Bluetooth-Based Room Localization Research Based on NB and SVM Approach

Abstract

It has been general recognized that the application of localization technology in home environment are beneficial to the development of health monitoring and mobile identification system development. As a kind of highly efficient sensor with obvious advantages such as low cost, the Bluetooth device has been widely used in our daily life. Research is carried out in an integrated environment based on mobile phone network signal measurement and Bluetooth link measurements in developing home localization systems. This paper presented a hybrid classification method, based on the combination of Bayesian network and supported vector machines, to support the development of Bluetooth-based room localization system. The proposed method mainly considers the dependency between features and non-linear overlapping of features between rooms. The results show that the prediction accuracy has been improved greatly in comparison to the traditional Naive Bayes classifier and the hidden Markov model used in previous studies.