A modular classification model for received signal strength based location systems

Estimating location of mobile devices based on received signal strength (RSS) patterns is an attractive method to realize indoor positioning systems. Accuracy of RSS based location estimation, particularly in large target sites, is effected by several environmental factors. Especially the temporal or permanent absence of radio signals introduces null values rendering sparsity and redundancy in feature space. We present a visibility matrix based modular classification model which systematically caters for unavailable signals. This model is practically realized using two eminent classification methods: (1) multi-layer perceptron and (2) LVQ. In order to confirm robustness and applicability of this model, we developed two location systems at different sites. Experimental results in real-world environments demonstrate that modular classification model consistently achieves superior location accuracy.