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

TL;DR

This paper presents a particle filter-based sensor fusion approach to improve vehicle location classification using RSSI data, demonstrating enhanced accuracy and efficiency with machine learning classifiers, especially SVMs.

Contribution

It introduces a novel fusion method combining particle filters with machine learning for improved location classification from RSSI data in CPPS.

Findings

Particle filter fusion improves location estimates over raw RSSI data.

SVM classifiers outperform other ML algorithms in accuracy.

Reduced feature set leads to more efficient classification.

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.