Thomas Blazek, Julian Karoliny, Fjolla Ademaj, Hans Peter Bernhard,
"RSSI-Based Location Classification Using a Particle Filter to Fuse Sensor Estimates"
: 2021 17th IEEE International Conference on Factory Communication Systems (WFCS), Serie IEEE Xplore, IEEE Computer Society, United States, Seite(n) 27-32, 2021, ISBN: 978-1-6654-2479-0
Original Titel:
RSSI-Based Location Classification Using a Particle Filter to Fuse Sensor Estimates
Sprache des Titels:
Englisch
Original Buchtitel:
2021 17th IEEE International Conference on Factory Communication Systems (WFCS)
Original Kurzfassung:
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.
Forschungs-