Abstract
Various methods employing RF signals, such as Wi-Fi, BLE, and UWB, have been proposed for estimating the locations of people and objects. However, one of the major challenges impeding their widespread adoption is the time and effort required to manage these systems, especially in terms of recharging and replacing batteries. To address this issue, our study focuses on localization using backscatter tags, which boast ultra-low power consumption. These tags operate by backscattering surrounding radio signals, thereby creating a frequency shift in the backscattered signal. As a preliminary investigation into localization, we utilized the MUSIC algorithm, commonly employed for Angle-of-Arrival (AoA) estimation. We adapted this algorithm for use with our backscatter tags and assessed its AoA estimation performance. Our experimental results indicate that AoA estimation is achievable with an error of 10.8° in a 5mZ_$\times$_Z 9m conference room.