HABITS: a Bayesian filter approach to indoor tracking and location

Using Wi-Fi signals is an attractive and reasonably affordable option to deal with the currently unsolved problem of widespread tracking in an indoor environment. History aware-based indoor tracking system (HABITS) models human movement patterns by applying a discrete Bayesian filter to predict the areas that will, or will not, be visited in the future. We outline here the operation of the HABITS real-time location system (RTLS) and discuss the implementation in relation to indoor Wi-Fi tracking with a large wireless network. Testing of HABITS shows that it gives comparable levels of accuracy to those achieved by doubling the number of access points. We conclude that HABITS improves on standard real-time location systems in term of accuracy (overcoming blackspots), latency (giving position fixes when others cannot), cost (less APs are required than are recommended by standard RTLS systems) and prediction (short, medium and longer-term predictions are available from HABITS).

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