Drift-Free Position Estimation of Periodic or Quasi-Periodic Motion Using Inertial Sensors
Position sensing with inertial sensors such as accelerometers and gyroscopes usually requires other aided sensors or prior knowledge of motion characteristics to remove position drift resulting from integration of acceleration or velocity so as to obtain accurate position estimation. A method based...
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| Format: | Article |
| Language: | English |
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MDPI AG
2011-05-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/11/6/5931/ |
| _version_ | 1798041412204232704 |
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| author | Wei Tech Ang Win Tun Latt Kalyana Chakravarthy Veluvolu |
| author_facet | Wei Tech Ang Win Tun Latt Kalyana Chakravarthy Veluvolu |
| author_sort | Wei Tech Ang |
| collection | DOAJ |
| description | Position sensing with inertial sensors such as accelerometers and gyroscopes usually requires other aided sensors or prior knowledge of motion characteristics to remove position drift resulting from integration of acceleration or velocity so as to obtain accurate position estimation. A method based on analytical integration has previously been developed to obtain accurate position estimate of periodic or quasi-periodic motion from inertial sensors using prior knowledge of the motion but without using aided sensors. In this paper, a new method is proposed which employs linear filtering stage coupled with adaptive filtering stage to remove drift and attenuation. The prior knowledge of the motion the proposed method requires is only approximate band of frequencies of the motion. Existing adaptive filtering methods based on Fourier series such as weighted-frequency Fourier linear combiner (WFLC), and band-limited multiple Fourier linear combiner (BMFLC) are modified to combine with the proposed method. To validate and compare the performance of the proposed method with the method based on analytical integration, simulation study is performed using periodic signals as well as real physiological tremor data, and real-time experiments are conducted using an ADXL-203 accelerometer. Results demonstrate that the performance of the proposed method outperforms the existing analytical integration method. |
| first_indexed | 2024-04-11T22:22:05Z |
| format | Article |
| id | doaj.art-9c091e14e2504595865957a942b586c6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:22:05Z |
| publishDate | 2011-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9c091e14e2504595865957a942b586c62022-12-22T04:00:06ZengMDPI AGSensors1424-82202011-05-011165931595110.3390/s110605931Drift-Free Position Estimation of Periodic or Quasi-Periodic Motion Using Inertial SensorsWei Tech AngWin Tun LattKalyana Chakravarthy VeluvoluPosition sensing with inertial sensors such as accelerometers and gyroscopes usually requires other aided sensors or prior knowledge of motion characteristics to remove position drift resulting from integration of acceleration or velocity so as to obtain accurate position estimation. A method based on analytical integration has previously been developed to obtain accurate position estimate of periodic or quasi-periodic motion from inertial sensors using prior knowledge of the motion but without using aided sensors. In this paper, a new method is proposed which employs linear filtering stage coupled with adaptive filtering stage to remove drift and attenuation. The prior knowledge of the motion the proposed method requires is only approximate band of frequencies of the motion. Existing adaptive filtering methods based on Fourier series such as weighted-frequency Fourier linear combiner (WFLC), and band-limited multiple Fourier linear combiner (BMFLC) are modified to combine with the proposed method. To validate and compare the performance of the proposed method with the method based on analytical integration, simulation study is performed using periodic signals as well as real physiological tremor data, and real-time experiments are conducted using an ADXL-203 accelerometer. Results demonstrate that the performance of the proposed method outperforms the existing analytical integration method.http://www.mdpi.com/1424-8220/11/6/5931/inertial sensorsintegration driftperiodic motionphase-shiftFourier linear combiner |
| spellingShingle | Wei Tech Ang Win Tun Latt Kalyana Chakravarthy Veluvolu Drift-Free Position Estimation of Periodic or Quasi-Periodic Motion Using Inertial Sensors Sensors inertial sensors integration drift periodic motion phase-shift Fourier linear combiner |
| title | Drift-Free Position Estimation of Periodic or Quasi-Periodic Motion Using Inertial Sensors |
| title_full | Drift-Free Position Estimation of Periodic or Quasi-Periodic Motion Using Inertial Sensors |
| title_fullStr | Drift-Free Position Estimation of Periodic or Quasi-Periodic Motion Using Inertial Sensors |
| title_full_unstemmed | Drift-Free Position Estimation of Periodic or Quasi-Periodic Motion Using Inertial Sensors |
| title_short | Drift-Free Position Estimation of Periodic or Quasi-Periodic Motion Using Inertial Sensors |
| title_sort | drift free position estimation of periodic or quasi periodic motion using inertial sensors |
| topic | inertial sensors integration drift periodic motion phase-shift Fourier linear combiner |
| url | http://www.mdpi.com/1424-8220/11/6/5931/ |
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