Time of Arrival Error Estimation for Positioning Using Convolutional Neural Networks

TL;DR

This paper introduces CNN-based techniques to improve time-of-arrival error estimation in wireless positioning, significantly enhancing accuracy across various ToA methods using real-world CIR data.

Contribution

It presents a novel CNN approach for ToA error mitigation that outperforms existing methods and reduces reliance on traditional optimization.

Findings

Up to 37% improvement in ranging accuracy.

Effective error mitigation across multiple ToA estimators.

Real-world data validates the proposed CNN approach.

Abstract

Wireless high-accuracy positioning has recently attracted growing research interest due to diversified nature of applications such as industrial asset tracking, autonomous driving, process automation, and many more. However, obtaining a highly accurate location information is hampered by challenges due to the radio environment. A major source of error for time-based positioning methods is inaccurate time-of-arrival (ToA) or range estimation. Existing machine learning-based solutions to mitigate such errors rely on propagation environment classification hindered by a low number of classes, employ a set of features representing channel measurements only to a limited extent, or account for only device-specific proprietary methods of ToA estimation. In this paper, we propose convolutional neural networks (CNNs) to estimate and mitigate the errors of a variety of ToA estimation methods utilizing channel impulse responses (CIRs). Based on real-world measurements from two independent campaigns, the proposed method yields significant improvements in ranging accuracy (up to 37%) of the state-of-the-art ToA estimators, often eliminating the need of optimizing the underlying conventional methods.