Precise Geolocation for Drones, Metaverse Users, and Beyond: Exploring Ranging Techniques Spanning 40 KHz to 400 GHz

This dissertation explores the realm of high-accuracy localization through the utilization of ranging-based techniques, encompassing a spectrum of signals ranging from low-frequency ultrasound acoustic signals to more intricate high-frequency signals like Wireless Fidelity (Wi-Fi) IEEE 802.11az, 5G New Radio (NR), and 6G. Moreover, another contribution is the conception of a novel timing mechanism and synchronization protocol grounded in tunable quantum photonic oscillators. In general, our primary focus is to facilitate precise indoor localization, where conventional GPS signals are notably absent. To showcase the significance of this innovation, we present two vital use cases at the forefront: drone localization and metaverse user positioning. In the context of indoor drone localization, the spectrum of applications ranges from recreational enthusiasts to critical missions requiring pinpoint accuracy. At the hobbyist level, drones can autonomously navigate intricate indoor courses, enriching the recreational experience. As a finer illustration of a hobbyist application, consider the case of ``follow me drones". These specialized drones are tailored for indoor photography and videography, demanding an exceptionally accurate autonomous flight capability. This precision is essential to ensure the drone can consistently track and capture its designated subject, even as it moves within the confined indoor environment. Moving on from hobby use cases, the technology extends its profound impact to more crucial scenarios, such as search and rescue operations within confined spaces. The ability of drones to localize with high precision enhances their autonomy, allowing them to maneuver seamlessly, even in environments where human intervention proves challenging. Furthermore, the technology holds the potential to revolutionize the metaverse. Within the metaverse, where augmented and virtual realities converge, the importance of high-accuracy localization is amplified. Immersive experiences like Augmented/Virtual/Mixed Reality (AR/VR/MR) gaming rely heavily on precise user positioning to create seamless interactions between digital and physical environments. In entertainment, this innovation sparks innovation in narrative design, enhancing user engagement by aligning virtual elements with real-world surroundings. Beyond entertainment, applications extend to areas like telemedicine, enabling remote medical procedures with virtual guidance that matches physical reality. In light of all these examples, the imperative for an advanced high-accuracy localization system has become increasingly pronounced. The core objective of this dissertation is to address this pressing need by engineering systems endowed with exceptional precision in localization. Among the array of potential techniques suitable for GPS-absent scenarios, we have elected to focus on ranging-based methods. Specifically, our methodologies are built upon the fundamental principles of time of arrival, time difference of arrival, and time of flight measurements. In essence, each of our devised systems harnesses the capabilities of beacons such as ultrasound acoustic sensors, 5G femtocells, or Wi-Fi access points, which function as the pivotal positioning nodes. Through the application of trilateration techniques, based on the calculated distances between these positioning nodes and the integrated sensors on the drone or metaverse user side, we facilitate robust three-dimensional localization. This strategic approach empowers us to realize our ambition of creating localization systems that not only compensate for the absence of GPS signals but also deliver unparalleled accuracy and reliability in complex and dynamic indoor environments. A significant challenge that we confronted during our research pertained to the disparity in z-axis localization performance compared to that of the x-y plane. This nuanced yet pivotal concern often remains overlooked in much of the prevailing state-of-the-art literature, which predominantly emphasizes two-dimensional localization methodologies. Given the demanding context of our work, where drones and metaverse users navigate dynamically across all three dimensions, the imperative for three-dimensional localization became evident. To address this, we embarked on a comprehensive analysis, encompassing mathematical derivations of error bounds for our proposed localization systems. Our investigations unveiled that localization errors trace their origins to two distinct sources: errors induced by ranging-based factors and errors stemming from geometric considerations. The former category is chiefly influenced by factors encompassing the quality of measurement devices, channel quality in which the signal communication between the sensor on the user and the positioning nodes takes place, environmental noise, multipath interference, and more. In contrast, the latter category, involving geometry-induced errors, arises primarily from the spatial configuration of the positioning nodes relative to the user. Throughout our journey, we dedicated efforts to mitigate both sources of error, ensuring the robustness of our system against diverse error origins. Our approach entails a two-fold strategy for each proposed localization system. Firstly, we introduce innovative techniques such as Frequency-Hopping Spread Spectrum (FHSS) and Frequency-Hopping Code Division Multiple Access (FH-CDMA) and incorporate devices such as Reconfigurable Intelligent Surfaces (RIS) and photonic oscillators to fortify the system against errors stemming from ranging-related factors. Secondly, we devised novel evolutionary-based optimization algorithms, adept at addressing the complex NP-Hard challenge of optimal positioning node placement. This strategic placement mitigates the impact of geometry-induced errors on localization accuracy across the entire environmental space. By meticulously addressing both these sources of error, our localization systems stand as a testament to comprehensive robustness and accuracy. Our methodologies not only extend the frontiers of three-dimensional localization but also equip the systems to navigate the intricacies of indoor environments with precision and reliability, effectively fulfilling the evolving demands of drone navigation and metaverse user interaction.