A Multi-Sensor Passive Occupant Localization

Indoor localization has emerged as a critical technology for enhancing the functionality and efficiency of smart environments. This dissertation focuses on vibro-localization, a novel IOL methodology that determines occupant positions by analyzing floor vibrations caused by footfall patterns. Unlike traditional localization techniques that rely on visual or radio-based sensing, vibro-localization leverages accelerometers fixed to the floor to capture vibro-measurements, offering a cost-effective and privacy-preserving alternative. The primary objective of this research is to address significant limitations in existing vibro-localization approaches, including sensor imperfections, measurement uncertainty, and complex wave dynamics. To this end, we develop comprehensive models that characterize both random and systematic errors introduced by accelerometers, integrating these models into the localization framework to enhance accuracy. Furthermore, we quantify the uncertainty in vibro-measurements and elucidate their contribution to localization errors, providing a robust foundation for error mitigation strategies. A key contribution of this work is the introduction of an information-theoretic Byzantine Sensor Elimination (BSE) algorithm. This algorithm assesses the reliability of vibro-measurement vectors by categorizing sensors into consistent and divergent subsets, thereby minimizing the impact of external uncertainties such as reflections and dispersion. Additionally, we propose multi-sensor vibro-localization techniques that aggregate data from multiple accelerometers, enhancing robustness against individual sensor inaccuracies and environmental variabilities. To accurately model wave propagation, this dissertation advances parametric models that account for dispersion, attenuation, and material inhomogeneities in the floor structure. These models facilitate precise occupant localization even with low-spectral resolution in transfer function estimates. Empirical validation using controlled experimental data demonstrates significant improvements in localization accuracy and precision over baseline methods, highlighting the efficacy of the proposed techniques. The outcomes of this research contribute to the development of economically feasible and ethically sound IOL technologies, broadening their applicability across various domains such as smart homes, healthcare, and energy management. By addressing critical challenges in sensor reliability and wave dynamics, this dissertation paves the way for more accurate, reliable, and scalable indoor localization systems.