Indoor Positioning

Indoor positioning aims to accurately determine the location and orientation of objects or people within indoor environments, where GPS is unavailable. Current research emphasizes improving accuracy and efficiency through diverse approaches, including sensor fusion (e.g., inertial measurement units with acoustics or magnetic fields), machine learning (e.g., deep neural networks, Kalman filters, and generative adversarial networks), and novel signal processing techniques (e.g., Time Difference of Arrival). These advancements are crucial for numerous applications, ranging from warehouse automation and healthcare to augmented reality and smart building management, driving significant progress in both algorithm development and data management strategies.