Adaptive Mobile Localization Method for Indoor Navigation

Navigation without GPS is a practical problem in indoor environment. The received signal strength indication known as RSSI can be used to compute the approximate distance and direction between an access point and a user mobile. Three or more multiple APs are normally cooperated to estimate the location of the mobile. However, the RSSI measurements are non-stationary and their mean and variance slowly changes over time and space. The RSSI propagation model derived from such measurements are not reliable. Hence, in this paper, an adaptive system using a neural, fuzzy inference system is introduced to effectively overcome the changes in indoor environment and improve the accuracy of mobile position estimation. The proposed system can reduce the erroneous mapping of the physical distances better than the conventional methods based on RSSI propagation modeling. The results of experiments show that the proposed system scales well in terms of the number of sensor nodes and provides an enhanced localization performance.