Drift-Free Indoor Pedestrian Dead Reckoning using Empirical Mode Decomposition

In recent years pedestrian dead reckoning (PDR) has been a popular indoor localization technique that takes advantage of inertial navigation sensors (INS) of smartphones due to its availability. Despite the convenience of INS, noise has been a recurring problem in PDR which encourages many researches on noise filtering. This paper proposes a model that utilizes empirical mode decomposition (EMD) as the noise filter. EMD is an unconventional filter being applied in PDR to remove noise by processing the raw signal before implementing step counts, step lengths and heading estimation. The experiment made using the proposed model was able to perform the standard PDR technique without overlaps in heading estimation.

[1]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[2]  Gabriel Rilling,et al.  on the Influence of Sampling on the Empirical Mode Decomposition , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[3]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  Qinghua Zhang,et al.  An EMD threshold de-noising method for inertial sensors , 2014 .

[5]  Vincent Bonnet,et al.  Estimate of lower trunk angles in pathological gaits using gyroscope data. , 2013, Gait & posture.

[6]  Jeffrey M. Hausdorff,et al.  Self-Organized Biological Dynamics and Nonlinear Control: Fractal mechanisms in neuronal control: human heartbeat and gait dynamics in health and disease , 2000 .

[7]  Yajie Qin,et al.  The application of EMD in activity recognition based on a single triaxial accelerometer. , 2015, Bio-medical materials and engineering.

[8]  Agata Brajdic,et al.  Walk detection and step counting on unconstrained smartphones , 2013, UbiComp.

[9]  Satoru Morita,et al.  Analysis of stroke patient walking dynamics using a tri-axial accelerometer. , 2009, Gait & posture.

[10]  Robert Piché,et al.  A Survey of Selected Indoor Positioning Methods for Smartphones , 2017, IEEE Communications Surveys & Tutorials.

[11]  Naser El-Sheimy,et al.  PDR/INS/WiFi Integration Based on Handheld Devices for Indoor Pedestrian Navigation , 2015, Micromachines.

[12]  Hendrik Johannes Luinge,et al.  Inertial sensing of human movement , 2002 .

[13]  Robert Harle,et al.  A Survey of Indoor Inertial Positioning Systems for Pedestrians , 2013, IEEE Communications Surveys & Tutorials.