Fuzzy Clustering for Improved Positioning

Abstract

In this research, we focused on developing positioning system based on common short-range wireless. So we developed a system that assumes the existence of a number of fixed access points (5+) and employ arrival difference (TDOA) with Kalman filter. We also discuss multiple / tri-lateration and evaluate some of the root causes dilution of precision (DOP) positioning using Kalman Filter. This article presents a simpler approach fuzzy clustering (SFCA) to support the short-range positioning. We use fixed access points calibrated at 2.4 GHz. We use real time data observed in the application of our model offline. We have extended the model to mimic the signals communications (DSRC) 5.9 GHz dedicated short range, as defined in 1609.x. IEEE The results are compared in each case with respect to the metering plate need Differential Global Positioning System (DGPS) captured in the same test. We kept Line-of-Sight (LOS) in all our clear assessment and use of vehicles (<60 km / h) moving at low speed. Two different options for implementing the SFCA are presented, analysed and compared the two.