  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.
