In-building Localization using Neural Networks

Location awareness is key capability of context-aware ubiquitous environments. Received signal strength (RSS) based localization is increasingly popular choice especially for indoor scenarios after pervasive adoption of IEEE 802.11 wireless LAN. Fundamental requirement of such localization systems is to estimate location from RSS at a particular location. Multi-path propagation effects make RSS to fluctuate in unpredictable manner, introducing uncertainty in location estimation. Moreover, in real life situations RSS values are not available at some locations all the time making the problem more difficult. We employ modular multi-layer perceptron (MMLP) approach to effectively reduce the uncertainty in location estimation system. It provides better location estimation results than other approaches and systematically caters for unavailable signals at estimation time

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