Gaussian Processes Online Observation Classification for RSSI-based Low-cost Indoor Positioning Systems

TL;DR

This paper introduces a real-time Gaussian Processes-based classification method to improve indoor positioning accuracy by filtering noisy RSSI measurements, enhancing robustness and adaptability of the system.

Contribution

It presents a novel online classification scheme using Gaussian Processes that does not depend on specific sensor models, improving robustness to noise and sensor failures in indoor positioning.

Findings

Enhanced positioning accuracy with the classifier integration

Robustness to sensor failures demonstrated in hardware experiments

Improved measurement filtering in noisy indoor environments

Abstract

In this paper, we propose a real-time classification scheme to cope with noisy Radio Signal Strength Indicator (RSSI) measurements utilized in indoor positioning systems. RSSI values are often converted to distances for position estimation. However due to multipathing and shadowing effects, finding a unique sensor model using both parametric and non-parametric methods is highly challenging. We learn decision regions using the Gaussian Processes classification to accept measurements that are consistent with the operating sensor model. The proposed approach can perform online, does not rely on a particular sensor model or parameters, and is robust to sensor failures. The experimental results achieved using hardware show that available positioning algorithms can benefit from incorporating the classifier into their measurement model as a meta-sensor modeling technique.