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Proceedings Paper

Characterizing wave propagation to improve indoor step-level person localization using floor vibration
Author(s): Mostafa Mirshekari; Shijia Pan; Pei Zhang; Hae Young Noh
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Paper Abstract

The objective of this paper is to characterize frequency-dependent wave propagation of footstep induced floor vibration to improve robustness of vibration-based occupant localization. Occupant localization is an essential part of many smart structure applications (e.g., energy management, patient/customer tracking, etc.). Exist- ing techniques include visual (e.g. cameras and IR sensors), acoustic, RF, and load-based approaches. These approaches have many deployment and operational requirements that limits their adaptation. To overcome these limitations, prior work has utilized footstep-induced vibrations to allow sparse sensor configuration and non-intrusive detection. However, frequency dependent propagation characteristics and low signal-to-noise ratio (SNR) of footstep-induced vibrations change the shape of the signal. Furthermore, estimating the wave propagation velocity for forming the multilateration equations and localizing the footsteps is a challenging task. They, in turn, lead to large errors of localization. In this paper, we present a structural vibration based indoor occupant localization technique using improved time-difference-of-arrival between multiple vibration sensors. In particular we overcome signal distortion by decomposing the signal into frequency components and focusing on high energy components for accurate indoor localization. Such decomposition leverages the frequency-specific propagation characteristics and reduces the effect of low SNR (by choosing the components of highest energy). Furthermore, we develop a velocity calibration method that finds the optimal velocity which minimizes the localization error. We validate our approach through field experiments in a building with human participants. We are able to achieve an average localization error of less than 0.21 meters, which corresponds to a 13X reduction in error when compared to the baseline method using raw data.

Paper Details

Date Published: 20 April 2016
PDF: 11 pages
Proc. SPIE 9803, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016, 980305 (20 April 2016); doi: 10.1117/12.2222136
Show Author Affiliations
Mostafa Mirshekari, Carnegie Mellon Univ. (United States)
Shijia Pan, Carnegie Mellon Univ. (United States)
Pei Zhang, Carnegie Mellon Univ. (United States)
Hae Young Noh, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 9803:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016
Jerome P. Lynch, Editor(s)

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