HABITS: a Bayesian filter approach to indoor tracking and location

Knowledge of the location of people and things has always been a valuable commodity. The explosion of new devices and techniques has brought people and item tracking out of the experimental 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. HABITS (History Aware Based Indoor Tracking System) 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. These probabilistic predictions may be used as an additional input into building automation systems for intelligent control of heating and lighting. It is twice as accurate as existing systems in dealing with signal black spots and it can predict the final destination of a person within the test environment almost 80% of the time.