MEMS based IMU for tilting measurement : comparison of complementary and kalman filter based data fusion. / Gui, Pengfei; Tang, Liqiong; Mukhopadhyay, Subhas.

Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015). Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2015. p. 2004-2009.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

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Gui P, Tang L, Mukhopadhyay S. MEMS based IMU for tilting measurement: comparison of complementary and kalman filter based data fusion. In Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2015. p. 2004-2009 https://doi.org/10.1109/ICIEA.2015.7334442