Experimental Study of Smoothing Modifications of the MUSIC Algorithm for Direction of Arrival Estimation in Indoor Environments

Nowadays, the Direction of Arrival (DoA) estimation problem attracts much attention because of emerging use cases for wireless networks. DoA is beneficial for Reconfigurable Intelligent Surfaces, indoor localization, and various navigation and sensing applications, such as gesture recognition and home monitoring. The Multiple Signal Classification (MUSIC) algorithm is very promising for DoA estimation because it provides better accuracy than the other algorithms and remains simple enough to implement in hardware. MUSIC has many modifications designed to achieve better accuracy in indoor environments by combining and smoothing several measurements. However, such modifications have been implemented in equipment with different capabilities. Consequently, the modifications have never been compared under identical conditions. The paper addresses this issue, provides a classification of existing smoothing modifications of MUSIC, and proposes new ones not considered in the literature yet. All of them are compared in real Wi-Fi networks. For that, a testbed is designed that allows automatic measurements in multiple experiments with different positions of devices. A new calibration procedure is created to achieve higher accuracy, and the testbed is validated in an anechoic chamber. Finally, the paper suggests the preferable smoothing modifications of MUSIC for finding the DoA.