  High-accuracy positioning has gained significant interest for many use-cases
across various domains such as industrial internet of things (IIoT), healthcare
and entertainment. Radio frequency (RF) measurements are widely utilized for
user localization. However, challenging radio conditions such as
non-line-of-sight (NLOS) and multipath propagation can deteriorate the
positioning accuracy. Machine learning (ML)-based estimators have been proposed
to overcome these challenges. RF measurements can be utilized for positioning
in multiple ways resulting in time-based, angle-based and fingerprinting-based
methods. Different methods, however, impose different implementation
requirements to the system, and may perform differently in terms of accuracy
for a given setting. In this paper, we use artificial neural networks (ANNs) to
realize time-of-arrival (ToA)-based and channel impulse response (CIR)
fingerprinting-based positioning. We compare their performance for different
indoor environments based on real-world ultra-wideband (UWB) measurements. We
first show that using ML techniques helps to improve the estimation accuracy
compared to conventional techniques for time-based positioning. When comparing
time-based and fingerprinting schemes using ANNs, we show that the favorable
method in terms of positioning accuracy is different for different
environments, where the accuracy is affected not only by the radio propagation
conditions but also the density and distribution of reference user locations
used for fingerprinting.
