Learning Significant Locations and Predicting User Movement with GPS
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
Hybrid predictors for next location prediction
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
Review: A survey of active and passive indoor localisation systems
Computer Communications
Hi-index | 0.00 |
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).