A Learning-Based AoA Estimation Method for Device-Free Localization

Abstract

Device-free localization (DFL), an important aspect in integrated sensing and communication, can be achieved through exploiting multipath components in ultra-wide bandwidth systems. However, incorrect identification of multipath components in the channel impulse responses will lead to large angle-of-arrival (AoA) estimation errors and subsequently poor localization performance. This letter proposes a learning-based AoA estimation method to improve the DFL accuracy. In the proposed method, we first design a classifier to identify the multipath components and then exploit the phase-difference-of-arrival to mitigate the AoA estimation error through a multilayer perceptron. Our learning-based method is validated using the datasets collected by ultra-wide bandwidth arrays, which significantly outperforms conventional methods in terms of AoA estimation and localization performance.