A Study on the Gradient Descent Based to Minimize Distance Loss in UWB Indoor Localization

In recent years, indoor localization has garnered significant attention due to its applications in various domains such as asset tracking, navigation, and context-aware services. Ultra-Wideband (UWB) technology, with its high accuracy and robustness, has emerged as a promising solution for indoor positioning. However, achieving optimal localization accuracy remains a challenge. Factors such as Time of Arrival (TOA) errors, line-of-sight (LOS) conditions, and computational complexity impact the performance of UWB-based localization systems. This study delves into the use of gradient descent techniques and deep learning to minimize distance loss in UWB indoor localization. Specifically, we investigate the impact of gradient descent optimization on distance estimation accuracy, explore the tradeoffs between learning rate, batch size, and hidden nodes in deep learning models, and compare the proposed approach with conventional UWB localization methods. Our findings demonstrate that deep learning-based optimization techniques can significantly improve the accuracy of UWB indoor localization.