Faculty Advisor or Committee Member
David Cyganski, Advisor
R. J. Duckworth, Arthur Heinricher
This thesis presents a 6 degree of freedom (DOF) position and orientation tracking solution suitable for pedestrian motion tracking based on 6DOF low cost MEMS inertial measurement units. This thesis was conducted as an extension of the ongoing efforts of the Precision Personnel Location (PPL) project at WPI. Prior to this work most of the PPL research focus has been on Radio Frequency (RF) location estimation. The newly developed inertial based system supports data fusion with the aforementioned RF system in a system currently under development. This work introduces a methodology for the implementation of a position estimation system based upon a Kalman filter structure, constructed from industry standard inertial sensor specifications and analytic noise models. This methodology is important because it allows for both rapid filter construction derived solely from specified values and flexible system definitions. In the course of the project, three different sensors were accommodated using the automatic design tools that were constructed. This thesis will present the mathematical basis of the new inertial tracking system followed by the stages of filter design and implementation, and finally the results of several trials with actual inertial data captures, using both public reference data and inertial captures from a foot mounted sensor that was developed as part of this work.
Worcester Polytechnic Institute
Electrical & Computer Engineering
All authors have granted to WPI a nonexclusive royalty-free license to distribute copies of the work. Copyright is held by the author or authors, with all rights reserved, unless otherwise noted. If you have any questions, please contact firstname.lastname@example.org.
Lowe, Matthew, "Inertial System Modeling and Kalman Filter Design from Sensor Specifications with Applications in Indoor Localization" (2011). Masters Theses (All Theses, All Years). 760.
Inertial Measurement Unit, Kalman Filter, Indoor Localization, System Modeling