The progress in the micro electro mechanical system (MEMS) sensors technology in size, cost, weight, and power consumption allows for new research opportunities in the navigation field. Today, most of smartphones, tablets, and other handheld devices are fully packed with the required sensors for any navigation system such as GPS, gyroscope, accelerometer, magnetometer, and pressure sensors. For seamless navigation, the sensors’ signal quality and the sensors availability are major challenges. Heading estimation is a fundamental challenge in the GPS-denied environments; therefore, targeting accurate attitude estimation is considered significant contribution to the overall navigation error. For that end, this research targets an improved pedestrian navigation by developing sensors fusion technique to exploit the gyroscope, magnetometer, and accelerometer data for device attitude estimation in the different environments based on quaternion mechanization. Results indicate that the improvement in the traveled distance and the heading estimations is capable of reducing the overall position error to be less than 15？m in the harsh environments. 1. Introduction Personal navigation requires technologies that are immune to signal obstructions and fading. One of the major challenges is obtaining a good heading solution in different environments and for different user positions without external absolute reference signals. Part of this challenge arises from the complexity and freedom of movement of a typical handheld user where the heading observability considerably degrades in low-speed walking, making this problem even more challenging. However, for short periods, the relative attitude and heading information is quite reliable. Self-contained systems requiring minimal infrastructure, for example, inertial measurement units (IMUs), stand as a viable option, since pedestrian navigation is not only focused on outdoor navigation but also on indoor navigation. Nowadays, most of the smartphones are programmable and equipped with self-contained, low cost, small size, and power-efficient sensors, such as magnetometers, gyroscopes, and accelerometers. Hence, integrating IMUs navigation solution with a magnetometer-based heading can play an important role in pedestrian navigation in all environments. In the current state of the art in MEMS technology, the accuracy of gyroscopes is not good enough for deriving an absolute heading or relative heading over longer durations of time. However, for short periods, the relative attitude information is quite reliable. Magnetometers,
References
[1]
S. Y. Cho, K. W. Lee, C. G. Park, and J. G. Lee, “A personal navigation system using low-cost MEMS/GPS/Fluxgate,” in Proceedings of the 59th Annual Meeting of the Institute of Navigation and CIGTF 22nd Guidance Test Symposium, Albuquerque, NM, USA, 2003.
[2]
C. Aparicio, Implementation of a Quaternion-Based Kalman Filter for Human Body Motion Tracking Using MARG Sensors, California Naval Postgraduate School, Monterey, Calif, USA, 2004.
[3]
X. Yun and E. R. Bachmann, “Design, implementation, and experimental results of a quaternion-based Kalman filter for human body motion tracking,” IEEE Transactions on Robotics, vol. 22, no. 6, pp. 1216–1227, 2006.
[4]
Honeywell, “DRM 4000 Dead Reckoning Module,” 2009, http://www51.honeywell.com/aero/common/documents/myaerospacecatalog-documents/Missiles-Munitions/DRM4000.pdf.
[5]
J. L. Marins, X. Yun, E. R. Bachmann, R. B. McGhee, and M. J. Zyda, “An extended Kalman filter for quaternion-based orientation estimation using MARG sensors,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 4, pp. 2003–2011, November 2001.
[6]
S. Han and J. Wang, “A novel method to integrate IMU and magnetometers in attitude and heading reference systems,” Journal of Navigation, vol. 64, no. 4, pp. 727–738, 2011.
[7]
S. Emura and S. Tachi, “Compensation of time lag between actual and virtual spaces by multi-sensor integration,” in Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI '94), pp. 463–469, October 1994.
[8]
J. M. Cooke, M. J. Zyda, D. R. Pratt, and R. B. McGhee, “NPSNET: flight simulation dynamic modeling using quaternions,” Presence, vol. 1, pp. 404–420, 1992.
[9]
E. Foxlin, “Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter,” in Proceedings of the IEEE Virtual Reality Annual International Symposium, pp. 185–194, April 1996.
[10]
P. Setoodeh, A. Khayatian, and E. Farjah, “Attitude estimation by separate-bias Kalman filter-based data fusion,” Journal of Navigation, vol. 57, no. 2, pp. 261–273, 2004.
[11]
S.-W. Liu, Z.-N. Zhang, and J. C. Hung, “High accuracy magnetic heading system composed of fluxgate magnetometers and a microcomputer,” in Proceedings of the IEEE National Aerospace and Electronics Conference (NAECON '89), pp. 148–152, May 1989.
[12]
B. Hoff and R. Azuma, “Autocalibration of an electronic compass in an outdoor augmented reality system,” in Proceedings of IEEE and ACM International Symposium on Augmented Reality, pp. 159–164, Munich, Germany, October 2000.
[13]
D. Gebre-Egziabher, G. H. Elkaim, J. D. Powell, and B. W. Parkinson, “A non-linear, two step estimation algorithm for calibrating solid-state Strapdown magnetometers,” in Proceedings of the 8th International St. Petersburg Conference on Navigation Systems, IEEE/AIAA, St. Petersburg, Russia, 2001.
[14]
C. L. Tsai, Motion-based differential carrier phase positioning: heading determination and its applications [Ph.D. dissertation], National Taiwan University of Science and Technology, 2006.
[15]
S. Kwanmuang, L. Ojeda, and J. Borenstein, “Magnetometer-enhanced personal locator for tunnels and GPS-denied outdoor environments,” in Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense X, Proceedings of SPIE, Orlando, Fla, USA, April 2011.
[16]
L. Xue, W. Yuan, H. Chang, and C. Jiang, “MEMS-based multi-sensor integrated attitude estimation technology for MAV applications,” in Proceedings of the 4th IEEE International Conference on Nano/Micro Engineered and Molecular Systems (NEMS '09), pp. 1031–1035, January 2009.
[17]
W. F. Storms and J. F. Raquet, “Magnetic field aided vehicle tracking,” in Proceedings of the 22nd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS '09), pp. 1767–1774, September 2009.
[18]
E. R. Bachmann, X. Yun, and C. W. Peterson, “An investigation of the effects of magnetic variations on inertial/magnetic orientation sensors,” in Proceedings of IEEE International Conference on Robotics and Automation (ICRA '04), pp. 1115–1122, May 2004.
[19]
N. El-Sheimy, Inertial Techniques and INS/DGPS Integration, Lecture Notes ENGO 623, Department of Geomatics Engineering, the University of Calgary, Calgary, Canada, 2012.
[20]
A. S. Ali, S. Siddharth, Z. Syed, C. L. Goodall, and N. El-Sheimy, “An efficient and robust maneuvering mode to calibrate low cost magnetometer for improved heading estimation for pedestrian navigation,” Journal of Applied Geodesy, vol. 7, no. 1, pp. 65–73, 2013.
[21]
C. C. Finlay, S. Maus, C. D. Beggan et al., “International geomagnetic reference field: the eleventh generation,” Geophysical Journal International, vol. 183, no. 3, pp. 1216–1230, 2010.
[22]
J. B. Kuipers, Quaternions and Rotation Sequences, Princeton University Press, Princeton, NJ, USA, 1999.
[23]
R. G. Brown and P. Y. Hwang, Introduction to Random Signals and Applied Kalman Filtering, Wiley, New York, NY, USA, 1992.
[24]
A. Gelb, Applied Optimal Estimation, MIT Press, 1974.
[25]
H. J. Luinge and P. H. Veltink, “Measuring orientation of human body segments using miniature gyroscopes and accelerometers,” Medical and Biological Engineering and Computing, vol. 43, no. 2, pp. 273–282, 2005.
