Localization Convolutional Neural Networks Using Angle of Arrival Images

Abstract

We introduce localization convolutional neural networks (CNNs), a data-driven time series-based angle of arrival (AOA) localization scheme capable of coping with noise and errors in AOA estimates measured at receiver nodes. Our localization CNNs enhance their robustness by using a time series of AOA measurements rather than a single-time instance measurement to localize mobile nodes. We analyze real-world noise models, and use them to generate synthetic training data that increase the CNN's tolerance to noise. This synthetic data generation method replaces the need for expensive data collection campaigns to capture noise conditions in the field. The proposed scheme is both simple to use and also lightweight, as the mobile node to be localized solely transmits a beacon signal and requires no further processing capabilities. Our scheme is novel in its use of: (1) CNNs operating on space-time AOA images composed of AOA data from multiple receiver nodes over time, and (2) synthetically-generated perturbed training examples obtained via modeling triangulation patterns from noisy AOA measurements. We demonstrate that a relatively small CNN can achieve state-of-the-art localization accuracy that meets the 5G standard requirements even under high degrees of AOA noise. We motivate the use of our proposed localization CNNs with a tracking application for mobile nodes, and argue that our solution is advantageous due to its high localization accuracy and computational efficiency.