Optimization of Sampling Cell Size for Fingerprint Positioning

Abstract

The fingerprint positioning based on received signal strength has a widespread development in many applications since the technology does not depend on additional hardware deployment. However, the site survey process which is executed before fingerprint positioning is time consuming and labor intensive. Sampling cell size directly affects the localization precision and the survey workload. We propose a sampling cell size optimization method to release the survey workload while holding the precision. We adopted the Gaussian process model to estimate the received signal strength based on some samples and used the inherent maximum Euclidean error of fingerprint vectors to compute the optimal cell size. We conduct some experiments to validate the efficiency of the mechanism. And the results show that our method can largely reduce the workload of the site survey process.