BBAuthors: AlHajri, M. I.; Advisor: -; Participants: Ali, N. T.; Shubair, R. M. (2019)
Evolving Internet-of-things applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by identifying the type of the surrounding indoor environment. This identification is of high importance since it leads to higher localization accuracy. This letter presents a novel method based on a cascaded two-stage machine learning approach for highly accurate and robust localization
in indoor environments using adaptive selection and combination of radio frequency (RF) features. In the proposed method, machine learning is first used to identify the type of the surrounding indoor environment. Then, in the second stage, ma... 
