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Self-Contained Pedestrian Tracking With Mems Sensors

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thesis
posted on 23.05.2021, 16:56 by Chengliang Huang
Due to the limitations of current indoor wireless positioning technologies, a novel positioning/tracking solution has to be explored and developed, in order to locate a person anywhere anytime without any infrastructure. The purpose of this thesis is to present the result of the first phase of a long-period research to find such a solution and develop a practical system. In this thesis, using inertial sensors for positioning of people is selected to replace wireless solutions, considering the development of micro-electromechanical systems. A sensing module consisting of accelerometers, rate gyroscopes and magnetometers used to monitor human kinetics. In order to make this proposal practical, a synergy of existing strapdown inertial navigation and pedestrian dead-reckoning is proposed to improve the accuracy of positioning. Furthermore, the cyclic alternation of stance phase and swing phase in human walking is used to reduce errors accumulating during projection and integration of sensed accelerometer signals. Other than the improvement of some existing methods to detect stance phase and reset the velocity, several new methods are proposed to remove the integral drift during both phases of a human stride. The algorithm to calculate heading of on the sensing module is also deduced to limit the integral drift of rate gyroscopes. All the methods and algorithms are applied in field experiments with carefully chosen sensing module mounted on human footwear. The results show promising accuracy of tracking, hence validate the feasibility of self-contained pedestrian tracking system with inertial sensors. Further work, especially with map correlation and particle filtering, will be done in the coming phases of the project to make the system applicable both outdoor and indoor.

History

Language

eng

Degree

Master of Applied Science

Program

Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type

Thesis