A Probabilistic Clustering-Based Indoor Location Determination System

Abstract

We present an indoor location determination system based on signal strength probability distributions for tackling the noisy wireless channel and clustering to reduce computation requirements. We provide two implementation techniques, namely, Joint Clustering and Incremental Triangulation and describe their tradeoffs in terms of location determination accuracy and computation requirement. Both techniques have been incorporated in two implemented context-aware systems: User Positioning System and the Rover System, both running on Compaq iPAQ Pocket PC's with Familiar distribution of Linux for PDA's. The results obtained show that both techniques give the user location with over 90% accuracy to within 7 feet with very low computation requirements, hence enabling a set of context-aware applications. Also UMIACS-TR-2002-30