Three-Dimensional Angles Measurement using IME based on MEMS Technology by Adaptive Kalman Filter

Document Type : Original Article

Authors

iran univer. of science and technology

Abstract

Three-dimensional angular measurements are used in a large number of applications, such as           positioning systems, robotics, motion analysis, control analysis, and so on. The sensors used for this       purpose are the accelerometer and the gyroscope. The angle measurement in the accelerometer sensor is based on the vector component of the gravitational acceleration on the sensor axes and the trigonometric relations. For this reason, only a quasi-static limit can be obtained from the almost precise angle. In      dynamic mode, angle measurement using this sensor is accompanied by large errors, due to the effect of other accelerations, in addition to gravity acceleration. The angle can also be obtained by integrating the gyroscope output, but in practice due to the problem of gyroscope drift, the measurement angle is far from the actual value, with increasing error over time. In this paper, we have been able to cover the weaknesses of each of these sensors using a combination of accelerometer and gyroscope features by an adaptive     Kalman filter. The proposed adaptive filter is able to adjust itself to the static and dynamic conditions, so that it can combine the output information of the sensors properly. To evaluate the proposed structure,    dynamic and static experiments were conducted on an IMU based on MEMS technology. The results show that compared to ordinary Kalman filter, the RMS error of angle measurement in the proposed scheme has an improvement of 34% in the static mode and 34.3% and 29.8% in dynamic modes of pitch and roll,     respectively.
 

Keywords


[1]   S. B. Lazarus, I. Ashokaraj, A. Tsourdos, P. M. Silson, N. Aouf, and B. A. White, “Vehicle Localization using Sensors Data Fusion via Integration of Covariance Intersection and Interval Analysis,” IEEE Sensors Journal, vol. 7, no. 9, pp. 1302-1314, 2007.##
[2]   M. Ghanbari and M. J. Yazdanpanah, “Delay Compensation of Tilt Sensors based on MEMS Accelerometer using Data Fusion Technique,” IEEE Sensors Journal, vol. 15, no. 3, pp. 1959-1966, 2015##.
[3]   S. Luczak, W. Oleksiuk, and M. Bodnicki, “Sensing Tilt with MEMS Accelerometers,” IEEE Sensors Journal, vol. 6, no. 6, pp. 1669-1675, 2006##.
[4]   F. Alam, Z. ZhaiHe, and H. Jia, “A Comparative Analysis of Orientation Estimation Filters using MEMS based IMU,” in Proceedings of the International Conference on Research in Science, Engineering and Technology, Dubai, UAE, pp. 21-22, 2014##.
 [5]   D. Gebre-Egziabher, R. C. Hayward, and J. D. Powell, “A Low-Cost GPS/Inertial Attitude Heading Reference System (AHRS) for General Aviation Applications,” in Position Location and Navigation Symposium, pp. 518-525, 1998##.
[6]   S. O. Madgwick, A. J. Harrison, and R. Vaidyanathan, “Estimation of IMU and MARG Orientation using a Gradient Descent Algorithm,” in IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1-7, 2011##.
[7]   E. R. Bachmann, I. Duman, U. Usta, R. B. McGhee, X. Yun, and M. Zyda, “Orientation Tracking for Humans and Robots using Inertial Sensors,” in International Conference on Computational Intelligence in Robotics and Automation, pp. 187-194, 1999##.
 [8]  E. Foxlin, “Inertial Head-Tracker Sensor Fusion by a Complementary Separate-Bias Kalman Filter,” in Virtual Reality Annual International Symposium, pp. 185-194, 1996##.
[9]   J. N. Lim, “Design of Attitude Estimation System for Micro Aerial Vehicle,” Mechanical and Aerospace Engineering, Seoul National University, Korea, Seoul, 1993##.
[10] N. Miller, O. C. Jenkins, M. Kallmann, and M. J. Mataric, “Motion Capture from Inertial Sensing for Untethered Humanoid Teleoperation,” in IEEE/RAS International Conference on Humanoid Robots, vol. 2, pp. 547-565, 2004##.
 [11] F. M. Mirzaei and S. I. Roumeliotis, “A Kalman Filter-based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation,” IEEE Transactions on Robotics, vol. 24, no. 5, pp. 1143-1156, 2008##.
[12] D. Roetenberg, H. J. Luinge, C. T. Baten, and P. H. Veltink, “Compensation of Magnetic Disturbances Improves Inertial and Magnetic Sensing of Human Body Segment Orientation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, no. 3, pp. 395-405, 2005##.
[13] S. Sabatelli, M. Galgani, L. Fanucci, and A. Rocchi, “A Double Stage Kalman Filter for Sensor Fusion and Orientation Tracking in 9D IMU,” in Sensors Applications Symposium (SAS), pp. 1-5, 2012##.
[14] R. Faragher, “Understanding the Basis of the Kalman Filter via a Simple and Intuitive Derivation,” IEEE Magazine on Signal Processing, vol. 29, no. 5, pp. 128-132, 2012##.
 [15] M. Ghanbari and M. J. Yazdanpanah, “Delay Compensation of Tilt Sensors Based on MEMS Accelerometer Using Data Fusion,” IEEE Sensors Journal, vol. 15, no. 3, pp. 1959-1966, 2015##.
[16] J. Lim and D. Hong, “Cost Reference Particle Filtering Approach to High-Bandwidth Tilt Estimation,” IEEE Transactions on Industrial Electronics, vol. 57, no. 11, pp. 3830-3839, 2010##.
[17] S. Jung, H. T. Cho, and T. C. Hsia, “Neural Network Control for Position Tracking of a Two-Axis Inverted Pendulum System: Experimental Studies,” IEEE Transactions on Neural Networks, vol. 18, no. 4, pp. 1042-1048, 2007##.
[18] C. W. Kang and C. G. Park, “Attitude Estimation with Accelerometers and Gyros using Fuzzy Tuned Kalman Filter,” in IEEE International Conference on Control Conference (ECC), pp. 3713-3718, 2009##.
  [19] H. Sun, J. Fu, X. Yuan, and W. Tang, “Analysis of the Kalman Filter with Different INS Error Models for GPS/INS Integration in Aerial Remote Sensing Applications,” The International Archives of the Phogrammetry, Remote Sensing and Spatial Information Sciences, vol. 37, pp. 883-890, 2008##.
[20] D. Simon, “Kalman Filtering,” Embedded Systems Programming, vol. 14, no. 6, pp. 72-79, 2001##.
[21] P. Gui, L. Tang, and S. Mukhopadhyay, “MEMS based IMU for Tilting Measurement: Comparison of Complementary and Kalman Filter based Data Fusion,” in IEEE International Conference on Industrial Electronics and Applications (ICIEA), pp. 2004-2009, 2015##.
 [22] M. R. Remus, “Fuzzy Logic Applied to Adaptive Kalman Filtering,” University of Nebraska - Lincoln, 1992.
[23] M. M. Fateh and S. Khorashadizadeh, “Robust Control of Electrically Driven Robots by Adaptive Fuzzy Estimation of Uncertainty,” Nonlinear Dynamics, vol. 69, no. 3, pp.        1465-1477, 2012.