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This thesis explores the use of probabilistic models for enhancing positioning applications with inertial sensors and gyroscopes. The author, Manon Kok, addresses the challenge of integration drift in inertial sensors, proposing solutions through sensor fusion and calibration with additional sensors like magnetometers. The work includes a practical magnetometer calibration algorithm and discusses using magnetic disturbances as a positioning source in indoor environments. The thesis also covers estimating a human body's 6D pose using multiple inertial sensors combined with a biomechanical model. Additionally, it presents an algorithm for indoor positioning using ultra-wideband (UWB) systems in combination with inertial measurements, emphasizing the need for accurate receiver position and clock offset estimates. Intended for an audience with a background in electrical engineering and control systems, the thesis provides insights into advanced sensor fusion techniques for precise positioning.

Multisensor data fusion. --- Indoor positioning systems (Wireless localization) --- Multisensor data fusion

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