A Bayesian filter approach to modelling human movement patterns for first responders within indoor locations

The arrival of new devices and techniques has brought tracking out of the investigation stage and into the wider world. 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. Here we present a system called HABITS (History Aware Based Indoor Tracking System) which aims at overcoming weaknesses in existing Real Time Location Systems (RTLS) by using approach of making educated guesses about future locations of humans. The primary research question that is foremost is whether the tracking capabilities of existing RTLS can be improved automatically by knowledge of previous movement especially in the short term in the case of emergency first responders by the application of a combination of artificial intelligence approaches, a key contributor being Bayesian filters. We conclude that HABITS improves on the standard Ekahau RTLS in term of accuracy (overcoming black spots), latency (giving position fixes when Ekahau cannot), cost (less APs are required than are recommended by Ekahau) and prediction (short term predictions are available from HABITS). These are features that no other indoor tracking system currently provides and could provide crucial in future emergency first responder incidents.