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For Cyper-Physical Production Systems (CPPS), localization is becomingincreasingly important as wireless and mobile devices are considered anintegral part. While localizing targets in a wireless communication systembased 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 inand 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 ahigh-quality position estimate. Then, we use Machine Learning (ML) to classifythe vehicle position. Our results show that the location output of the particlefilter is a better input to the classifiers than the raw RSSI data, and weachieve improved accuracy while simultaneously reducing the number of featuresthat the ML has to consider. We also compare the performance of multiple MLalgorithms and show that SVMs provide the overall best performance for thegiven task.

RSSI-Based Location Classification Using a Particle Filter to Fuse Sensor Estimates