Gaussian Processes Online Observation Classification for RSSI-based Low-cost Indoor Positioning Systems
This work addresses the challenge of sensor noise for low-cost indoor positioning systems, offering a robust, model-agnostic solution that is incremental in nature.
The paper tackles the problem of noisy RSSI measurements in indoor positioning by proposing a real-time classification scheme using Gaussian Processes to filter consistent measurements, enabling existing positioning algorithms to benefit from improved accuracy without specifying a particular sensor model.
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.