  Recent localization frameworks exploit spatial information of complex channel
measurements (CMs) to estimate accurate positions even in multipath propagation
scenarios. State-of-the art CM fingerprinting(FP)-based methods employ
convolutional neural networks (CNN) to extract the spatial information.
However, they need spatially dense data sets (associated with high acquisition
and maintenance efforts) to work well -- which is rarely the case in practical
applications. If such data is not available (or its quality is low), we cannot
compensate the performance degradation of CNN-based FP as they do not provide
statistical position estimates, which prevents a fusion with other sources of
information on the observation level.
  We propose a novel localization framework that adapts well to sparse datasets
that only contain CMs of specific areas within the environment with strong
multipath propagation. Our framework compresses CMs into informative features
to unravel spatial information. It then regresses Gaussian processes (GPs) for
each of them, which imply statistical observation models based on
distance-dependent covariance kernels. Our framework combines the trained GPs
with line-of-sight ranges and a dynamics model in a particle filter. Our
measurements show that our approach outperforms state-of-the-art CNN
fingerprinting (0.52 m vs. 1.3 m MAE) on spatially sparse data collected in a
realistic industrial indoor environment.
