UC Irvine

In the age of Industry 4.0, the field of indoor localization has gained paramount importance, with applications spanning various domains. This master's thesis delves into the complex challenges inherent in indoor localization and proposes innovative solutions to overcome them. The research primarily focuses on improving the accuracy of the DecaWave UWB 1001c system for precisely positioning laboratory animals, specifically rats, in an indoor environment. The investigation explores hybrid approaches that combine multiple techniques to expand the boundaries of indoor localization capabilities.

The thesis encompasses a comprehensive review of the field, examining existing techniques and scrutinizing their effectiveness. Beyond theoretical discussions, this research offers a practical dimension by conducting real-world experiments to validate the proposed solutions.

Our practical work unfolds in two phases, each contributing to a deeper understanding of indoor localization challenges. The first phase involves the development of a sophisticated data collection system that integrates Ultra-wideband (UWB) and WiFi wireless protocols. This system serves as the foundation for a series of experiments designed to replicate Line-of-Sight (LOS) and Non-line-of-Sight (NLOS) conditions. These experiments provide invaluable insights into signal propagation complexities within indoor environments.

The second phase of our research entails a meticulous analysis of the extensive data gathered during the experimentation phase. Its core objective is to predict Received Signal Strength Indication (RSSI) for WiFi based on data obtained from UWB, revealing the intricate relationship between these communication protocols. A visual comparison of RSSI values obtained through both UWB and WiFi offers a compelling view of the convergence and divergence of signal characteristics. Furthermore, this phase culminates in the fusion of information from both protocols, resulting in a more robust and accurate indoor localization methodology.

To evaluate the effectiveness of the UWB and WiFi protocols, we have chosen cutting-edge devices. The DecaWave MDEK1001c kit, used alongside a highly precise and versatile Android application, serves as the foundation for our tests. The findings from this study show that by effectively integrating the data collected and applying sophisticated analytical techniques to the coordinates determined by the DecaWave system, we can greatly improve the accuracy of the DecaWave system. In certain situations, this improvement can lead to a notable doubling in precision, paving the way for new applications in indoor positioning systems.