Abstract
Micro-electro-mechanical system (MEMS) sensors are widely used in many applications due to their low cost, low power consumption, small size and light weight. Such MEMS sensors which are usually called multi-sensors include accelerometers, gyroscopes, magnetometers and barometers. In this research, Samsung Galaxy Note is used as the MEMS multi-sensors platform for pedestrian navigation. It contains a three-axis accelerometer, a three-axis gyroscope, a three-axis magnetometer and GPS receiver. Pedestrian Dead Reckoning (PDR) algorithms which include step detection, stride length estimation, heading estimation and PDR mechanization are carefully discussed in this paper. GPS solution is the major aiding source to reduce the MEMS IMU position, velocity and attitude errors when GPS signals are available. Magnetometers are also used to reduce the attitude errors of gyroscopes if there are no environment disturbances. A loosely-coupled extended Kalman Filter is implemented in the paper to fuse all the information to obtain the position result. Two typical scenarios are tested and analyzed in this paper: walking from outdoor to indoor and indoor walking. The MEMS multi-sensors system works well for both scenarios. To conclude, algorithms of MEMS multi-sensors system can provide an accurate, reliable and continuous result for pedestrian navigation on the platform of smart phone.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mezentsev O (2005) Sensor aiding of HSGPS pedestrian navigation. PhD Thesis, Department of geomatics engineering, University of Calgary, Canada, UCGE Report No. 20212
Jimenez AR, Seco F, Priteo C, Guevara J (2009) A comparison of pedestrian dead-reckoning algorithms using a low-cost MEMS IMU. WISP 2009, Budapest Hungary
Zhao X, Syed Z, Wright DB, El-Sheimy N (2009) An economical and effective multi-sensor integration for portable navigation system. In: Proceeding of the institute of navigation GNSS 2009 conference, Savannah
Weinberg H (2002) Using the ADXL202 in Pdeometer and personal navigation applications. Analog devices AN-602 application note
El-Sheimy N (2012) Inertial techniques and INS/DGPS integration, ENGO 623-course notes. Department of Geomatics Engineering, University of Calgary, Canada
Ladetto Q, Gabaglio V, Merminod B (2001) Combining gyroscopes, magnetic compass and GPS for pedestrian navigation. International symposium on kinematic systems in geodesy, geomatics, and navigation, Banff, pp 205–212
Gebre-Egziabher D, Elkaim GH, Powell JD, Parkinson BW (2001) A non-linear, two-step estimation algorithm for calibrating solid-state strapdown magnetometers. In: Proceedings of the international conference on integrated navigation systems, St. Petersburg, pp 290–297
Li Q, Dempster A, Li B, Wang J, Rizos C (2006) A low-cost attitude heading reference system by combination of GPS and magnetometers and MEMS inertial sensors for mobile applications. Journal of Global Positioning System
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhuang, Y., Chang, H.W., El-Sheimy, N. (2013). A MEMS Multi-Sensors System for Pedestrian Navigation. In: Sun, J., Jiao, W., Wu, H., Shi, C. (eds) China Satellite Navigation Conference (CSNC) 2013 Proceedings. Lecture Notes in Electrical Engineering, vol 245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37407-4_60
Download citation
DOI: https://doi.org/10.1007/978-3-642-37407-4_60
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37406-7
Online ISBN: 978-3-642-37407-4
eBook Packages: EngineeringEngineering (R0)