Signal Conditioning of Low-Cost Gyroscope Using Kalman Filter and Nonlinear Least Square Method

Abstract:

Gyroscopes are important sensors in motion control in equipment such as airplanes, missiles and Segway. Low-cost gyroscopes have problems in signals such as bias, noise and scaling factor that decrease the efficiency of motion control. Therefore this paper is to present signal conditioning of low-cost gyroscopes using a Kalman filter to remove unwanted noise and nonlinear least square method to estimate parameters for compensation errors to the model by comparison with the encoder. The experimental results is shown that Kalman filter and nonlinear least square method can be used in signal conditioning of low-cost gyroscope for a more accurate signal.

• Periodical: Advanced Materials Research (Volumes 622-623)
• Pages: 1519-1523
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.622-623.1519
• Online since: December 2012
• Authors: C. Saraporn, T. Dolwichai, J. Srisertpol, K. Teeka
• Keywords: Kalman Filter (KF), Low-Cost Gyroscope, Nonlinear Least Square Method

References

[1] Leavitt, J., Sideris, A., and Bobrow, J.E., High bandwidth tilt measurement using low-cost sensors, IEEE/ASME Transactions on Mechatronics, vol. 11 (3), pp.1017-1022, June (2006).

DOI: 10.1109/tmech.2006.875571

[2] Kwang Yong Lim, Goh, F.Y.K., Wei Dong, Kim Doang Nguyen, I-Ming Chen, Song Huat Yeo, Duh, H.B.L., Chung Gon Kim, A Wearable Self-Calibrating, Wireless Sensor Network for Body Motion Processing, IEEE International Conference on Robotics and Automation, pp.1017-1022, May 19-23, (2008).

DOI: 10.1109/robot.2008.4543338

[3] Lai, Y. -C., Jan S. -S., Hsiao F. -B., Development of a Low-Cost Attitude and Heading Reference System Using a Three-Axis Rotating Platform, Sensors, vol. 10 (4), pp.2472-2491, March (2010).

DOI: 10.3390/s100402472

[4] Nebot, E., Durrant-Whyte, H., Initial calibration and alignment of low-cost inertial navigation units for land vehicle applications, Journal of Robotic Systems, vol. 16 (2), p.81–92, February (1999).

DOI: 10.1002/(sici)1097-4563(199902)16:2<81::aid-rob2>3.0.co;2-9

[5] Park, S., Horowitz, R., Discrete time adaptive control for a MEMS gyroscope, International Journal of Adaptive Control and Signal Processing, vol. 19, pp.485-503, April (2005).

DOI: 10.1002/acs.868

[6] Chang, B. -S., Lee, J. -G., Kang, T., Design and performance test of digital rebalance loop for MEMS gyroscope, Key Engineering Materials, vol. 326-328, pp.249-252, December (2006).

DOI: 10.4028/www.scientific.net/kem.326-328.249

[7] Fei, J., Ding, H., System Dynamics and Adaptive Control for MEMS Gyroscope Sensor, International Journal of Advanced Robotic System, vol. 7 (4), pp.81-86, (2010).

DOI: 10.5772/10488

[8] Wang, L., Hao, Y., Wang, F., Calibration of Low Cost MEMS Inertial Measurement Unit for an FPGA-based Navigation System, IEEE International Conference on Information and Automation, pp.181-186, June (2011).

DOI: 10.1109/icinfa.2011.5948984

[9] Vaganay, J., et al., Mobile robot attitude estimation by fusion of inertial data, Proceedings IEEE International Conference on Robotics and Automation, vol. 1, p.277–282, May (1993).

DOI: 10.1109/robot.1993.291995

[10] Qian, H., Xia, Q., et al., On modeling of random drift of MEMS gyroscope and design of Kalman filter, International Conference on Mechatronics and Automation (ICMA), pp.4355-4360, August (2009).

DOI: 10.1109/icma.2009.5246601

[11] Sahawneh, L., Development and calibration of low cost MEMS IMU for UAV applications, International Symposium on Mechatronics and Its Applications (ISMA) 5th, pp.1-9, May (2008).

DOI: 10.1109/isma.2008.4648819