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

Research output: Conference proceeding/Chapter in Book/Report/Conference Paperpeer-review

Abstract

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.
Original languageEnglish
Title of host publication2021 17th IEEE International Conference on Factory Communication Systems (WFCS)
PublisherIEEE Computer Society
Pages27-32
Number of pages6
ISBN (Print)978-1-6654-2479-0
DOIs
Publication statusPublished - 11 Jun 2021
Externally publishedYes
Event2021 17th IEEE International Conference on Factory Communication Systems (WFCS) - Linz, Austria
Duration: 9 Jun 202111 Jun 2021

Publication series

Name2021 17th IEEE International Conference on Factory Communication Systems (WFCS)

Conference

Conference2021 17th IEEE International Conference on Factory Communication Systems (WFCS)
Period9/06/2111/06/21

Keywords

  • Wireless communication
  • Support vector machines
  • Wireless sensor networks
  • Production systems
  • Machine learning
  • Sensor fusion
  • Particle filters

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