Experimental Demonstration of AoA Estimation Uncertainty for IoT Sensor Networks

In practice, the subspace-based algorithms such as Multiple Signal Classification (MUSIC) suffer from sensitivity to antenna-array response errors and therefore they require the assessment of the calibration gain and phase perturbations. This paper evaluates experimentally the accuracy of Angle-of-Arrival (AoA) estimation based on the MUSIC algorithm only coming from these perturbations in the context of Internet-of-Thing (IoT) applications. First of all, a new Over-the-Air (OTA) calibration method is proposed and gain and phase uncertainties are investigated. The impact of these uncertainties on the accuracy of AoA estimation is then studied and compared with the theoretical analysis. The experimental results show that the calibration errors coming from hardware imperfections can cause some degrees of uncertainty in AoA estimation.