A Multi-Sensor Passive Occupant Localization
| dc.contributor.author | Ambarkutuk, Murat | en |
| dc.contributor.committeechair | Plassmann, Paul E. | en |
| dc.contributor.committeechair | Jones, Creed Farris | en |
| dc.contributor.committeemember | Tian, Zhenhua | en |
| dc.contributor.committeemember | Boker, Almuatazbellah M. | en |
| dc.contributor.committeemember | Abbott, Amos L. | en |
| dc.contributor.committeemember | Alajlouni, Sa'ed Ahmad | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2024-11-26T09:00:11Z | en |
| dc.date.available | 2024-11-26T09:00:11Z | en |
| dc.date.issued | 2024-11-25 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | In our increasingly connected world, knowing the precise location of individuals within indoor spaces—such as homes, offices, and hospitals—has become essential for enhancing convenience, safety, and energy efficiency. Traditional methods for indoor localization often rely on cameras or radio signals, which can be expensive and raise privacy concerns. This dissertation introduces an innovative approach called vibro-localization, which determines the position of occupants by analyzing the subtle vibrations in the floor caused by their footsteps. Vibro-localization utilizes simple and affordable sensors called accelerometers, which are placed on the floor to detect vibrations. When a person walks, their footsteps generate unique vibration patterns that travel through the building structure. By capturing and analyzing these patterns, our system can accurately pinpoint the individual's location without the need for invasive cameras or constant radio signal transmissions. This method not only reduces costs but also preserves the privacy of occupants, as it does not capture visual or personal data. One of the main challenges in vibro-localization is ensuring accuracy despite various sources of error. Sensors can introduce noise and inaccuracies, and factors like the building's materials and layout can affect how vibrations propagate. To overcome these challenges, this research develops sophisticated models that account for sensor imperfections and environmental factors. By understanding and correcting for these variables, the system can deliver precise location data even in complex indoor environments. A significant advancement presented in this work is the development of an algorithm that intelligently selects the most reliable sensor data. This algorithm distinguishes between consistent measurements and those affected by external disturbances, such as echoes or structural inconsistencies, ensuring that only the highest quality data is used for localization. This not only improves accuracy but also makes the system more robust and reliable in real-world settings. Moreover, the dissertation explores the use of multiple sensors working together to enhance localization performance. By combining data from several accelerometers, the system can cross-verify measurements and reduce the impact of individual sensor errors. This multi-sensor approach leads to more stable and accurate location tracking, making the technology suitable for a wide range of applications. To validate the effectiveness of the proposed vibro-localization techniques, extensive experiments were conducted in controlled environments. The results demonstrated significant improvements in both accuracy and reliability compared to existing methods, showcasing the potential of vibro-localization as a practical solution for indoor positioning needs. The implications of this research are far-reaching. In smart homes, vibro-localization can enable automated lighting and climate control based on occupant presence, enhancing energy efficiency and comfort. In healthcare settings, it can assist in monitoring patient movements, ensuring safety and improving care. Additionally, in emergency situations, accurate indoor localization can facilitate quicker and more efficient evacuations. In summary, this dissertation presents a groundbreaking approach to indoor localization that is cost-effective, privacy-preserving, and highly accurate. By leveraging floor vibrations and advanced sensor data processing techniques, vibro-localization offers a viable alternative to traditional methods, with broad applications that can significantly enhance the functionality and safety of indoor environments. This research not only addresses current limitations in indoor localization technology but also paves the way for future innovations in smart building systems and occupant-aware technologies. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:41627 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/123653 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | vibrolocalization | en |
| dc.subject | dispersion | en |
| dc.subject | structural vibrations | en |
| dc.subject | vibroacoustics | en |
| dc.title | A Multi-Sensor Passive Occupant Localization | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Computer Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
Files
Original bundle
1 - 1 of 1