Learning Approach for Angle Estimation Based on Characteristics of Phase Drift

Abstract

Indoor positioning systems (IPSs) are increasingly vital for various applications on the Internet of Things (IoT), including home automation, navigation in large buildings, AR, and smart city development. These systems rely on techniques such as time of arrival (ToA), angle of arrival (AoA), and received signal strength indicator (RSSI), and Bluetooth low energy (BLE). Despite advancements, challenges such as signal fluctuations, multipath effects, and high infrastructure costs limit the accuracy and adoption of these systems. This paper proposes a deep neural network-based approach to enhance angle estimation by leveraging phase drift values, an underutilized aspect in current models. By employing the phase drift-dependent lightweight angle estimation (PLAE) model, we aim to improve angle prediction accuracy, particularly in complex indoor environments. Experimental results demonstrate that our model achieves higher accuracy compared to traditional methods. The integration of time series data handling capabilities in our approach highlights its potential to provide more reliable indoor positioning solutions. This research contributes to the development of specialized models for precise AoA estimation, addressing the gaps in existing methodologies.