  For Cyper-Physical Production Systems (CPPS), localization is becoming
increasingly important as wireless and mobile devices are considered an
integral part. While localizing targets in a wireless communication system
based on the Received Signal Strength Indicators (RSSIs) is a usual solution,
it is limited by sensor quality. We consider the scenario of a car moving in
and out of a chamber and propose to use a particle filter for sensor fusion,
allowing us to incorporate non-idealities in our model and achieve a
high-quality position estimate. Then, we use Machine Learning (ML) to classify
the vehicle position. Our results show that the location output of the particle
filter is a better input to the classifiers than the raw RSSI data, and we
achieve improved accuracy while simultaneously reducing the number of features
that the ML has to consider. We also compare the performance of multiple ML
algorithms and show that SVMs provide the overall best performance for the
given task.
