Accurate Indoor localization

Abstract

Many people are now interested in indoor locator systems that use wireless access point signal strength indicators. Using a geometric technique, you can locate objects in a room using a standard spatial graph. However, when just a little quantity of suitable data is available, like when the trilateration technique is applied, the findings may be poor. If you're looking for pinpoint accuracy, fingerprint-based localization may be the way to go, but building a database of fingerprints takes time and effort. Therefore, this thesis presents a comprehensive investigation of RSS-based locating strategies. We present a trilateration localization approach that uses all of the effective anchor points and is based on recalculating (Gaussian-distribution random variable). In comparison 𝑋σ to trilateration and fingerprint methods, the trilateration with modified 𝑋σ approach enhances localization accuracy while reducing localization complexity.