Intelligent radio map management for future WLAN indoor location fingerprinting

The fingerprint-based techniques are commonly used for indoor location estimation, in which a radio map is built by calibrating signal-strength values at several training locations in the offline phase. However, the signal-strength values change as the environment changes, and hence the radio map built may be out of date. Further, the recalibration of signal-strength values for each environment change is laborious and time consuming. Therefore, in this paper, an efficient radio map management method is proposed in response to the environment changes. We first demonstrate that different features of signal propagation are obtained in different regions of the indoor environment. According to the different features of signal propagation, the environment is divided into several regions and the signal-strength values collected at the training locations in different regions can be updated accurately and individually using proper propagation features. The experimental results show the usefulness of the proposed method and the accuracy of the localization can be improved.