Indoor cell-level localization based on RSSI classification

The task of estimating the location of a mobile transceiver using the Received Signal Strength Indication (RSSI) values of radio transmissions is an inference problem. Contextual information, i.e., if the target is in a specific region, is sufficient for most applications. Therefore, instead of estimating position coordinates, we take a slightly different approach and look at localization as a classification problem. We perform a comparison between the K-Nearest Neighbor (KNN), the Support Vector Machine (SVM) and the Simple Gaussian Classifier (SGC), three classifiers proposed previously under different contexts. Using experimental results, we demonstrate that the SGC achieves a competitive performance despite its simplicity. Furthermore, we consider the extension of the SGC to a Hidden Markov Model (HMM) and demonstrate the performance gains. The derivative of the HMM filter allows us to do online parameter tracking, realizing an adaptive scheme. To our knowledge, this adaptive scheme has not been used for the SGC before. Considering the advantages of the SGC, we advocate the SGC as a competitive solution for estimating contextual location information.