  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.
